CN118663538B

CN118663538B - Impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking

Info

Publication number: CN118663538B
Application number: CN202411163144.9A
Authority: CN
Inventors: 冯晓祥; 黄风平; 潘娟娟; 苏背背
Original assignee: Hangzhou Honghu Electronic Technology Co ltd
Current assignee: Hangzhou Honghu Electronic Technology Co ltd
Filing date: 2024-08-23
Publication date: 2024-11-19
Anticipated expiration: 2044-08-23

Abstract

The application discloses an impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking, which relates to the technical field of information and comprises the following steps: sampling the voltage and the current of the ultrasonic piezoelectric transducer; amplifying and filtering the voltage sampling signal and the current sampling signal; converting the amplified and filtered voltage sampling signal and current sampling signal into a voltage square wave signal and a current square wave signal through a zero comparison circuit; acquiring a phase difference pulse signal of the voltage square wave signal and the current square wave signal by using a phase comparison circuit; calculating the phase difference digital quantity of the obtained phase difference pulse signals; according to the obtained phase difference digital quantity, controlling the output of the excitation frequency of the ultrasonic signal source through a frequency adjusting algorithm, and adjusting the excitation frequency to be consistent with the resonance frequency of the piezoelectric transducer; aiming at the problem that the resonant frequency of the transducer in the prior art drifts along with the temperature change, the impedance matching precision is low, and the application improves the impedance matching precision.

Description

Impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking

Technical Field

The application relates to the technical field of information, in particular to an impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking.

Background

Ultrasonic technology has been widely used in the fields of industry, medical treatment, military, etc. by virtue of its unique advantages. As a core component of an ultrasonic system, the performance of a piezoelectric transducer directly affects the operation of an ultrasonic device. Impedance matching of the transducer is critical to achieve efficient transmission and conversion of ultrasonic energy. Ideally, the best energy transfer effect is achieved when the resonant frequency of the transducer matches the excitation frequency of the sonotrode.

However, in practical applications, the resonant frequency of the transducer is not constant, but may drift with changes in operating environment temperature, load conditions, and the like. When the resonant frequency of the transducer is not matched with the excitation frequency, energy reflection and loss are increased, impedance matching precision is reduced, and the working performance and stability of the ultrasonic equipment are seriously affected.

Currently, there are a number of solutions to the transducer impedance matching problem. Among them, it is common to employ an inductance-capacitance (LC) matching circuit to compensate for the shift in the resonant frequency of the transducer by adjusting the value of the inductance or capacitance. However, since the LC element itself is easily affected by factors such as temperature and vibration, it is difficult to adapt to the dynamically changing operating condition requirements. Another idea is to integrate impedance transformation devices, such as transformers, autotransformers, etc., at the transducer end or at the generator end, to achieve impedance matching by changing the transformation ratio. However, this method increases the volume and cost of the system, and the accuracy of matching and the range of application are limited.

In addition, there have been some studies on the realization of adaptive impedance matching using intelligent algorithms, such as genetic algorithms, particle swarm algorithms, and the like. These algorithms improve the impedance matching effect to some extent by optimizing the matching circuit parameters or control strategies. However, the convergence speed and stability of the algorithm are still to be improved, and the real-time performance and robustness are difficult to meet the requirement of dynamic frequency tracking.

Disclosure of Invention

Aiming at the problem that the resonant frequency of the transducer can drift along with temperature change and the impedance matching precision is low in the prior art, the application provides the impedance matching method based on the ultrasonic piezoelectric transducer frequency dynamic tracking.

Technical solution the object of the present application is achieved by the following technical solution.

The specification provides an impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking, which comprises the following steps: sampling the voltage and the current of the ultrasonic piezoelectric transducer; the current sampling is carried out by connecting a sampling resistor in series in a piezoelectric transducer load loop, and converting a current signal into a voltage signal for sampling; amplifying and filtering the obtained voltage sampling signal and current sampling signal; the amplifying treatment adopts an in-phase amplifying circuit, and the filtering treatment adopts an active band-pass filtering circuit; converting the amplified and filtered voltage sampling signal and current sampling signal into a voltage square wave signal and a current square wave signal through a zero comparison circuit; the zero comparison circuit comprises a voltage follower and a zero-crossing comparator; the voltage follower is used for isolating a later-stage circuit, so that mutual interference is avoided; the zero-crossing comparator adopts an LM311N comparator to convert the sine wave into a square wave; acquiring a phase difference pulse signal of the voltage square wave signal and the current square wave signal by using a phase comparison circuit; the voltage square wave signal is connected to the same-phase end of the phase comparison circuit; the current square wave signal is connected to the inverting terminal of the phase comparison circuit; the calculated phase difference digital quantity of the phase difference pulse signals is used as a feedback quantity of frequency adjustment; according to the obtained phase difference digital quantity, controlling the output of the excitation frequency of the ultrasonic signal source through a frequency adjusting algorithm, and adjusting the excitation frequency to be consistent with the resonance frequency of the piezoelectric transducer; the steps are repeated to enable the excitation frequency and the resonance frequency of the piezoelectric transducer to synchronously change, and impedance dynamic matching is carried out.

Wherein, in-phase amplifying circuit: an amplifying circuit in which the phase of an output signal with respect to an input signal is unchanged. The method is used for amplifying voltage or current signals and improving the amplitude of the signals so as to facilitate subsequent processing. Common in-phase amplification circuits include non-inverting amplifiers, differential amplifiers, and the like. Active bandpass filter circuit: a bandpass filter circuit implemented using active devices such as operational amplifiers. It allows signals within a particular frequency range to pass through while attenuating or filtering out signals outside that range. Is commonly used for extracting specific frequency components and inhibiting noise interference. The zero comparison circuit is a circuit that compares an input signal with a zero level and outputs a high level or a low level. The method can realize the functions of converting sine waves into square waves, detecting zero crossings of signals and the like. The zero comparison circuit is typically composed of a comparator and an auxiliary circuit. LM311N comparator: a high-speed voltage comparator chip can compare two paths of analog voltage signals and output digital level signals. The method has the characteristics of high speed, small delay, high sensitivity and the like, and is suitable for occasions such as signal processing, pulse generation and the like. A voltage follower: an operational amplifier circuit with gain of 1, the output voltage always follows the input voltage. The high-impedance high-output-impedance high-voltage power amplifier has the characteristics of high input impedance and low output impedance, and is commonly used for signal buffering, impedance matching, isolation and other purposes. Excitation frequency: the frequency of the alternating electrical signal applied to the piezoelectric transducer. By adjusting the excitation frequency, the vibration state of the piezoelectric transducer can be controlled, and efficient transmission and conversion of energy are realized. Resonant frequency: the vibration frequency of the piezoelectric transducer in the resonance state. At the resonant frequency, the impedance of the piezoelectric transducer is minimal and the vibration amplitude is maximal, so that the electric energy can be most effectively converted into mechanical energy. The resonant frequency depends on the material properties and the structural dimensions of the piezoelectric transducer.

Further, the center frequency of the active band-pass filter circuit is set to the operating frequency of the ultrasonic transducer. In the filter circuit, the center frequency refers to a frequency point with minimum amplitude attenuation (or maximum gain) on a frequency response curve of the filter. For a bandpass filter, the center frequency is located in the center of the passband, and the cut-off frequencies on both sides are equidistant from the center frequency. The center frequency determines the frequency selective characteristics of the filter, i.e. which frequency range of signals is passed through. In an active bandpass filter circuit, the center frequency can be set by adjusting parameters of active devices (such as operational amplifiers) and resistance-capacitance values of peripheral circuits. Common ways of adjustment include changing the resistance value, capacitance value, or using variable resistors, capacitors, etc. By adjusting the center frequency, the passband of the filter can be matched with the required signal frequency range, and interference of other frequency components can be filtered. The working frequency refers to the vibration frequency of the ultrasonic transducer in the actual working state. An ultrasonic transducer is a device that converts electrical energy into mechanical vibration energy using a piezoelectric effect or a magnetostrictive effect. When an alternating electric field of a certain frequency is applied to the transducer, the transducer generates mechanical vibrations of a corresponding frequency and radiates ultrasound waves to the surrounding medium.

Further, amplifying the obtained voltage sampling signal and current sampling signal includes: the amplifying process adopts an in-phase amplifying circuit which is composed of an OPA846 operational amplifier; the non-inverting input end of the OPA846 operational amplifier is connected with a voltage sampling signal or a current sampling signal, and the inverting input end of the OPA846 operational amplifier is connected with a feedback circuit formed by a resistor and a potentiometer; the amplification factors are changed by adjusting the potentiometer, so that the amplification factors of the voltage sampling signal and the current sampling signal are the same, and the phase relation between the voltage sampling signal and the current sampling signal is kept; the output of the OPA846 op-amp outputs amplified voltage and current sample signals.

OPA846 is a high-speed, high-precision operational amplifier manufactured by texas instruments (Texas Instruments) corporation, among others. The integrated circuit chip is integrated with high-performance analog circuits, and can amplify, filter, buffer and the like input voltage signals. In the in-phase amplifying circuit, the in-phase input end of the OPA846 operational amplifier is connected with a signal to be amplified (such as a voltage sampling signal or a current sampling signal), and the opposite-phase input end is connected with the output end of the operational amplifier through a feedback circuit (composed of a resistor and a potentiometer). The size of the feedback resistor can be changed by adjusting the potentiometer, so that the gain of the amplifying circuit is adjusted.

Further, the phase comparison circuit is used for obtaining a phase difference pulse signal of the voltage square wave signal and the current square wave signal, and the phase difference pulse signal comprises: the phase comparison circuit adopts an LM311N comparator; the voltage square wave signal is connected to the non-inverting input end of the LM311N comparator, and the current square wave signal is connected to the inverting input end of the LM311N comparator; the output end of the LM311N comparator is connected with a pull-up resistor, and when the voltage square wave signal leads the current square wave signal, the LM311N comparator outputs a high-level pulse to indicate that the load of the piezoelectric transducer is inductive; otherwise, the LM311N comparator outputs a low-level pulse, which indicates that the load of the piezoelectric transducer is capacitive; the high level pulse and the low level pulse output by the LM311N comparator form a phase difference pulse signal.

In an ac circuit, if the phase of the current lags behind the phase of the voltage, the circuit is said to have inductive characteristics. Inductive elements, such as inductors, whose impedance increases with increasing frequency, cause a phase difference in the circuit where the current lags the voltage. Inductive loads may cause a delay in the current waveform relative to the voltage waveform. In an ac circuit, if the phase of the current leads the phase of the voltage, the circuit is said to be capacitive. Capacitive elements, such as capacitors, whose impedance decreases with increasing frequency, cause a phase difference in the circuit in which the current leads the voltage. The capacitive load advances the current waveform somewhat relative to the voltage waveform. In an ac circuit, if one signal is phase advanced relative to the other signal, the signal is said to be advanced relative to the other signal. The magnitude of the advance may be expressed in terms of phase angle, typically in degrees or radians. When the piezoelectric transducer load is capacitive, the current signal may lead the voltage signal. In a digital circuit, a high level pulse refers to a pulse signal whose voltage amplitude reaches a high level threshold of a logic circuit. The high level is typically close to the supply voltage, representing a logic "1" state. In the LM311N comparator, the output generates a high pulse when the voltage at the non-inverting input is higher than the voltage at the inverting input. In a digital circuit, a low-level pulse refers to a pulse signal whose voltage amplitude is close to the ground potential. The low level is typically near 0V, representing a logic "0" state. In the LM311N comparator, the output generates a low pulse when the voltage at the non-inverting input is lower than the voltage at the inverting input.

Further, the calculated phase difference digital quantity of the phase difference pulse signal includes: connecting the phase difference pulse signal output by the phase comparison circuit to an external interrupt pin of the singlechip; setting an external interrupt triggering mode of the singlechip as falling edge triggering, and triggering an interrupt when the phase difference pulse signal jumps from a high level to a low level; in the interruption process, counting the high-level duration time of the phase difference pulse signals by using a timer of the singlechip; the counting frequency of the timer is consistent with the clock frequency of the singlechip; obtaining a phase difference pulse width count value by setting a prescaler coefficient and a count initial value of a timer; at the moment when the phase difference pulse signal jumps from high level to low level, the timer stops counting, and the value of the timer counting register is read to obtain a phase difference pulse width counting value; the phase difference pulse width count value is input as an analog signal of the A/D converter; the analog signals are sampled, held and quantized and encoded by a successive approximation type A/D converter arranged in the singlechip, and the phase difference pulse width count value is converted into a corresponding phase difference digital quantity.

The singlechip is specially used for receiving pins of external interrupt signals. When the external interrupt pin detects a specific level jump (such as a rising edge or a falling edge), the singlechip is triggered to execute a corresponding interrupt service routine. By external interruption, the real-time communication and synchronization between the singlechip and external equipment can be realized. And the singlechip is used for: a microcomputer system integrating CPU, RAM, ROM, timer, I/O interface and other functional modules. The single chip microcomputer has small volume, low cost and low power consumption, and is widely applied to the fields of embedded systems and industrial control. Through programming, various complex control and data processing tasks can be realized by the singlechip. The instant the signal jumps from high to low. In digital circuits, the falling edge is typically used as a trigger signal or a synchronous clock. When the external interrupt pin is set to be triggered by a falling edge, the single chip can trigger an interrupt service routine at the moment of detecting that an input signal jumps from a high level to a low level. In the timer of the singlechip, the prescaler coefficient is used for reducing the counting frequency of the timer. The actual counting frequency of the timer can be obtained by dividing the clock frequency of the singlechip by the prescaled coefficient. The counting period of the timer can be changed by adjusting the prescaler coefficient, so that the timer is suitable for different timing requirements. In the phase difference pulse signal outputted from the phase comparison circuit, the high level is continued for a time. The pulse width of the phase difference is proportional to the phase difference between the voltage signal and the current signal, and quantitative information of the phase difference can be obtained by measuring the pulse width. The singlechip counts the phase difference pulse width through a timer to obtain a phase difference pulse width count value. An analog-to-digital converter, a circuit for converting an analog signal to a digital signal. The a/D converter obtains a digital quantity corresponding to the amplitude of the analog signal by sampling, quantizing, and encoding the analog signal. The successive approximation type A/D converter arranged in the singlechip can convert the phase difference pulse width count value into a phase difference digital quantity, and is convenient for subsequent digital signal processing. The sample-hold circuit performs instantaneous sampling on the input analog signal, and holds the sampled value after the sampling is finished, thereby providing a stable input signal for A/D conversion. The performance of the sample-and-hold circuit, such as sample rate, hold time, etc., determines the accuracy and speed of the a/D conversion. The successive approximation a/D converter determines the digital output code bit by comparing with a series of reference voltages. The number of bits (e.g., 8 bits, 12 bits, etc.) of the quantized code determines the resolution of the a/D conversion. The phase difference pulse width count value is converted into a corresponding phase difference digital quantity by quantization coding.

Further, according to the obtained phase difference digital quantity, the output of the excitation frequency of the ultrasonic signal source is controlled by a frequency adjustment algorithm, and the adjustment of the excitation frequency to be consistent with the resonance frequency of the piezoelectric transducer comprises the following steps: a fuzzy PID control algorithm is adopted as a frequency adjustment algorithm; according to the magnitude, the change rate and the change acceleration of the phase difference digital quantity, the proportional coefficient, the integral coefficient and the differential coefficient of a PID control algorithm are adjusted; amplifying the proportional coefficient according to a preset first multiple when the absolute value of the phase difference digital quantity is larger than a preset first threshold value; otherwise, the scaling factor is reduced according to a preset second multiple; amplifying the differential coefficient according to a preset third multiple when the change rate of the phase difference digital quantity is larger than a preset second threshold value; otherwise, reducing the differential coefficient according to a preset fourth multiple; when the variation acceleration of the phase difference digital quantity is larger than a preset third threshold value, amplifying an integral coefficient according to a preset fifth multiple; otherwise, reducing the integral coefficient according to a preset sixth multiple; and carrying out fuzzy processing on the phase difference digital quantity, the change rate of the phase difference digital quantity and the change acceleration of the phase difference digital quantity by adopting a segmented trapezoid membership function to obtain corresponding membership degree.

Wherein, setting a first threshold: the first threshold is used for judging whether the absolute value of the phase difference digital quantity is large or not, so as to determine whether to enlarge or reduce the scale factor. The first threshold is set taking into account the quality factor of the piezoelectric transducer, the resonant frequency range, and the required frequency tracking accuracy. The first threshold is preferably set to 10% of the full scale range of the phase difference digital quantity. The second threshold is used for judging whether the change rate of the phase difference digital quantity is large or not, so as to determine whether to enlarge or reduce the differential coefficient. The second threshold is set taking into account the rate of change of the resonant frequency of the piezoelectric transducer and the response speed of the system. The second threshold is preferably set to 1% of the full scale range of the phase difference digital quantity. Setting a third threshold: the third threshold value is used for judging whether the variation acceleration of the phase difference digital quantity is large or not, so as to determine whether to enlarge or reduce the integral coefficient. The third threshold is set taking into account the resonant frequency varying acceleration of the piezoelectric transducer and the stability of the system. The third threshold is preferably set to 0.1% of the full scale range of the phase difference digital quantity.

Further, the method further comprises the following steps: performing fuzzy reasoning according to preset fuzzy reasoning rules, namely membership of the phase difference digital quantity, membership of the change rate of the phase difference digital quantity and membership of the change acceleration of the phase difference digital quantity, and obtaining membership of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm; performing defuzzification treatment on membership degrees of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm by adopting a gravity center method to obtain the proportional coefficient, the integral coefficient and the differential coefficient after self-adaptive adjustment; substituting the self-adaptive adjusted proportional coefficient, integral coefficient and differential coefficient into a PID control algorithm, taking the phase difference digital quantity as input, and calculating to obtain a frequency adjustment value; and converting the frequency adjustment value into a frequency control word of the DDS direct digital frequency synthesizer, controlling the DDS direct digital frequency synthesizer to output an excitation signal with a frequency corresponding to the frequency control word, and dynamically adjusting the excitation frequency of the piezoelectric transducer.

In the fuzzy control, a language rule describing a relationship between an input variable and an output variable is used. The fuzzy inference rule is generally in the form of "IF-THEN", for example, "IF phase difference digital quantity is positive AND change rate of phase difference digital quantity is positive THEN scaling factor is large". By setting a series of fuzzy inference rules, the membership degree of the output variable can be deduced according to the membership degree of the input variable, so as to realize fuzzy control. In the fuzzy set theory, the degree of attribution of an element to a fuzzy set is represented. The membership range is typically [0,1],0 representing no complete set, 1 representing complete set, and a value between 0 and 1 representing partial set. In fuzzy control, an input variable is mapped into membership degrees of corresponding fuzzy sets through membership functions, and the membership degrees are used as the basis of fuzzy reasoning. And converting the membership degree of the input variable into the membership degree of the output variable according to the fuzzy inference rule. Common fuzzy reasoning methods are Mamdani reasoning and Sugeno reasoning. In the fuzzy PID control algorithm, the membership of the proportional coefficient, the integral coefficient and the differential coefficient of the PID control algorithm is obtained according to the phase difference digital quantity, the change rate of the phase difference digital quantity and the membership of the change acceleration through fuzzy reasoning. A control algorithm combines fuzzy control with PID control.

And the proportional coefficient, the integral coefficient and the differential coefficient of the PID control algorithm are adaptively adjusted according to the input and output states of the system through fuzzy reasoning, so that better control performance is realized. The fuzzy PID control algorithm has strong robustness and adaptability, and can effectively process nonlinear, time-varying and uncertainty systems. A commonly used fuzzy control defuzzification method. The amount of blur is converted into an accurate amount by calculating the barycentric coordinates of the blur set. In the fuzzy PID control algorithm, the membership of the proportional coefficient, the integral coefficient and the differential coefficient of the PID control algorithm is subjected to defuzzification processing by adopting a gravity center method, so that the control parameters after self-adaption adjustment are obtained. DDS direct digital frequency synthesizer: a device for generating a sine wave signal with controllable frequency and phase by digital technology. The DDS converts the digital frequency control word into an analog sine wave signal to be output through an accumulator, a phase mapping device, a waveform memory, a D/A converter and other components. By varying the frequency control word, an accurate adjustment of the frequency can be achieved. The DDS has the advantages of high frequency resolution, high conversion speed, continuous phase and the like, and is widely applied to the fields of communication, radars, instruments, meters and the like. In a DDS direct digital frequency synthesizer, a digital quantity is used to control the frequency of the output signal. The number of bits of the frequency control word determines the frequency resolution of the DDS, the higher the number of bits, the smaller the adjustable frequency step.

Further, the fuzzy processing is carried out on the phase difference digital quantity, the change rate of the phase difference digital quantity and the change acceleration of the phase difference digital quantity by adopting a segmented trapezoid membership function to obtain corresponding membership degree, and the fuzzy processing comprises the following steps: determining a phase difference digital quantity, a change rate of the phase difference digital quantity and a discourse domain range of change acceleration of the phase difference digital quantity; dividing the domain range into a plurality of fuzzy subsets; for each fuzzy subset, representing a corresponding membership function by adopting a segmented trapezoidal membership function, wherein the segmented trapezoidal membership function comprises four parameters which are respectively a left bottom boundary, a left upper boundary, a right upper boundary and a right bottom boundary of a trapezoid; determining a corresponding fuzzy subset according to the magnitude of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating to obtain the membership degree of the phase difference digital quantity; determining a corresponding fuzzy subset according to the change rate of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating to obtain the membership degree of the change rate of the phase difference digital quantity; and determining a corresponding fuzzy subset according to the magnitude of the variable acceleration of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating the membership degree of the variable acceleration of the phase difference digital quantity.

In the fuzzy set theory, a fuzzy set can be divided into a plurality of fuzzy subsets, and each fuzzy subset represents a value range of a linguistic variable. For example, the argument of the phase difference digital quantity is divided into fuzzy subsets of "negative large", "negative medium", "negative small", "zero", "positive small", "medium", "positive large", etc. Each fuzzy subset corresponds to a membership function representing the degree of membership of the input variable to the subset. Through the division of fuzzy subsets, continuous input variables can be converted into discrete language descriptions, so that fuzzy reasoning is facilitated. In the segmented trapezoidal membership function, the boundary value of the left side bottom edge of the trapezoid. For a fuzzy subset, the bottom left boundary indicates that the input variable does not belong to the lower limit of the subset at all. When the input variable is smaller than the bottom left boundary, its membership is 0. The setting of the bottom left boundary determines the starting position of the blurred subset and the degree of blurring. In the segmented trapezoidal membership function, the boundary value on the left side of the trapezoid. For a fuzzy subset, the upper left boundary indicates that the input variable starts to fall entirely within the lower limit of the subset. When the input variable is greater than or equal to the upper left boundary, the membership degree is 1. The setting of the upper left boundary determines the starting position of the core area of the fuzzy subset. In the segmented trapezoidal membership function, the boundary value on the right side of the trapezoid. For a fuzzy subset, the upper right boundary indicates that the input variable is no longer entirely within the upper limit of the subset. When the input variable is less than or equal to the upper right boundary, the membership degree is 1. The setting of the upper right boundary determines the end position of the core region of the fuzzy subset. In the segmented trapezoidal membership function, the boundary value of the right bottom edge of the trapezoid. For a fuzzy subset, the bottom right boundary indicates that the input variable does not belong to the upper limit of the subset at all. When the input variable is greater than the bottom right boundary, its membership is 0. The setting of the bottom right boundary determines the end position of the fuzzy subset and the degree of blurring.

Further, the piecewise trapezoidal membership function expression is as follows:

wherein, Representing a membership function, x representing an input variable; a represents a trapezoid left bottom boundary, namely a left boundary with a membership degree of 0; b represents the upper left boundary of the trapezoid, i.e. the inflection point where the membership rises from 0; c represents the upper right boundary of the trapezoid, namely the inflection point of the membership falling from 1; d represents the right bottom boundary of the trapezoid, namely the right boundary with membership decreasing to 0; The change rate factor is used for adjusting the sensitivity degree of the membership function to the change rate of the input variable; the acceleration factor is used for adjusting the sensitivity degree of the membership function to the acceleration of the input variable; r represents the rate of change of the input variable x, and a represents the acceleration of the input variable x.

Further, the calculation formulas of r and a are as follows:， By introducing the change rate factor k1 and the acceleration factor k2, the segmented trapezoidal membership function can be more sensitive to the dynamic change of the input variable, so that the adaptability and the robustness of fuzzy control are improved. Meanwhile, the shape of the membership function can be flexibly controlled by adjusting the values of k1 and k2, so that the membership function is suitable for different control requirements.

Compared with the prior art, the application has the advantages that:

The voltage and current signals of the ultrasonic piezoelectric transducer are subjected to sampling, amplifying and filtering, square wave conversion and the like, so that the voltage and current phase difference is obtained and used as feedback quantity of frequency adjustment, the impedance characteristic and resonance frequency change of the transducer can be accurately reflected, a reliable frequency adjustment basis is provided for realizing dynamic impedance matching, and the problem of impedance mismatch caused by transducer resonance frequency drift is avoided.

The in-phase amplifying circuit and the active band-pass filter circuit are adopted to amplify and filter the voltage and current sampling signals, so that the signal-to-noise ratio and amplitude-frequency characteristics of the signals are effectively improved, and the accuracy and the reliability of phase difference detection are ensured.

The zero-crossing comparator is used for converting the sine wave signal into a square wave signal, so that a phase difference detection circuit is simplified, and the complexity and cost of the system are reduced; meanwhile, the voltage follower isolates the post-stage circuit, so that mutual interference is avoided, and the detection precision is improved.

The frequency is adaptively adjusted by adopting a fuzzy PID control algorithm, and parameters of a PID controller are dynamically adjusted according to the magnitude, the change rate and the acceleration of the phase difference digital quantity, so that the response speed and the accuracy of frequency tracking are improved.

The phase difference, the change rate and the acceleration of the phase difference are introduced into the fuzzy PID control algorithm, and the adaptability of the algorithm to the phase difference dynamic change is enhanced through the change rate factor and the acceleration factor, so that the frequency tracking performance is improved.

The single chip timer and the A/D converter are utilized to realize the measurement and digital quantity conversion of the phase difference pulse signals, so that an additional phase difference detection chip is avoided, and the hardware overhead of the system is reduced.

The output of the PID control algorithm is converted into the frequency control word of the DDS direct digital frequency synthesizer, so that the accurate adjustment of the excitation frequency is realized, the synchronous change is realized with the resonant frequency of the transducer, and the impedance dynamic matching of the two parties is ensured, thereby effectively improving the energy conversion efficiency of the transducer and reducing the energy loss.

Drawings

FIG. 1 is a circuit diagram of a frequency dynamic tracking module shown in accordance with some embodiments of the present description;

FIG. 2 is a voltage-current sampling circuit shown in accordance with some embodiments of the present description;

FIG. 3 is a signal amplification circuit shown according to some embodiments of the present description;

FIG. 4 is an oscilloscope input/output waveform of a signal amplifier circuit according to some embodiments of the present description;

FIG. 5 is a signal filtering circuit shown in accordance with some embodiments of the present description;

FIG. 6 is a diagram illustrating filter circuit input-output signals according to some embodiments of the present disclosure;

FIG. 7 is a pulse conversion circuit shown in accordance with some embodiments of the present description;

FIG. 8 is a conversion circuit oscilloscope waveform output shown according to some embodiments of the present description;

fig. 9 is a phase difference detection circuit according to some embodiments of the present description.

Detailed Description

The method and system provided in the embodiments of the present specification are described in detail below with reference to the accompanying drawings.

Fig. 1 is a circuit diagram of a frequency dynamic tracking module according to some embodiments of the present disclosure, and the dynamic matching technology compares a matching inductance dynamic adjustment and a frequency dynamic tracking scheme, so that the complexity of a hardware circuit for matching digital inductance dynamic adjustment is high and the implementation difficulty is high. The frequency dynamic tracking technology is selected to realize dynamic matching of the impedance of the piezoelectric transducer, so that a series of problems of impedance detuning and the like caused by resonance frequency drift of the piezoelectric transducer due to external environmental factors are solved. The frequency dynamic tracking scheme adopted in the application is based on a phase detection method, namely, the voltage and current phase information of a piezoelectric transducer loop is detected as feedback quantity, the phase difference of the voltage and current phase information is used for adjusting the excitation frequency of an ultrasonic signal until the phase difference is zero, and the load system reaches a resonance state at the moment. Therefore, in order to achieve the purpose of phase detection, a voltage and current sampling circuit is needed to collect voltage and current signals of the transducer, signal preprocessing such as amplification and filtering is needed after sampling, phase difference information in the voltage and current is finally detected and fed back to an MCU singlechip, and the MCU regulates and controls the frequency of excitation pulses in an ultrasonic signal generating circuit, so that the effect of dynamic frequency tracking is achieved.

As shown in fig. 2, in order to sample the voltage and current signals of the ultrasonic piezoelectric transducer, a precision sampling resistor R is connected in series in the transducer load loop. Because of the limitation of experimental equipment, the current signal is not convenient to collect directly, and the current signal is converted into a voltage signal proportional to the current signal by utilizing a sampling resistor. According to ohm's law, the voltage drop across the sampling resistor R is proportional to the current flowing through the resistor, i.e., u=ir, where U is the voltage drop, I is the current, and R is the resistance of the sampling resistor. The voltage sampling point is arranged between the piezoelectric transducer and the input end of the impedance matching module and is used for acquiring voltage signals at two ends of the transducer. The voltage waveform of the transducer can be directly observed by connecting the voltage sampling point through the channel 1 of the oscilloscope. The current sampling points are positioned at two ends of the sampling resistor R, and the voltage drop at two ends of the sampling resistor can be measured by connecting the current sampling points through the channel 2 of the oscilloscope. Since the voltage drop is proportional to the current, the current signal flowing through the transducer can be reflected indirectly by the measured voltage waveform. In order to ensure sampling precision and signal integrity, the precision sampling resistor selected should meet the following requirements: the resistance value precision is high, and the error is less than 1%; the power is high enough to bear the heat loss caused by the current; the frequency response is good, and the detail change of the current waveform can be accurately presented. After the voltage signal and the current signal of the piezoelectric transducer are obtained, the voltage signal and the current signal are respectively input into a subsequent amplifying and filtering circuit and a phase detection circuit for signal conditioning and processing. The amplifying and filtering circuit is used for improving the amplitude and the signal-to-noise ratio of the signals, and the phase detection circuit is used for extracting the phase difference between the voltage signals and the current signals and providing feedback basis for subsequent frequency tracking and impedance matching.

Fig. 3 is a signal amplifying circuit according to some embodiments of the present disclosure, in which a boost circuit has been used to boost the excitation voltage, but the signal amplifying circuit is designed to amplify the waveform because the impedance of the load system is relatively large, the current signal collected in the sampling circuit is relatively small, on the order of millivolts, and it is inconvenient to directly compare and measure with the voltage signal. The circuit adopts an in-phase amplifying circuit based on an operational amplifier chip OPA 846. According to the gain calculation formula of the in-phase amplifier, the amplification factor of the circuit is as follows: this was verified using a simulation platform to obtain the results shown in fig. 4, where the input signal was set to 1V and the frequency was 200KHz. The output signal is in phase with the input and is about 10V, indicating that the amplifying circuit is functioning properly.

Amplifying the obtained voltage sampling signal and current sampling signal, comprising: the amplifying process adopts an in-phase amplifying circuit which is composed of an OPA846 operational amplifier; the non-inverting input end of the OPA846 operational amplifier is connected with a voltage sampling signal or a current sampling signal, and the inverting input end of the OPA846 operational amplifier is connected with a feedback circuit formed by a resistor and a potentiometer; the amplification factors are changed by adjusting the potentiometer, so that the amplification factors of the voltage sampling signal and the current sampling signal are the same, and the phase relation between the voltage sampling signal and the current sampling signal is kept; the output of the OPA846 op-amp outputs amplified voltage and current sample signals.

Because the circuit works, the sampled voltage and current signals are inevitably doped with noise interference, and the subsequent signal processing is inconvenient, a filter circuit is required to be introduced before the circuit to filter the signals, and effective signals are extracted. According to the components of the circuit, passive filtering and active filtering are classified. Compared with the passive mode, the active filter circuit has the advantages of small volume, stable performance and the like, and has the advantages of high integrated operational amplifier gain, high input impedance, low output impedance, and signal amplification, isolation and buffering functions. And the amplification factor of the pre-signal amplifying circuit is limited, so that the sampling signal is further amplified to facilitate the subsequent signal processing operation, and the active filtering circuit is selected for filtering. The active filter circuit can be divided into 4 kinds of filter circuits of low pass, high pass, band pass and band stop according to different functions. Since the signal spectrum analysis finds that the effective signal concentrates in the frequency band of about 200KHz, an active band-pass filter circuit needs to be designed, as shown in fig. 5, and in general, c1=c2=c0. The gain of the filter circuit can be obtained by analyzing according to the circuit principle: wherein B is the passband width of the filter circuit. The center frequency is: ; passband width B: ; the quality factor Q is: 。

In the process of debugging the circuit by using the simulation platform, the central frequency of the band-pass filter can be changed by changing the resistance values of R1, R2 and R3, the bandwidth of the band-pass filter can be changed by changing the resistance values of R4 and Rf, and the number relation of R4 and Rf is found, namely the resistance value of Rf can not be more than twice the resistance value of R4, otherwise, the band-pass filter can lose the filtering effect. Based on the conclusion, in combination with the result of the signal spectrum analysis, the center frequency of the filter circuit was set to 200kHz, and the passband was set to a range of 20kHz before and after, i.e., the bandwidth was 40kHz.

And calculating each filtering parameter in the circuit according to the filtering condition and the formula. To simplify the calculation process, let r1=r31/2r2=r, then the center frequency is: . The values of the center frequency and the bandwidth are respectively brought into the above formula to obtain r1=r3=800Ω, r2=1.6k, c= lnF, r4=lk, rf=1.9k.

The active band-pass filter circuit is drawn in simulation software, transient analysis is carried out, and input and output signals of the circuit are obtained as shown in fig. 6. In the figure, a red waveform is an input signal, the frequency is set to be 200kHz, and the positive and negative oscillation amplitude is 50mV; the green waveform is a filtered output, and reaches steady state over a period of about 6-7T starting at 0, the response speed of the visible circuit is fast, and has a steady state amplitude of about 1.3V at a magnification of about 26. Therefore, the circuit can further amplify the sampling signal to meet the requirement of subsequent signal processing.

As shown in fig. 7, the pulse conversion circuit is used for converting the amplified and filtered voltage signal and current signal into square wave signals, and obtaining phase difference pulse signals of the voltage signal and the current signal through the phase comparison circuit. Firstly, the amplified and filtered voltage signal and current signal are respectively input into a zero comparison circuit formed by a voltage follower and a zero-crossing comparator. The voltage follower is composed of an operational amplifier, the input end of the voltage follower is connected with a voltage signal or a current signal, and the output end of the voltage follower is connected with the input end of the zero-crossing comparator. The voltage follower has the characteristics of high input impedance and low output impedance, and can isolate a signal source and reduce the influence on signals. The zero-crossing comparator is also implemented using an operational amplifier, the inverting input of which is grounded, and the non-inverting input of which is connected to the output of the voltage follower. When the instantaneous value of the input signal is greater than zero, the output of the zero-crossing comparator is high level; when the instantaneous value of the input signal is less than zero, the output of the zero-crossing comparator is low. The sine wave signal is converted into a square wave signal through a zero-crossing comparator, and a voltage square wave signal and a current square wave signal are obtained. Next, the voltage square wave signal and the current square wave signal are introduced into a phase comparison circuit for detecting a phase difference of both. The phase comparison circuit adopts an LM311N comparator, and the device has the advantages of high speed, low power consumption, TTL/CMOS output compatibility and the like. The voltage square wave signal is connected to the non-inverting input terminal (+) of the LM311N comparator, and the current square wave signal is connected to the inverting input terminal (-) of the LM311N comparator. The output end of the LM311N comparator is connected with a pull-up resistor, and when the voltage of the non-inverting input end is higher than the voltage of the inverting input end, the comparator outputs a high level; otherwise, the comparator outputs a low level. According to the phase relation between the voltage square wave signal and the current square wave signal, the property of the piezoelectric transducer load can be judged: when the voltage square wave signal leads the current square wave signal, the LM311N comparator outputs high-level pulse, which indicates that the load of the piezoelectric transducer is inductive; when the current square wave signal leads the voltage square wave signal, the LM311N comparator outputs a low level pulse, which indicates that the piezoelectric transducer load is capacitive. The high level pulse and the low level pulse output by the LM311N comparator are combined to form a phase difference pulse signal which is used as feedback quantity of subsequent frequency tracking and impedance matching.

In order to verify the performance of the pulse conversion circuit, a circuit model is built on a simulation platform and simulation test is carried out. As shown in FIG. 8, the input end of the setting circuit is a sine wave pulse signal source with the frequency of 200kHz and the amplitude of 3V, and after the input end of the setting circuit passes through the pulse conversion circuit, the output waveform of the oscilloscope is square wave pulse, which indicates that the conversion circuit can effectively convert the sine wave signal into square wave signal. Meanwhile, the simulation result shows that the circuit has high response speed and small conversion delay, and meets the real-time requirement of phase difference detection.

As shown in fig. 9, the purpose of the phase difference detection circuit is to convert the phase difference pulse signal output from the phase comparison circuit into a phase difference digital quantity corresponding thereto, so that the subsequent fuzzy PID control algorithm performs frequency adjustment. Firstly, a phase difference pulse signal output by a phase comparison circuit is connected to an external interrupt pin of a singlechip. The external interrupt triggering mode of the singlechip is set to be falling edge triggering, namely, the interrupt is triggered when the phase difference pulse signal jumps from high level to low level. This captures the high-level duration of the phase difference pulse signal, thereby obtaining phase difference information. In the interrupt service routine, a timer of the singlechip is used for counting the high-level duration time of the phase difference pulse signal. The counting frequency of the timer is consistent with the clock frequency of the singlechip, and the counting precision and range of the timer can be adjusted by setting the prescale coefficient and the counting initial value of the timer so as to adapt to phase difference pulse signals with different frequencies. When the phase difference pulse signal jumps from high level to low level, an external interrupt is triggered, and a timer starts counting. At the timing when the phase difference pulse signal transitions again from the low level to the high level, the timer count is stopped. By reading the value of the timer count register, a phase difference pulse width count value can be obtained, which is proportional to the high level duration of the phase difference pulse. In order to convert the phase difference pulse width count value into a digital quantity, the digital quantity is used as an analog signal input of a successive approximation type A/D converter built in the singlechip. The a/D converter samples and holds an input analog signal and converts it into a corresponding digital quantity by quantization coding. The successive approximation type A/D converter has the characteristics of high conversion speed and high precision, and can meet the real-time performance and precision requirements of phase difference detection.

The conversion process of the a/D converter is as follows: first, an input analog signal is sampled by a sample-and-hold circuit, and the sampled value is temporarily stored in a hold phase. Then, the amplitude of the analog signal is compared with the reference voltage output by a digital-to-analog converter (DAC) through the comparison and approximation of a Successive Approximation Register (SAR), and the value of the digital quantity is determined bit by bit. After repeated iterative comparison, the digital quantity corresponding to the amplitude of the analog signal, namely the phase difference digital quantity, is obtained. Finally, the phase difference digital quantity obtained by conversion is obtained by reading the data register of the A/D converter. The digital quantity is in linear relation with the high level duration of the phase difference pulse signal, and reflects the impedance characteristic and resonance frequency shift condition of the piezoelectric transducer.

After the phase difference digital quantity is obtained, the excitation frequency output of the ultrasonic signal source is controlled through a frequency adjustment algorithm, so that the excitation frequency output is consistent with the resonance frequency of the piezoelectric transducer, and dynamic impedance matching is realized. In the embodiment, a fuzzy PID control algorithm is adopted as a frequency adjustment algorithm, and the proportional coefficient, the integral coefficient and the differential coefficient of the PID controller are adaptively adjusted through parameters such as the magnitude, the change rate and the change acceleration of the phase difference digital quantity, so as to optimize the control performance. The basic principle of the fuzzy PID control algorithm is to combine classical PID control with fuzzy control, and utilize fuzzy reasoning rules to adjust parameters of the PID controller on line. Firstly, setting corresponding fuzzy language variables and membership functions according to the magnitude, the change rate and the change acceleration of the phase difference digital quantity, and fuzzifying the parameters into corresponding fuzzy subsets. And then, carrying out fuzzy reasoning on the parameters after blurring according to a preset fuzzy control rule table to obtain fuzzy control quantity of the parameters of the PID controller. Finally, the fuzzy control quantity is converted into the adjustment values of the actual proportional coefficient, integral coefficient and differential coefficient through the defuzzification processing.

In adjusting the PID controller parameters, the ratio coefficients (Kp), integral coefficients (Ki) and differential coefficients (Kd) are adaptively adjusted according to the following rules: when the absolute value of the phase difference digital quantity is larger than a preset first threshold value, the fact that the resonance frequency of the piezoelectric transducer is larger than the excitation frequency is indicated, and rapid adjustment is needed. At this time, the control response speed is increased by amplifying the comparison example coefficient according to a preset first multiple (for example, 1.5 times). On the contrary, when the absolute value of the phase difference digital quantity is smaller than the first threshold value, the frequency difference is smaller, the proportion coefficient can be properly reduced, the proportion coefficient is reduced according to a preset second multiple (such as 0.8 times), and the oscillation caused by the overlarge adjusting amplitude is avoided. When the rate of change of the phase difference digital quantity is greater than a preset second threshold, it is indicated that the frequency difference is changed faster, and the effect of differential control needs to be enhanced. At this time, the differential coefficient is amplified according to a preset third multiple (for example, 1.2 times), so that the rapidity and stability of the control system are improved. And on the contrary, when the change rate is smaller than the second threshold value, the differential coefficient is reduced according to a preset fourth multiple (such as 0.9 times), and the influence of differential control is weakened. When the change acceleration of the phase difference digital quantity is larger than a preset third threshold value, the trend of the frequency difference change is obvious, and the effect of integral control needs to be enhanced. At this time, the integral coefficient is amplified according to a preset fifth multiple (for example, 1.3 times), so as to accelerate the speed of eliminating the steady-state error. Otherwise, when the variation acceleration is smaller than the third threshold, the integration coefficient is reduced according to a preset sixth multiple (such as 0.85 times), so that the risk of integral saturation is reduced. Through the self-adaptive adjustment of the parameters of the PID controller by the rules, the performance of a control algorithm can be dynamically optimized according to the change condition of the resonant frequency of the piezoelectric transducer, and the speed and the accuracy of frequency tracking are improved. The adjusted PID controller outputs frequency adjustment quantity, controls the excitation frequency output of the ultrasonic signal source, and enables the excitation frequency output to be synchronous with the resonance frequency of the piezoelectric transducer in real time, and finally dynamic impedance matching is achieved.

In the fuzzy PID control algorithm, the phase difference digital quantity, the change rate of the phase difference digital quantity and the change acceleration of the phase difference digital quantity are required to be subjected to fuzzy processing, and are converted into corresponding membership values. The embodiment adopts the segmented trapezoidal membership function to realize fuzzy processing, and the shape of the membership function can be adjusted according to the characteristics of input variables by reasonably setting the parameters of the trapezoidal function, so that the adaptability of fuzzy control is improved. First, the phase difference digital quantity, the rate of change of the phase difference digital quantity, and the domain range of the acceleration of the change of the phase difference digital quantity, that is, the range of the values of these variables, are determined. And setting a proper domain boundary value according to actual engineering experience and test data. Then, fuzzy processing is carried out on the input variable by using the segmented trapezoidal membership function. The expression of the piecewise trapezoidal membership function is as follows: by introducing a rate of change factor into the linear function And acceleration factorThe slope of the membership function can be dynamically adjusted according to the change condition of the input variable, so that the membership function is more sensitive or duller; when the input variable x is in the interval (b, c), the membership degree is kept to be 1, when the input variable x is in the interval (c, d), the membership degree is linearly reduced from 1 to 0, the reduction speed is also influenced by the change rate and the acceleration, when the input variable x is more than or equal to d, the membership degree is 0, and the parameters of the piecewise trapezoidal membership function are reasonably setThe shape of the membership function can be adjusted according to actual control requirements, so that the fuzzy characteristic of the input variable can be reflected better. By adjustingAndThe sensitivity of the membership function to the change rate and the acceleration is changed, so that the adaptive control of different dynamic characteristics is realized. The phase difference digital quantity, the change rate and the acceleration are subjected to fuzzy processing by adopting the segmented trapezoid membership function, the shape of the membership function can be adaptively adjusted according to the dynamic characteristics of the input variable, and the flexibility and the adaptability of fuzzy control are improved. The performance of the fuzzy PID control algorithm can be optimized by reasonably setting membership function parameters, and the efficient frequency tracking control in the dynamic impedance matching of the piezoelectric transducer is realized.

Specifically, in this embodiment, the following domain ranges and parameter settings are obtained through actual engineering tests and data analysis: the range of the phase difference digital quantity is [ -180, 180], and the unit is degree. The parameters of the segmental trapezoidal membership function for setting the phase difference digital quantity are as follows: a= -180, b= -90, c=90, d=180, k1=0.02, k2=0.01; the range of the change rate of the phase difference digital quantity is [ -20, 20], which is expressed in degrees/second. The parameters of the piecewise trapezoidal membership function for setting the change rate of the phase difference digital quantity are as follows: a= -20, b= -10, c=10, d=20, k1=0.05, k2=0.02; the range of the variable acceleration of the phase difference digital quantity is [ -5,5], and the unit is degree/second. The segmental trapezoidal membership function parameters of the variable acceleration of the set phase difference digital quantity are as follows: a= -5, b= -2, c=2, d=5, k1=0.1, k2=0.05.

And carrying out fuzzy processing on the input variable according to the parameter setting. Taking the phase difference digital quantity as an example, the current phase difference digital quantity is 60 degrees, the change rate is 5 degrees/second, and the acceleration is 1 degree/second. Calculating the membership degree of the phase difference digital quantity, wherein x=60, r=5 and a=1; 60 belongs to the interval (b, c), namely (90, 90), satisfies the condition b < x.ltoreq.c, so that the membership degree u (x) =1 of the phase difference digital quantity, x= 5,r =1, a=0.5, 5 belongs to the interval (b, c), namely (10, 10), satisfies the condition b < x.ltoreq.c, so that the membership degree u (x) =1 of the phase difference digital quantity change rate, x=1, r=0.5, a= 0.2,1 belongs to the interval (b, c), namely (2, 2), satisfies the condition b < x.ltoreq.c, so that the membership degree u (x) =1 of the phase difference digital quantity change acceleration, when the phase difference digital quantity is 60 degrees, the change rate is 5 degrees/second, the membership degree of the phase difference digital quantity is 1 degree is calculated, the membership degree of the phase difference digital quantity is 1 degree of the fuzzy function is processed, the fuzzy control is used as a fuzzy control, the fuzzy control is used as an input fuzzy control parameter, and the fuzzy control is realized.

Dividing the domain range into a plurality of fuzzy subsets; and for each fuzzy subset, representing the corresponding membership function by adopting a segmented trapezoidal membership function. Taking the phase difference digital quantity as an example, the range of the domain of the phase difference digital quantity is divided into the following 7 fuzzy subsets: NB (negative big), NM (negative medium), NS (negative small), ZO (zero), PS (positive small), PM (median), PB (positive big). Each fuzzy subset corresponds to a segmented trapezoidal membership function, and the parameters are set as follows:

Fuzzy subset	Left bottom boundary (a)	Left upper border (b)	Right upper boundary (c)	Right bottom boundary (d)
					NB	-180	-180	-135	-90
NM	-135	-90	-45	0
					NS	-90	-45	-10	0
ZO	-45	-10	10	45
					PS	0	10	45	90
PM	0	45	90	135
					PB	90	135	180	180

The current phase difference digital quantity is 60 degrees, the change rate is 5 degrees/second, and the acceleration is 1 degree/second. The membership degree of the phase difference digital quantity is calculated, x=60, and the phase difference digital quantity is determined to belong to the fuzzy subset PM according to the magnitude of the phase difference digital quantity. Substituting x into the segmented trapezoidal membership function corresponding to PM: u (x) = (d-x)/(d-c) = (135-60)/(135-90) =0.833. Calculating the membership degree of the change rate of the phase difference digital quantity, and similarly dividing the domain range of the change rate of the phase difference digital quantity [ -20, 20] into 7 fuzzy subsets: NB, NM, NS, ZO, PS, PM, PB. Each fuzzy subset corresponds to a segmented trapezoidal membership function, and the parameters are set as follows:

Fuzzy subset	Left bottom boundary (a)	Left upper border (b)	Right upper boundary (c)	Right bottom boundary (d)
					NB	-20	-20	-15	-10
NM	-15	-10	-5	0
					NS	-10	-5	-1	0
ZO	-5	-1	1	5
					PS	0	1	5	10
PM	0	5	10	15
					PB	10	15	20	20

X=5, and it is determined that it belongs to the ambiguity subset PM according to the magnitude of the phase difference digital quantity change rate. Substituting x into the segmented trapezoidal membership function corresponding to PM: u (x) = (x-a)/(b-a) = (5-0)/(5-0) =1. And calculating the membership degree of the phase difference digital variable acceleration. Likewise, the range of the domain of the varying acceleration of the phase difference digital quantity [ -5,5] is divided into 7 fuzzy subsets: NB, NM, NS, ZO, PS, PM, PB. Each fuzzy subset corresponds to a segmented trapezoidal membership function, and the parameters are set as follows:

Fuzzy subset	Left bottom boundary (a)	Left upper border (b)	Right upper boundary (c)	Right bottom boundary (d)
					NB	-5	-5	-3.75	-2.5
NM	-3.75	-2.5	-1.25	0
					NS	-2.5	-1.25	-0.25	0
ZO	-1.25	-0.25	0.25	1.25
					PS	0	0.25	1.25	2.5
PM	0	1.25	2.5	3.75
					PB	2.5	3.75	5	5

X=1, and it is determined that it belongs to the fuzzy subset PS according to the magnitude of the phase difference digital quantity variation acceleration. Substituting x into the segmented trapezoidal membership function corresponding to PS: u (x) = (x-a)/(b-a) = (1-0)/(0.25-0) =0.8. The phase difference digital quantity, the change rate and the acceleration are subjected to fuzzy processing through a segmented trapezoid membership function, so that the following membership value is obtained: the membership degree of the phase difference digital quantity is 0.833, the membership degree of the phase difference digital quantity change rate is 1 corresponding to the fuzzy subset PM, the membership degree of the phase difference digital quantity change acceleration is 0.8 corresponding to the fuzzy subset PS. The membership values reflect the attribution degree of the input variable in the corresponding fuzzy subset, can be used as the input of a fuzzy controller, and can be used for generating the adjustment quantity of the parameters of the PID controller through fuzzy reasoning and fuzzy de-fuzzy processing to realize the self-adaptive frequency control.

Performing fuzzy reasoning according to preset fuzzy reasoning rules, namely membership of the phase difference digital quantity, membership of the change rate of the phase difference digital quantity and membership of the change acceleration of the phase difference digital quantity, and obtaining membership of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm; setting the domain range and fuzzy subsets of PID control algorithm parameters, wherein the domain range of the proportional coefficient Kp is [0, 10], and the proportional coefficient Kp is divided into 5 fuzzy subsets: VS (small), S (small), M (medium), B (large), VB (large). The range of the domain of integration coefficient Ki is [0,1], divided into 5 fuzzy subsets: VS, S, M, B, VB. The range of the argument of the differential coefficient Kd is [0,1], divided into 5 fuzzy subsets: VS, S, M, B, VB. Each rule in the fuzzy inference rule base takes the phase difference digital quantity, the change rate of the phase difference digital quantity and the language variable of the change acceleration of the phase difference digital quantity as preconditions, and the language variables of the proportional coefficient, the integral coefficient and the differential coefficient as conclusions. Rule 1: if the phase difference digital quantity is PM, the rate of change of the phase difference digital quantity is PM, and the rate of change acceleration of the phase difference digital quantity is PS, the proportional coefficient is B, the integral coefficient is S, and the differential coefficient is M. Rule 2: if the phase difference digital quantity is PS, the change rate of the phase difference digital quantity is PS, the change acceleration of the phase difference digital quantity is ZO, the proportional coefficient is M, the integral coefficient is M, and the differential coefficient is S.

Fuzzy reasoning process, precondition 1: the linguistic variable of the phase difference digital quantity is PM, and the precondition 2 is: the linguistic variable of the rate of change of the phase difference digital quantity is PM, provided that condition 3: the language variable of the variation acceleration of the phase difference digital quantity is PS, and a matched rule is searched in a fuzzy reasoning rule base according to preconditions. Matching to rule 1, then conclude: the linguistic variable of the proportional coefficient Kp is B, the linguistic variable of the integral coefficient Ki is S, and the linguistic variable of the differential coefficient Kd is M. The linguistic variables are converted into membership functions, corresponding fuzzy subsets are determined according to the linguistic variables of the proportional coefficients, the integral coefficients and the differential coefficients, and the linguistic variables are converted into corresponding membership functions. The membership function corresponding to each fuzzy subset is a triangle membership function, and the parameters are set as follows:

Fuzzy subset	Left bottom boundary	Vertex point	Right bottom boundary
				VS	0	0	2.5
S	0	2.5	5
				M	2.5	5	7.5
B	5	7.5	10
				VB	7.5	10	10

Obtaining the membership degree of the PID control algorithm parameters according to the language variables: the proportionality coefficient Kp belongs to the fuzzy subset B, the membership function thereof is a triangular membership function (5, 7.5, 10), the integral coefficient Ki belongs to the fuzzy subset S, the membership function thereof is a triangular membership function (0,0.25,0.5), the differential coefficient Kd belongs to the fuzzy subset M, and the membership function thereof is a triangular membership function (0.25,0.5,0.75).

In conclusion, membership functions of parameters of the PID control algorithm are obtained through fuzzy reasoning and language variable conversion: the membership function of the proportionality coefficient Kp is a triangular membership function (5, 7.5, 10), the membership function of the integral coefficient Ki is a triangular membership function (0,0.25,0.5), and the membership function of the derivative coefficient Kd is a triangular membership function (0.25,0.5,0.75). The membership functions represent the attribution degree of PID control algorithm parameters in corresponding fuzzy subsets, can be used for the subsequent fuzzy solving process, generate specific parameter values and realize self-adaptive PID control. Other fuzzy inference methods, such as Mamdani inference, sugeno inference, etc., may also be preferably employed to accommodate different control requirements.

Performing defuzzification treatment on membership degrees of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm by adopting a gravity center method to obtain the proportional coefficient, the integral coefficient and the differential coefficient after self-adaptive adjustment; calculating a solution fuzzy value of the proportionality coefficient Kp, dividing the area under the membership function curve of the proportionality coefficient Kp into two trapezoidal subregions, and respectively calculating the area and the barycenter abscissa of each subregion: sub-region 1: trapezoid (5,0,7.5,1) with an area s1= (7.5-5) ×1/2=1.25, centroid abscissa x1= (5+7.5)/3=4.17; sub-region 2: trapezoids (7.5, 1, 10, 0) with an area s2= (10-7.5) ×1++2=1.25 and centroid abscissa x2= (7.5×2+10) ++3=8.33. Multiplying the area of each sub-region by the abscissa of its centroid to obtain the product: p1=s1 x1 = 1.25 x 4.17=5.21, p2=s2 x2 = 1.25×8.33=10.41. And adding the products of all the subareas, and dividing by the sum of the areas of all the subareas to obtain the barycenter abscissa of the membership function of the proportionality coefficient Kp: kp= (p1+p2)/(s1+s2) = (5.21+10.41)/(1.25+1.25) =6.25, and thus the scaling factor kp=6.25 after the adaptation.

The method comprises the steps of calculating a solution ambiguity value of an integral coefficient Ki, dividing the area under a membership function curve of the integral coefficient Ki into two trapezoid subareas, and calculating the area and the barycenter abscissa of each subarea respectively: sub-region 1: trapezoid (0,0,0.25,1) with an area s1= (0.25-0) ×1/2=0.125, and centroid abscissa x1= (0+0.25)/3=0.083; sub-region 2: trapezoid (0.25,1,0.5,0) with an area s2= (0.5-0.25) ×1/2=0.125 and centroid abscissa x2= (0.25×2+0.5)/(3=0.333). Multiplying the area of each sub-region by the abscissa of its centroid to obtain the product: p1=s1× x1=0.125 x 0.083=0.010, p2=s2× x2=0.125 x 0.333=0.042. And adding the products of all the subareas, dividing by the sum of the areas of all the subareas to obtain the barycenter abscissa of the membership function of the integral coefficient Ki: ki= (p1+p2)/(s1+s2) = (0.010+0.042)/(0.125+0.125) =0.208, and thus, the integration coefficient ki=0.208 after the adaptation.

Calculating a solution ambiguity value of the differential coefficient Kd, and equally dividing the area under the membership function curve of the differential coefficient Kd into two trapezoid subareas, and respectively calculating the area and the barycenter abscissa of each subarea: sub-region 1: trapezoid (0.25,0,0.5,1) with an area s1= (0.5-0.25) ×1++2=0.125, centroid abscissa x1= (0.25+0.5) ++3=0.25, sub-area 2: trapezoid (0.5,1,0.75,0) with an area s2= (0.75-0.5) ×1/2=0.125 and centroid abscissa x2= (0.5×2+0.75)/(3=0.583). Multiplying the area of each sub-region by the abscissa of its centroid to obtain the product: p1=s1× x1=0.125 x 0.25 = 0.031, p2=s2× x2=0.125 x 0.583=0.073. And adding the products of all the subareas, and dividing by the sum of the areas of all the subareas to obtain the barycenter abscissa of the membership function of the differential coefficient Kd: kd= (p1+p2)/(s1+s2) = (0.031+0.073)/(0.125+0.125) =0.417, and thus the differential coefficient kd=0.417 after the adaptation. In summary, the membership function of the PID control algorithm parameter is defuzzified by a gravity center method, and the parameter value after self-adaptive adjustment is obtained: the proportionality coefficient kp=6.25, the integral coefficient ki=0.208, and the differential coefficient kd=0.417. These parameter values can be directly applied to the PID control algorithm to realize the adaptive frequency control. Compared with the PID control with fixed parameters, the self-adaptive PID control can automatically adjust the control parameters according to the dynamic characteristics and external disturbance of the system, and the robustness and the adaptability of the control system are improved.

Substituting the self-adaptive adjusted proportional coefficient, integral coefficient and differential coefficient into a PID control algorithm, taking the phase difference digital quantity as input, and calculating to obtain a frequency adjustment value; and converting the frequency adjustment value into a frequency control word of the DDS direct digital frequency synthesizer, controlling the DDS direct digital frequency synthesizer to output an excitation signal with a frequency corresponding to the frequency control word, and dynamically adjusting the excitation frequency of the piezoelectric transducer.

Calculating the output of the PID control algorithm, and setting the current moment as k and the phase difference digital quantity as e (k), wherein the output u (k) of the PID control algorithm is as follows: u (k) =kp×e (k) +ki×Σ [ i=0, k ] e (i) +kd× [ e (k) -e (k-1) ], wherein Σ [ i=0, k ] e (i) represents the cumulative sum of the phase difference digital amounts from time 0 to time k. At the current time k=10, the phase difference digital quantity e (10) =60, the phase difference digital quantity e (9) =55 at the previous time, and the sum Σi=0, 10] e (i) =550, then: u (10) =6.25×60+0.208×550+0.417× (60-55) = 491.35, and therefore the frequency adjustment value at the current time is 491.35.

The frequency regulation value is converted into a DDS frequency control word, the DDS direct digital frequency synthesizer generates a sine wave signal in a digital mode, and the output frequency of the sine wave signal is determined by the frequency control word. Let the clock frequency of DDS be fclk, the bit number of the phase accumulator be N, then the relation between the frequency fout of DDS output signal and the frequency control word M is: fout=m×fclk++2n, dds clock frequency fclk=100 MHz, phase accumulator bit n=32, then frequency resolution is: Δf=fclk ≡2ζ=100 MHz 2.32.apprxeq.0.023 Hz. Converting the frequency adjustment value 491.35 into a frequency control word M: m= 491.35 ++Δf= 491.35 0.023 ≡ 21363, the rounding off yields the frequency control word m=21363.

The DDS is controlled to output an excitation signal, the frequency control word m=21363 is written into a frequency control register of the DDS, and the DDS outputs the frequency as follows: fout=m×fclk ≡2 ζ n=21363×100MHz ≡2 ζ 491.35Hz, and the sine wave signal of this frequency is used as the excitation signal of the piezoelectric transducer to dynamically adjust the vibration frequency. In conclusion, the output of the self-adaptive PID control algorithm is converted into a DDS frequency control word, so that the dynamic adjustment of the excitation frequency of the piezoelectric transducer is realized. According to the phase difference digital quantity, parameters Kp, ki and Kd of the self-adaptive PID control algorithm are calculated through fuzzy reasoning and defuzzification processing. The phase difference digital quantity is used as input of PID control algorithm to calculate frequency regulation value. And converting the frequency adjustment value into a DDS frequency control word, and writing the DDS frequency control word into a DDS frequency control register. The DDS outputs a sine wave signal of a frequency corresponding to the frequency control word as an excitation signal of the piezoelectric transducer. The steps are repeated, so that the dynamic adjustment of the excitation frequency of the piezoelectric transducer is realized, and the piezoelectric transducer always works in an optimal vibration state.

Claims

1. An impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking comprises the following steps:

sampling the voltage and the current of the ultrasonic piezoelectric transducer; the current sampling is carried out by connecting a sampling resistor in series in a piezoelectric transducer load loop, and converting a current signal into a voltage signal for sampling;

Amplifying and filtering the obtained voltage sampling signal and current sampling signal; the amplifying treatment adopts an in-phase amplifying circuit, and the filtering treatment adopts an active band-pass filtering circuit;

Converting the amplified and filtered voltage sampling signal and current sampling signal into a voltage square wave signal and a current square wave signal through a zero comparison circuit; the zero comparison circuit comprises a voltage follower and a zero-crossing comparator; the voltage follower is used for isolating a later-stage circuit, so that mutual interference is avoided; the zero-crossing comparator adopts an LM311N comparator to convert the sine wave into a square wave;

Acquiring a phase difference pulse signal of the voltage square wave signal and the current square wave signal by using a phase comparison circuit; the voltage square wave signal is connected to the same-phase end of the phase comparison circuit; the current square wave signal is connected to the inverting terminal of the phase comparison circuit;

The calculated phase difference digital quantity of the phase difference pulse signals is used as a feedback quantity of frequency adjustment;

according to the obtained phase difference digital quantity, the output of the excitation frequency of the ultrasonic signal source is controlled through a frequency adjustment algorithm, and the excitation frequency is adjusted to be consistent with the resonance frequency of the piezoelectric transducer:

A fuzzy PID control algorithm is adopted as a frequency adjustment algorithm;

According to the magnitude, the change rate and the change acceleration of the phase difference digital quantity, the proportional coefficient, the integral coefficient and the differential coefficient of a PID control algorithm are adjusted;

Amplifying the proportional coefficient according to a preset first multiple when the absolute value of the phase difference digital quantity is larger than a preset first threshold value; otherwise, the scaling factor is reduced according to a preset second multiple;

Amplifying the differential coefficient according to a preset third multiple when the change rate of the phase difference digital quantity is larger than a preset second threshold value; otherwise, reducing the differential coefficient according to a preset fourth multiple;

When the variation acceleration of the phase difference digital quantity is larger than a preset third threshold value, amplifying an integral coefficient according to a preset fifth multiple; otherwise, reducing the integral coefficient according to a preset sixth multiple;

Fuzzy processing is carried out on the phase difference digital quantity, the change rate of the phase difference digital quantity and the change acceleration of the phase difference digital quantity by adopting a segmental trapezoid membership function, so as to obtain corresponding membership;

the steps are repeated to enable the excitation frequency and the resonance frequency of the piezoelectric transducer to synchronously change, and impedance dynamic matching is carried out.

2. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 1, wherein:

The center frequency of the active band-pass filter circuit is set to the operating frequency of the ultrasonic transducer.

3. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 1, wherein:

Amplifying the obtained voltage sampling signal and current sampling signal, comprising:

The amplifying process adopts an in-phase amplifying circuit which is composed of an OPA846 operational amplifier;

the non-inverting input end of the OPA846 operational amplifier is connected with a voltage sampling signal or a current sampling signal, and the inverting input end of the OPA846 operational amplifier is connected with a feedback circuit formed by a resistor and a potentiometer;

The amplification factors are changed by adjusting the potentiometer, so that the amplification factors of the voltage sampling signal and the current sampling signal are the same, and the phase relation between the voltage sampling signal and the current sampling signal is kept;

the output of the OPA846 op-amp outputs amplified voltage and current sample signals.

4. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 1, wherein:

obtaining a phase difference pulse signal of the voltage square wave signal and the current square wave signal by using a phase comparison circuit, wherein the phase difference pulse signal comprises:

the phase comparison circuit adopts an LM311N comparator;

The voltage square wave signal is connected to the non-inverting input end of the LM311N comparator, and the current square wave signal is connected to the inverting input end of the LM311N comparator;

The output end of the LM311N comparator is connected with a pull-up resistor, and when the voltage square wave signal leads the current square wave signal, the LM311N comparator outputs a high-level pulse to indicate that the load of the piezoelectric transducer is inductive; otherwise, the LM311N comparator outputs a low-level pulse, which indicates that the load of the piezoelectric transducer is capacitive;

The high level pulse and the low level pulse output by the LM311N comparator form a phase difference pulse signal.

5. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 1, wherein:

the calculated phase difference digital quantity of the phase difference pulse signal comprises the following steps:

Connecting the phase difference pulse signal output by the phase comparison circuit to an external interrupt pin of the singlechip;

Setting an external interrupt triggering mode of the singlechip as falling edge triggering, and triggering an interrupt when the phase difference pulse signal jumps from a high level to a low level;

In the interruption process, counting the high-level duration time of the phase difference pulse signals by using a timer of the singlechip; the counting frequency of the timer is consistent with the clock frequency of the singlechip; obtaining a phase difference pulse width count value by setting a prescaler coefficient and a count initial value of a timer;

At the moment when the phase difference pulse signal jumps from high level to low level, the timer stops counting, and the value of the timer counting register is read to obtain a phase difference pulse width counting value;

The phase difference pulse width count value is input as an analog signal of the A/D converter;

The analog signals are sampled, held and quantized and encoded by a successive approximation type A/D converter arranged in the singlechip, and the phase difference pulse width count value is converted into a corresponding phase difference digital quantity.

6. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 1, wherein:

according to the obtained phase difference digital quantity, the output of the excitation frequency of the ultrasonic signal source is controlled through a frequency adjustment algorithm, the excitation frequency is adjusted to be consistent with the resonance frequency of the piezoelectric transducer, and the method further comprises the following steps:

Performing fuzzy reasoning according to preset fuzzy reasoning rules, namely membership of the phase difference digital quantity, membership of the change rate of the phase difference digital quantity and membership of the change acceleration of the phase difference digital quantity, and obtaining membership of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm;

performing defuzzification treatment on membership degrees of a proportional coefficient, an integral coefficient and a differential coefficient of a PID control algorithm by adopting a gravity center method to obtain the proportional coefficient, the integral coefficient and the differential coefficient after self-adaptive adjustment;

substituting the self-adaptive adjusted proportional coefficient, integral coefficient and differential coefficient into a PID control algorithm, taking the phase difference digital quantity as input, and calculating to obtain a frequency adjustment value;

And converting the frequency adjustment value into a frequency control word of the DDS direct digital frequency synthesizer, controlling the DDS direct digital frequency synthesizer to output an excitation signal with a frequency corresponding to the frequency control word, and dynamically adjusting the excitation frequency of the piezoelectric transducer.

7. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 6, wherein:

The method comprises the steps of performing fuzzy processing on the phase difference digital quantity, the change rate of the phase difference digital quantity and the change acceleration of the phase difference digital quantity by adopting a segmented trapezoid membership function to obtain corresponding membership degree, and comprises the following steps:

determining a phase difference digital quantity, a change rate of the phase difference digital quantity and a discourse domain range of change acceleration of the phase difference digital quantity;

Dividing the domain range into a plurality of fuzzy subsets;

For each fuzzy subset, representing a corresponding membership function by adopting a segmented trapezoidal membership function, wherein the segmented trapezoidal membership function comprises four parameters which are respectively a left bottom boundary, a left upper boundary, a right upper boundary and a right bottom boundary of a trapezoid;

Determining a corresponding fuzzy subset according to the magnitude of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating to obtain the membership degree of the phase difference digital quantity;

determining a corresponding fuzzy subset according to the change rate of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating to obtain the membership degree of the change rate of the phase difference digital quantity;

And determining a corresponding fuzzy subset according to the magnitude of the variable acceleration of the phase difference digital quantity, substituting the phase difference digital quantity into a segmented trapezoidal membership function corresponding to the determined fuzzy subset, and calculating the membership degree of the variable acceleration of the phase difference digital quantity.

8. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 7, wherein:

The piecewise trapezoidal membership function expression is as follows:

9. The impedance matching method based on ultrasonic piezoelectric transducer frequency dynamic tracking according to claim 8, wherein:

The calculation formulas of r and a are as follows:

where r represents the rate of change of the input variable x, and a represents the acceleration of the input variable x.