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CN111152776B - Steering and braking coordination control method and system for unmanned formula racing car - Google Patents

Steering and braking coordination control method and system for unmanned formula racing car Download PDF

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CN111152776B
CN111152776B CN202010024663.2A CN202010024663A CN111152776B CN 111152776 B CN111152776 B CN 111152776B CN 202010024663 A CN202010024663 A CN 202010024663A CN 111152776 B CN111152776 B CN 111152776B
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steering
racing car
braking
control
wheel angle
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CN111152776A (en
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汪洪波
胡承磊
高含
蔡云庆
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Hefei University of Technology
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/20—Conjoint control of vehicle sub-units of different type or different function including control of steering systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
    • B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
    • B60T7/00—Brake-action initiating means
    • B60T7/12—Brake-action initiating means for automatic initiation; for initiation not subject to will of driver or passenger
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W10/00—Conjoint control of vehicle sub-units of different type or different function
    • B60W10/18—Conjoint control of vehicle sub-units of different type or different function including control of braking systems
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/10—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to vehicle motion
    • B60W40/105—Speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D6/00—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits
    • B62D6/002—Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits computing target steering angles for front or rear wheels
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B—PERFORMING OPERATIONS; TRANSPORTING
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    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/18—Braking system
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems

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Abstract

The invention discloses a method and a system for coordinated control of steering and braking of an unmanned formula racing car. The control system comprises an extensible entropy weight joint controller, a steering control system based on incremental PID and a brake control system based on an adaptive fuzzy neural network. The control method comprises planning of an ideal track, acquisition of ideal parameters, a control method of an extension entropy weight combined controller, a control method of a steering control system based on incremental PID and a control method of a brake control system based on an adaptive fuzzy neural network. The invention aims at a steering and braking coordination control system of the unmanned formula racing car, and performs combined control on steering and braking through the extendible entropy weight combined controller, thereby greatly reducing the interference between the steering control system and the braking system caused by the coupling relationship, reducing the risk of deviating from a racing track due to over-high speed or insufficient steering real-time property of the racing car, improving the overall performance of the racing car and reducing the control cost.

Description

Steering and braking coordination control method and system for unmanned formula racing car
Technical Field
The invention relates to the field of unmanned driving, in particular to a steering and braking coordination control method and system for an unmanned formula racing car.
Background
The unmanned formula competition of college students in China is a car design and manufacturing competition sponsored by the Chinese automobile engineering society and participated in by college students in schools of the related major of all college cars. As the race progresses, the speed of the race cars in a particular track increases.
Independent steering or braking control is mostly adopted when the domestic unmanned formula racing car passes a bend at the present stage, so that the bend is decelerated greatly and the racing car smoothly passes the bend, or slow-speed constant-speed control is directly adopted to ensure that the racing car does not deviate from a track when passing the bend, but the two methods greatly increase the duration of a single turn. Similar to a common household car, when a racing car turns over a curve, a steering and braking system has a certain coupling relation and strong nonlinearity, and if steering or braking control is independently adopted during the turning over, the lateral stability of the racing car is to be improved, and the racing car can be knocked down to a pile barrel or deviated from a track.
Disclosure of Invention
The invention provides a method and a system for coordination control of steering and braking of an unmanned formula racing car, which aims to reduce the risk of deviation of a racing track caused by over-high speed or insufficient steering real-time performance of the racing car, solve the problem of control over the lateral stability of the unmanned formula racing car based on steering and braking, overcome the coupling effect of steering and braking dynamics of the car, improve the overall performance of the racing car and reduce the control cost.
The invention is realized by adopting the following technical scheme: a method for coordinately controlling steering and braking of an unmanned formula racing car comprises the following steps:
sensing the surrounding track through an environment sensing system, planning an ideal running track of the racing car, and tracking the algorithm according to the front wheel turning angle delta of the racing car based on the trackfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*；
The method for controlling the extensible entropy weight joint controller comprises the following steps:
selecting the front wheel corner deltafAnd the longitudinal speed u is used as a characteristic quantity, a section P, a stable region J and an extension region E of the characteristic quantity of each characteristic quantity when the racing car runs are determined, and D (delta) is definedfU) characteristic state of said racing car at a time, said front wheel angle δfCharacterizing race carsThe longitudinal speed u represents a braking control characteristic of the racing car;
wherein the node region P is
Figure GDA0002775232830000021
A stable region J of the characteristic quantity of
Figure GDA0002775232830000022
Has an extension field E of
Figure GDA0002775232830000023
Wherein, deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2A maximum longitudinal speed for ensuring normal running of the racing car;
calculating real-time information entropy according to the characteristic quantities, and calculating the weight W (delta) of each characteristic quantity according to the information entropyf,u)；
Calculating a degree of association K (D) between the characteristic quantity and the stable domain according to the characteristic stable domain of the characteristic quantity to determine a corner control output and a brake control output;
Figure GDA0002775232830000024
when K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable domain, the longitudinal speed is low, the racing car can smoothly pass a bend without adopting braking action, and the control output of the turning angle is
Figure GDA0002775232830000025
When K is-1 ≦ K (D ≦ 0), the racing car feature state is within the extension range, the longitudinal speed is large, and the weight W (δ) of the feature amount is considered by the combined control of the steering control and the braking controlfU) the rotation angle control output is
Figure GDA0002775232830000026
The brake control output is Y (u) ═ W (u) K (D) u*；
When K (D) is less than-1, the longitudinal speed is too high, the vehicle speed is greatly reduced, then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
As a further improvement of the above solution, the method for coordinated control of steering and braking of formula racing unmanned vehicle further comprises:
the control method of the steering control system based on the incremental PID comprises the following steps: acquiring an actual steering wheel angle, calculating the number of PWM pulses required when a steering driver of the steering control system rotates to enable the actual steering wheel angle to be consistent with an ideal steering wheel angle, setting 2 degrees as a threshold value between the ideal steering wheel angle and the actual steering wheel angle, and performing incremental PID control when the difference between the ideal steering wheel angle and the actual steering wheel angle exceeds +/-2 degrees; wherein the ideal steering wheel angle is based on the ideal front wheel angle δf *And a gear ratio of the steering control system.
As a further improvement of the above solution, the method for coordinated control of steering and braking of formula racing unmanned vehicle further comprises:
the control method of the brake control system based on the self-adaptive fuzzy neural network comprises the following steps:
fuzzifying an input signal: wherein the input signal is the longitudinal speed error e of the racing caruAnd acceleration error eaThe number of network nodes is 2; and calculating the membership degree of each node by applying a bell-shaped function as follows:
Figure GDA0002775232830000031
in the formula, x is the input of a node i, i is 1, 2; j is 1, 2; { ai，bi，ciThe antecedent parameter set, the change of which will affect the specific shape of the membership function;
defining:
eu＝Y(u)-u
calculating the excitation strength of each node: multiplying two input signals, the output of the product being:
Figure GDA0002775232830000032
Githe output of the ith node represents the excitation strength of a rule;
calculating the normalized excitation intensity of the node: the expression is as follows:
Figure GDA0002775232830000041
calculate per node output:
Figure GDA0002775232830000042
in the formula (f)i＝piea+qieu+ri，{pi，qi，riThe parameter set of the node is called a back-piece parameter;
and calculating the sum of all input signals to obtain the brake oil pressure:
Figure GDA0002775232830000043
as a further improvement of the above scheme, the characteristic state of the racing car further comprises a standardization processing procedure: normalizing the characteristic quantity to obtain normalized characteristic quantity D (delta'fU'), the normalized formula is:
D(δ′f,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
wherein: dmin(δfU) and Dmax(δfU) is derived from the section field P.
As a further improvement of the above scheme, the weight W (delta) of the characteristic quantityfAnd u) the specific calculation method is as follows:
calculating a distance L (delta) between the feature quantity and the stable regionf,u)：
Figure GDA0002775232830000044
Calculating the real-time information entropy:
Figure GDA0002775232830000045
wherein,
Figure GDA0002775232830000046
i represents the number of characteristic quantities, and n is a positive integer;
calculating the weight W (delta) of each characteristic quantity according to the real-time information entropyf,u)：
Figure GDA0002775232830000047
Where K denotes the kth characteristic amount.
As a further improvement of the above solution, the method for acquiring the actual steering wheel angle includes:
and controlling the rotating speed of a steering driver of the racing car by adopting a PWM duty ratio signal, driving a steering column and a front wheel of the racing car to rotate by the rotation of the steering driver, and reading the steering wheel angle in real time by a steering wheel angle sensor positioned on the steering column.
As a further improvement of the above scheme, the specific calculation method of the number of PWM pulses is:
CI＝Kp(e(k)-e(k-1))+Kie(k)+Kd(e(k)-2e(k-1)+e(k-2))
wherein e (k) ═ Y (δ) - δfIs the error value of this time;
e (k) -e (k-1) is the difference value of the current error and the last error;
e (k-1) -e (k-2) is the difference value of the last error and the last error;
and k is the control frequency of the steering control system for controlling the steering wheel to rotate from the actual steering wheel angle to the ideal steering wheel angle.
For the calculation of the number of PWM pulses at a certain moment, there are
Figure GDA0002775232830000051
Wherein: get Kp＝1,Ki＝0,Kd＝0.01。
As a further improvement of the scheme, the steering driver is an epoch supergroup servo motor powered by a 36V power supply, the rated torque of the epoch supergroup servo motor is 1.27 N.m, and the epoch supergroup servo motor is matched with a speed reducer with the speed reduction ratio of 1: 24.
As a further improvement of the scheme, the front piece parameters and the back piece parameters are adjusted by adopting a hybrid learning algorithm combining a back propagation method and a least square method.
The invention also discloses a steering and braking coordination control system of the unmanned formula racing car, which comprises an extension entropy weight combination controller, a steering control system based on the incremental PID and a braking control system based on the adaptive fuzzy neural network, wherein the extension entropy weight combination controller, the steering control system based on the incremental PID and the braking control system based on the adaptive fuzzy neural network are controlled by any one of the steering and braking coordination control methods of the unmanned formula racing car.
The invention aims at a steering and braking coordination control system of the unmanned formula racing car, and performs combined control on steering and braking through the extendible entropy weight combined controller, thereby greatly reducing the interference between the steering control system and the braking system caused by the coupling relationship, reducing the risk of deviating from a racing track due to over-high speed or insufficient steering real-time property of the racing car, improving the overall performance of the racing car and reducing the control cost.
Drawings
FIG. 1 is a flow chart of a method for coordinating steering and braking of an unmanned formula racing car according to the present invention;
FIG. 2 is a schematic diagram of a steering control system of the coordinated control method for steering and braking of the formula racing unmanned vehicle according to the invention;
FIG. 3 is a schematic diagram of a brake control system of the coordinated control method for steering and braking of the formula racing unmanned vehicle according to the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The invention discloses an unmanned formula racing car steering and braking coordination control system which comprises an extension entropy weight combined controller, an incremental PID (proportion integration differentiation) -based steering control system and an adaptive fuzzy neural network-based braking control system.
The invention also discloses a coordination control method for steering and braking of the unmanned formula racing car, which comprises the following specific steps:
1. the environment sensing system of the unmanned formula racing car plans an ideal running track of a racing car by sensing a peripheral track and based on a track tracking algorithm according to a front wheel corner delta of the racing carfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*。
2. A control method of an extensible entropy weight joint controller.
Referring to fig. 1, the extensible entropy weight combination controller selects the front wheel corner δ of the racing carfAnd longitudinal speed u of racing car as characteristic quantity, front wheel steering angle deltafCharacterizing the steering control characteristics of the racing car; the longitudinal speed u characterizes the braking control characteristics of the racing car.
And determining the value range of each characteristic quantity when the racing car runs, wherein the range is the section area of the characteristic quantity and is represented by P. Then
Figure GDA0002775232830000071
A stable region J of the characteristic quantity of
Figure GDA0002775232830000072
Has an extension field E of
Figure GDA0002775232830000073
Wherein, deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2To ensure the maximum longitudinal speed of the car for normal running. Taking the steering and speed characteristics of the formula of unmanned racing car into consideration and taking deltaf1＝30°，δf2＝18°，u1＝25km/h，u2＝15km/h。
Definition D (δ)fU) represents a characteristic state of the racing car at a certain time, and the characteristic quantity is normalized to obtain a normalized characteristic quantity D (delta'fU'), standardized by the formula
D(δ′f,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
Wherein Dmin(δfU) and Dmax(δfU) is available from the node region P.
The extension L (delta) between the feature quantity and the stable region is calculated by the following formulaf,u)。
Figure GDA0002775232830000074
Computing real-time information entropy
Figure GDA0002775232830000075
Wherein,
Figure GDA0002775232830000076
i represents the number of characteristic quantities, and n is a positive integer.
Calculating the weight W (delta) of each feature quantity according to the information entropy obtained abovef,u)
Figure GDA0002775232830000081
Where K denotes the kth characteristic amount.
To calculate the degree of association between the feature quantity and the stable domain, the following association function is defined
Figure GDA0002775232830000082
When K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable domain, the longitudinal speed of the racing car is low at the moment, the racing car can smoothly pass a curve without adopting a braking action, a steering execution mechanism at the bottom layer strictly tracks a target corner sent from the upper layer, and a corner value is output at the bottom layer
Figure GDA0002775232830000083
When K is larger than or equal to-1 and D is smaller than or equal to 0, the characteristic state of the racing car is in the extension range, the longitudinal speed of the racing car is higher, a combined control strategy based on steering control and braking control is adopted, and the purpose is to keep the racing car transversely stable and avoid the racing car from deviating from the track and shorten the overbending time. Considering the weight of the characteristic quantity, the turning angle control output is
Figure GDA0002775232830000084
The brake control output is Y (u) ═ W (u) K (D) u*. Can be used forThe topology domain is a domain in which output steering and brake coordination control is required.
When K (D) is less than-1, the longitudinal speed is too high, the vehicle speed needs to be reduced greatly, and then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
3. The control method of the steering control system based on the incremental PID comprises the following steps: the incremental PID algorithm is used for controlling a racing car steering control system, the incremental PID algorithm is used for controlling the difference between an ideal rotating angle and an actual rotating angle, the algorithm process does not need to be accumulated, and the size of the current difference is only closely related to the difference of the last three times.
The racing car uses the super group servo motor of the era of 36V power supply as a steering control system driver, the rated torque of the super group servo motor is 1.27 N.m, and the speed reduction ratio of the super group servo motor is 1: 24. The PWM duty ratio signal is adopted to control the rotating speed of the motor, the motor rotates to drive the steering column and the front wheel to rotate, and the steering wheel corner sensor positioned above the steering column can read the steering wheel corner in real time. The ideal steering wheel angle can be obtained by calculating the ideal front wheel angle and the transmission ratio of a steering control system, and in order to stop the motor at a target position, the number of PWM pulses for controlling the motor to rotate until the actual steering wheel angle is consistent with the ideal steering wheel angle is calculated by using an incremental PID algorithm.
The incremental PID control is performed when 2 ° is set as a threshold value between the ideal steering wheel angle and the actual steering wheel angle, i.e., when the difference between the two exceeds ± 2 °. If the actual steering wheel angle is 5 degrees and the ideal steering wheel angle is 10 degrees, the PID algorithm is used for calculating that 1000 PWM waves are theoretically needed to enable the steering wheel to rotate from 5 degrees to 10 degrees, and the PWM waves are used for driving the servo motor to rotate next step. However, due to an algorithm error, 1000 PWM waves may actually rotate the steering wheel to a position where the actual steering wheel angle is 8 °, and since the incremental PID control is performed when the difference between the two set values exceeds ± 2 °, the default control is terminated when the actual steering wheel angle is 8 °.
The number of PWM pulses is:
CI＝Kp(e(k)-e(k-1))+Kie(k)+Kd(e(k)-2e(k-1)+e(k-2))
wherein e (k) ═ Y (δ) - δfIs the error value of this time;
e (k) -e (k-1) is the difference value of the current error and the last error;
e (k-1) -e (k-2) is the difference value of the last error and the last error;
and k is the control frequency of the steering control system for controlling the steering wheel to rotate from the actual steering wheel angle to the ideal steering wheel angle.
For the calculation of the number of PWM pulses at a certain moment, there are
Figure GDA0002775232830000091
After a plurality of real vehicle tests, K is takenp＝1,Ki＝0,KdWhen the value is 0.01, the control effect is preferable.
4. The control method of the brake control system based on the self-adaptive fuzzy neural network comprises the following steps: adaptive fuzzy neural network systems are also known as network-based adaptive fuzzy systems, abbreviated ANFIS, and were proposed by Jang Roger in 1993. The neural network learning system integrates the advantages of a learning mechanism of a neural network, the language reasoning ability of a fuzzy system and the like, makes up the respective defects, and belongs to a neural fuzzy system. Compared with other neuro-fuzzy systems, the ANFIS has the characteristics of convenience and high efficiency, and is successfully applied in a plurality of fields.
The control of the fuzzy neural network of the racing car brake control system has five levels for control:
the first layer is a blurring layer, which is responsible for blurring the input signal. Wherein the input signal is the longitudinal speed error e of the racing caruAnd acceleration error eaThe number of network nodes is 2. And calculating the membership degree of each node by applying a bell-shaped function as follows:
Figure GDA0002775232830000101
wherein x is an input to a node i (i ═ 1, 2); j is 1, 2; { ai，bi，ciThe set of antecedent parameters whose changes will affect the specific shape of the membership function, which may also be other suitable parameterized functions.
Defining:
eu＝Y(u)-u
the second layer is a rule layer, which is used to calculate the excitation strength of each rule, and multiplies the two input signals, the product of which is output as
Figure GDA0002775232830000102
GiFor the output of the ith node, the output of each node represents the fitness of a rule.
The third layer is a normalization layer used to calculate the normalized excitation strength of the rule, i.e., the normalized confidence of the ith rule.
Figure GDA0002775232830000103
The fourth layer is a node output layer, and the output of each node is calculated.
Figure GDA0002775232830000104
In the formula (f)i＝piea+qieu+ri，{pi，qi，riAnd the parameter set of the node is called a back-piece parameter.
The fifth layer is a total output layer, and the layer can calculate the sum of all input signals to obtain the brake oil pressure.
Figure GDA0002775232830000111
The front part parameters and the back part parameters of the membership functions are adjusted by adopting a hybrid learning algorithm combining a back propagation method and a least square method, so that the self-learning function of the fuzzy system based on data can be realized.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.

Claims (10)

1. A method for coordinately controlling steering and braking of an unmanned formula racing car is characterized by comprising the following steps:
sensing the surrounding track through an environment sensing system, planning an ideal running track of the racing car, and tracking the algorithm according to the front wheel turning angle delta of the racing car based on the trackfAnd calculating the ideal front wheel corner delta from the longitudinal speed u of the racing carf *And a desired longitudinal speed u*；
The method for controlling the extensible entropy weight joint controller comprises the following steps:
selecting the front wheel corner deltafAnd the longitudinal speed u is used as a characteristic quantity, a section P, a stable domain J and an extension domain E of the characteristic quantity of each characteristic quantity when the racing car runs are determined, and D (delta) is definedfU) characteristic state of said racing car at a time, said front wheel angle δfCharacterizing the steering control characteristic of the racing car, wherein the longitudinal speed u characterizes the braking control characteristic of the racing car;
wherein the node region P is
Figure FDA0002775232820000011
A stable region J of the characteristic quantity of
Figure FDA0002775232820000012
Has an extension field E of
Figure FDA0002775232820000013
Wherein, deltaf1Is the maximum front wheel angle, delta, of said car racef2Maximum front wheel angle u for ensuring normal running of said racing car1Is the maximum longitudinal speed, u, of said racing car2A maximum longitudinal speed for ensuring normal running of the racing car;
calculating real-time information entropy according to the characteristic quantities, and calculating the weight W (delta) of each characteristic quantity according to the information entropyf,u)；
Calculating a correlation degree K (D) between the characteristic quantity and the stable domain according to the stable domain of the characteristic quantity to determine a corner control output and a brake control output;
Figure FDA0002775232820000021
when K (D) is more than or equal to 0, the characteristic state of the racing car is in a stable domain, the longitudinal speed is low, the racing car can smoothly pass a bend without adopting braking action, and the control output of the turning angle is
Figure FDA0002775232820000022
When K is-1 ≦ K (D ≦ 0), the racing car feature state is within the extension range, the longitudinal speed is large, and the weight W (δ) of the feature amount is considered by the combined control of the steering control and the braking controlfU) the rotation angle control output is
Figure FDA0002775232820000023
The brake control output is Y (u) ═ W (u) K (D) u*；
When K (D) is less than-1, the longitudinal speed is too fast, the vehicle speed is greatly reduced, then the steering operation is carried out based on the upper layer command, and the brake control output is Y (u) ═ W (u) K (D) u*。
2. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 1, wherein: the steering and braking coordination control method of the formula racing unmanned vehicle further comprises the following steps:
the control method of the steering control system based on the incremental PID comprises the following steps: acquiring an actual steering wheel angle, calculating the number of PWM pulses required when a steering driver of the steering control system rotates to enable the actual steering wheel angle to be consistent with an ideal steering wheel angle, setting 2 degrees as a threshold value between the ideal steering wheel angle and the actual steering wheel angle, and performing incremental PID control when the difference between the ideal steering wheel angle and the actual steering wheel angle exceeds +/-2 degrees; wherein the ideal steering wheel angle is based on the ideal front wheel angle δf *And a gear ratio of the steering control system.
3. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 1, wherein: the steering and braking coordination control method of the formula racing unmanned vehicle further comprises the following steps:
the control method of the brake control system based on the self-adaptive fuzzy neural network comprises the following steps:
fuzzifying an input signal: wherein the input signal is the longitudinal speed error e of the racing caruAnd acceleration error eaThe number of network nodes is 2; and calculating the membership degree of each node by applying a bell-shaped function as follows:
Figure FDA0002775232820000031
in the formula, x is the input of a node i, i is 1, 2; j is 1, 2; { ai，bi，ciThe antecedent parameter set, the change of which will affect the specific shape of the membership function;
defining:
eu＝Y(u)-u
calculating the excitation strength of each node: multiplying two input signals, the output of the product being:
Figure FDA0002775232820000032
Githe output of the ith node represents the excitation strength of a rule;
calculating the normalized excitation intensity of the node: the expression is as follows:
Figure FDA0002775232820000033
calculate per node output:
Figure FDA0002775232820000034
in the formula (f)i＝piea+qieu+ri，{pi，qi，riThe parameter set of the node is called a back-piece parameter;
and calculating the sum of all input signals to obtain the brake oil pressure:
Figure FDA0002775232820000035
4. the method for coordinating steering and braking of formula racing unmanned vehicle of claim 1, wherein: the characteristic state of the racing car further comprises a standardization processing process: normalizing the characteristic quantity to obtain normalized characteristic quantity D (delta'fU'), the normalized formula is:
D(δ′f,u′)＝(D(δf,u)-Dmin(δf,u))/(Dmax(δf,u)-Dmin(δf,u))
wherein: dmin(δfU) and Dmax(δfU) is derived from the section field P.
5. As claimed in claim4 the steering and braking coordination control method of the unmanned formula racing car is characterized in that: the weight W (delta) of the characteristic quantityfAnd u) the specific calculation method is as follows:
calculating a distance L (delta) between the feature quantity and the stable regionf,u)：
Figure FDA0002775232820000041
Calculating the real-time information entropy:
Figure FDA0002775232820000042
wherein,
Figure FDA0002775232820000043
i represents the number of characteristic quantities, and n is a positive integer;
calculating the weight W (delta) of each characteristic quantity according to the real-time information entropyf,u)：
Figure FDA0002775232820000044
Where K denotes the kth characteristic amount.
6. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 2, wherein: the method for acquiring the actual steering wheel angle comprises the following steps:
and controlling the rotating speed of a steering driver of the racing car by adopting a PWM duty ratio signal, driving a steering column and a front wheel of the racing car to rotate by the rotation of the steering driver, and reading the steering wheel angle in real time by a steering wheel angle sensor positioned on the steering column.
7. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 2, wherein: the specific calculation method of the number of the PWM pulses comprises the following steps:
CI＝Kp(e(k)-e(k-1))+Kie(k)+Kd(e(k)-2e(k-1)+e(k-2))
wherein e (k) ═ Y (δ) - δfIs the error value of this time;
e (k) -e (k-1) is the difference value of the current error and the last error;
e (k-1) -e (k-2) is the difference value of the last error and the last error;
k is the control frequency of the steering control system for controlling the steering wheel to rotate from the actual steering wheel angle to the ideal steering wheel angle;
for the calculation of the number of PWM pulses at a certain moment, there are
Figure FDA0002775232820000051
Wherein: get Kp＝1,Ki＝0,Kd＝0.01。
8. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 6, wherein: the steering driver is an epoch supergroup servo motor powered by a 36V power supply, the rated torque of the epoch supergroup servo motor is 1.27 N.m, and the steering driver is matched with a speed reducer with the speed reduction ratio of 1: 24.
9. The method for coordinating steering and braking of formula racing unmanned vehicle of claim 3, wherein: and the front piece parameters and the back piece parameters are adjusted by adopting a hybrid learning algorithm combining a back propagation method and a least square method.
10. The utility model provides an unmanned formula car turns to and braking coordinated control system which characterized in that: the system comprises an extensible entropy weight combined controller, an incremental PID-based steering control system and an adaptive fuzzy neural network-based brake control system, wherein the extensible entropy weight combined controller, the incremental PID-based steering control system and the adaptive fuzzy neural network-based brake control system are controlled by the unmanned formula racing car steering and brake coordination control method of any one of claims 1-9.
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