CN109669345A - Underwater robot fuzzy motion control method based on ESO - Google Patents
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Abstract
Underwater robot fuzzy motion control method of the present invention based on ESO, it proposes based on ESO and PID fuzzy logic control, the deficiency of parameter adjustment cannot be carried out under complicated marine environment to make up existing PID control technology, and estimation and compensation in the case where external environmental interference to the athletic posture of submarine navigation device and environmental disturbances, the final athletic posture for realizing submarine navigation device under complicated marine environment are stablized.Method includes with the next stage: (1) acquisition of information and demand analysis；(2) system mode prediction and interference compensation；(3) pid parameter self-adjusting and AUV motion control.
Description
Technical field
The present invention relates to a kind of submarine navigation device fuzzy motion control method based on ESO, belongs to fuzzy logic control
Technical field.
Background technique
Autonomous Underwater Vehicle (AUV) is to provide simple, inexpensive, medium-term and long-term, collection ring for scientist and researcher
The tool of border data facilitates scientist to carry out underwater animals and plants investigation, foul water operation, seabed mapping, environmental monitoring etc. and appoints
Business.Underwater robot has the complexity of structured particularity and working environment, this makes AUV control parameter in different works
The selection made under environment is very difficult.Therefore controller itself should have the adaptivity of parameter and Shandong under different operating environment
Stick, to resist variation and uncertain environmental perturbation.
The CN201510536241.2 patent application proposed such as Zhejiang Polytechnical University and Jiangsu University of Science and Technology propose
CN201710595475.3 patent application.In these formerly disclosed technical solution, it not can solve due to AUV Nonlinear Dynamic
Mechanical characteristic and the problem of cause it to be difficult to carry out common Linear Control.I.e. under water during motion control, the power of AUV
Disturbance caused by characteristic, the uncertainty of hydrodynamic force coefficient and ocean current and wave can all directly result in AUV control flexibility
Decline, can control parameter adjustment capability will reflect, when meeting underwater execution task R AUV whether stable to AUV posture
Robustness demand.
There are still above-mentioned deficiencies for robot control parameter self-adaptative adjustment technology at present, and to external environmental interference into
Still improved place in need in row processing.
In view of this, special propose present patent application.
Summary of the invention
Underwater robot fuzzy motion control method of the present invention based on ESO, designed for solving
The above-mentioned problems of the prior art and propose based on ESO and PID fuzzy logic control, to make up existing PID control technology
The deficiency of parameter adjustment cannot be carried out under complicated marine environment, and to submarine navigation device in the case where external environmental interference
Athletic posture and environmental disturbances estimation and compensation, the final movement appearance for realizing submarine navigation device under complicated marine environment
State is stablized.
To realize above-mentioned purpose of design, the underwater robot fuzzy motion control method based on ESO, including
Have with the next stage:
(1) acquisition of information and demand analysis
AUV posture information and depth information are obtained by the sensor of carrying, user sends the AUV phase at bank base interface
Hope course and depth information to carry out work requirements analysis；
(2) system mode prediction and interference compensation
AUV termination is received by the course of Real-time Feedback and depth information after user demand information, carries out system shape by ESO
State prediction and interference compensation operation；
(3) pid parameter self-adjusting and AUV motion control
Fuzzy adjusts PID coefficient according to the error and error rate of posture information, realizes the self-adjusting of parameter；
Vertical rudder, the hydroplane of AUV is calculated according to controller as a result, realizing the course of AUV, pitch angle and depth
Control, meets the mission requirements that AUV works under marine environment.
If above-mentioned Basic Design is conceived, the application propose a kind of fuzzy controller (FPID+ESO) based on ESO come
Realize rapidity, the method for stability and robustness of AUV motion control.
The application is to improve the posture and deep-controlled performance of AUV, in the core control element of automatic disturbance rejection controller --- expand
On the basis of opening state observer ESO, fuzzy control is combined with PID control.
Traditional PID control has better simply control structure, easily operated, the advantages that stability is preferable and answered extensively
With.But in the actual production process, due to the non-linear and time variation of system；The performance of PID controller be it is variable, can not cope with
Due to environmental change is proposed to pid parameter the problem of updating.And fuzzy controller is a kind of to melt the control experience of people with strategy
Enter the control mode into controller；Accurate mathematical model is not needed；With strong antijamming capability, fast response time, and
The characteristics of system parameter variations being handled in time.From the angle for improving its working performance, fuzzy control has certainly
Adjustment, the feature of modification and improvement Fuzzy Controller Parameters or rule make system have the characteristics that independence and adaptive, guarantee
Control system properties constant realizes better control effect.
But the control rule table of conventional fuzzy control is summed up by experimental study to be controlled by system
The error and error rate of amount control to table look-up to obtain corresponding control output realization system；This control mode cannot be in ring
Change control rule under the constraints such as border variation, it is difficult to guarantee control effect；So conventional fuzzy control acts on underwater machine
Device people complex environment, interference is big, there is lag when motion process when, then when controlling controlled device, meeting
Occur that controlled device response speed is slow, and overshoot is excessive, or even phenomena such as vibrate.Therefore Adaptive Fuzzy PID is proposed
(FPID) on-line amending control parameter is realized in control, so that underwater robot be made to be adapted to complicated marine environment.
Although parameter self-tuning fuzzy PID controller realizes the adjustment problem of the parameter under different marine environment, but externally
Boundary's environmental disturbances are not controlled, therefore the present invention is seen while carrying out the adjustment of fuzzy parameter also by expansion state
The interference of device (ESO) real-time perfoming is surveyed to estimate and by disturbance estimated value compensation to system to solve the problems, such as external disturbance.
For the stage (2) in the above method, refinement and advantageous measure further below can be taken:
ESO carries out system mode prediction and Interference Estimation by the course of Real-time Feedback, pitching and depth information；
Predicted after obtaining AUV attitudes vibration information by ESO, feedback to AUV controller input terminal and expectation target value into
Row relatively obtains attitude error (e)；
ESO predicts to obtain error change amount (z2), it feeds back to system and generates the error rate (ec) after feedback.
For the stage (3) in the above method, refinement and advantageous measure further below can be taken:
Fuzzy controller calculates adjustment PID according to attitude error (e) and error rate (ec), by fuzzy reasoning
Coefficient is to realize the self-adjusting of pid parameter；
AUV be adjusted after parameter, the desired value u of AUV vertical rudder, diving-plane angle is calculated by PID0(t)；
ESO carries out Interference Estimation and carries out control compensation (z to the control amount of AUV3/ b), obtain the practical control amount u of AUV
(t)。
In conclusion the application has the advantage that is with beneficial effect:
1, posture information and Interference Estimation and predictive compensation are carried out to AUV by ESO, can be improved the anti-dry of AUV
Disturb ability；
2, fuzzy logic control passes through the ESO system state estimation carried out and interference compensation feedback and the actual appearance of AUV
The situation of change of state information and control amount realize pid parameter self-adjusting, thus improve AUV control system stability and
Anti-interference ability.
3, the rapidity, stability and robustness of AUV motion control are truly realized.
Detailed description of the invention
Fig. 1 is the Control system architecture block diagram of herein described method；
Fig. 2 is the flow diagram of AUV attitude control method；
Fig. 3 is that the algorithm (FPID+ESO) proposed based on PID, fuzzy and the application is navigated under no external environmental interference
To the comparison diagram of control effect；
Fig. 4 is the algorithm (FPID+ESO) based on PID, fuzzy and the application proposition under ocean current interference environmental disturbances
The comparison diagram of Heading control effect；
Fig. 5 is the algorithm (FPID+ESO) based on PID, fuzzy and the application proposition in no external environmental interference nutation
Face upward the comparison diagram of control effect；
Fig. 6 is the algorithm (FPID+ESO) based on PID, fuzzy and the application proposition under ocean current interference environmental disturbances
The comparison diagram of pitch control effect.
Specific embodiment
The application is described further with reference to the accompanying drawings and examples.
Embodiment 1, as shown in Figure 1, being mentioned to realize the underwater robot fuzzy motion control method based on ESO
Go out following kinetic control system:
Classical PID controller；And
With the fuzzy control model of fuzzy logic control set, pid control parameter is carried out by the real-time posture information of AUV
Adjustment, improve control parameter adaptability under various circumstances, improve the control stability of AUV；
Extended state observer ESO, ESO control information by the course fed back AUV or pitch information and AUV and carry out appearance
State observation and Interference Estimation carry out gesture feedback compensation to AUV and control compensate.The attitude error predicted by ESO
And the compensation rate of error rate feeds back to system input and interference prediction compensation rate feedback to control output end, to change
The control effect of kind fuzzy controller, handles AUV to environmental disturbances, has higher anti-interference ability, realizes appearance
State stability control.
As shown in Fig. 2, the underwater robot fuzzy motion control method based on ESO, the sensor carried by AUV
Course angle, pitch angle and depth information are obtained, extended state observer carries out posture and interference by the posture information obtained
Estimation carries out dynamic compensation to AUV, and it is poor that AUV is made by the controls input setting such as desired course and expectation pitching with feedback information,
Attitude error and error rate after obtaining ESO predictive compensation；Fuzzy carries out fuzzy push away by error and error rate
Reason carries out pid parameter adjustment, and controller output is calculated together according to obtained PID parameter is adjusted in real time in last PID controller
When the interference compensation value complement that is calculated of ESO repay the control output quantity that AUV is finally obtained in the output quantity of PID controller.
The method includes having with the next stage:
(1) acquisition of information and demand analysis
AUV obtains the information such as the course AUV, pitching and depth by sensors such as the AHRS of carrying；User is at bank base interface
AUV desired course and depth information are sent to carry out work requirements analysis；
(2) system mode prediction and interference compensation
AUV termination is received by the course of Real-time Feedback and depth information after user demand information, carries out system shape by ESO
State prediction and interference compensation operation.Specifically, it is divided into following implementation steps:
Step 2.1,
Course, pitching and the depth information that ESO obtains AUV feedback carry out posture state observation and interference compensation operates,
Posture observation and the expectation posture of input (course or pitching) are obtained into attitude error e as difference, the differential value that ESO is observed is anti-
It is fed on posture error rate, obtains posture error rate ec；Wherein, the present invention is extended using three ranks
Its expression formula of state observer are as follows:
e1=z1-y；
In formula: e1For the systematic observation error of ESO；z1, z2, z3For extended state observer output；As shown in Figure 1, z1For
ESO carries out posture observation, z2For the observation of the ESO attitude error change rate observed, z3Disturbance-observer is carried out for ESO
To the offset of system control input；δ is linearly interval；β1, β2, β3For extended mode observer output error weight factor；b
For penalty coefficient, b value is 0.01 in the present invention；α1And α2It is usually taken to be 0.5 and 0.25；Fal (x, α, δ) function expression is such as
Under:
Step 2.2,
Posture error e=r (t)-z can be obtained from above1-y(t)；Posture error rate
(3) pid parameter self-adjusting and AUV motion control
Fuzzy adjusts PID coefficient according to the error and error rate of posture information, realizes the self-adjusting of parameter；
Vertical rudder, the hydroplane of AUV is calculated according to controller as a result, realizing the course of AUV, pitch angle and depth
Control, meets the mission requirements that AUV works under marine environment.Specifically, it is divided into following steps:
Step 3.1,
The Fuzzy processing of system input quantity and output quantity；By course (pitching) error e and course (pitching) error change
Rate ec is fuzzy to be converted into the Linguistic Value to match.E and ec is the input quantity of controller, blurring obtain it is negative big, bear, bear it is small,
Zero, it is just small, center, it is honest fuzzy subset, corresponding record be { NB, NM, NS, ZO, PS, PM, PB } in invention, course
(pitching) controls error in (- 2 π, 2 π) range, and course (pitching) controls error rate in (- 2,2) range, by posture
Deviation e and error rate ec is quantified as { -3, -2, -1,0,1,2,3 }.Similarly, by output variable Δ kp, Δ ki, Δ
kd, it is fuzzy to turn to fuzzy subset { negative big, to bear, bear small, zero, just small, center is honest }, be denoted as NB, NM, NS, ZO, PS,
PM, PB }, quantization is arrived within the scope of the domain of { -3, -2, -1,0,1,2,3 }.
Step 3.2,
The foundation of fuzzy control rule obtains rule by expertise and experiment and establishes fuzzy rule base, realizes and exist
The self-adjusting of pid parameter when environmental change.In view of the stability of system, response speed, overshoot and steady state error etc.,
The k in control processp, ki, kdAdjustment rule it is as follows:
(1) when error | e | when larger, to make system that there is preferable quick tracking performance, no matter the variation tendency of error
How biggish k should all be takenpWith lesser kd, while to avoid system response from larger overshoot occur, reply integral action is limited
System, takes lesser kiValue；
(2) when error | e | when being in median size, to make system response that there is lesser overshoot, kpShould take it is smaller, simultaneously
For the response speed for guaranteeing system, kiAnd kdSize wants moderate, wherein kdValue be affected to system response；
(3) when error | e | when smaller, to guarantee that system has preferable steady-state performance, kpAnd kiWhat should be taken is bigger, simultaneously
To avoid system from vibrating near setting value, and consider the interference free performance of system, when | ec | when smaller, kdIt can take big
It is some；When | ec | when larger, kdIt should take smaller.
The adjusting of pid parameter must simultaneously take account influence of 3 control parameters to control effect in different situations
And their interaction.It is available following fuzzy according to the expertise of Test Summary and a large amount of emulation experiment
Control rule table, as shown in table 1:
1 k of tablep, ki, kdState modulator adjustment rule
Step 3.3,
Fuzzy reasoning can obtain fuzzy rule, Ifeis A, andecis B, then k by table 1pIs C, kiIs D, kdis E
Deng 49, Mandani operation method is used herein, then total fuzzy relation are as follows:Ri represents control system in formula
Each fuzzy rule.
Step 3.4,
Deblurring processing, since fuzzy control the reasoning results are fuzzy value, it is not directly applicable controll plant, because
This needs to carry out de-fuzzy to it, to be converted into the precise volume that actuator can execute.Gravity model appoach is used in the present invention
Ambiguity solution operation is carried out, calculation formula is as follows:In formula, z*For exact value,To be subordinate to
Degree, ωjFor domain.Wherein degree of membershipIt is to be calculated by membership function, is calculated be subordinate to using triangular membership functions herein
Category degree.
Step 3.5,
By blurring, fuzzy reasoning and deblurring process, final kp, ki, kd；Specific adjustment formula is as follows:
kp=k 'p+ { e, ec } * Kp=k 'p+Δkp
ki=k 'i+ { e, ec } * ki=k 'i+Δki
kd=k 'd+ { e, ec } * kd=k 'd+Δkd
Step 3.6,
Output obtains AUV control rudder angle u after fuzzy controller calculates0(t), at this time in u0(t) ESO is added in
Carry out the compensation rate z for the system disturbance that Interference Estimation is calculated3/ b finally obtains the final control output quantity u (t) of system,
That is AUV carries out expectation rudder angle value required when gesture stability.Calculation formula is as follows:
U (t)=u0(t)-z3/b；
MATLAB Simulation results are as shown in Fig. 3, Fig. 4, Fig. 5 and Fig. 6.The result shows that posture control of the present invention in AUV
There is better control stability and better anti-interference ability in system.
It should be understood that for those of ordinary skills, it can be modified or changed according to the above description,
And all these modifications and variations should all belong to the protection domain of appended claims of the present invention.
Claims (3)
1. a kind of underwater robot fuzzy motion control method based on ESO, it is characterised in that: include with the next stage,
(1) acquisition of information and demand analysis
AUV posture information and depth information are obtained by the sensor of carrying, user sends AUV desired course at bank base interface
With depth information to carry out work requirements analysis；
(2) system mode prediction and interference compensation
AUV termination is received by the course of Real-time Feedback and depth information after user demand information, and it is pre- to carry out system mode by ESO
It surveys and interference compensation operates；
(3) pid parameter self-adjusting and AUV motion control
Fuzzy adjusts PID coefficient according to the error and error rate of posture information, realizes the self-adjusting of parameter；
Vertical rudder, the hydroplane of AUV is calculated according to controller as a result, realizing the control in the course, pitch angle and depth of AUV
System, meets the mission requirements that AUV works under marine environment.
2. the underwater robot fuzzy motion control method according to claim 1 based on ESO, it is characterised in that:
Include the following steps in the stage (2),
ESO carries out system mode prediction and Interference Estimation by the course of Real-time Feedback, pitching and depth information；
It is predicted after obtaining AUV attitudes vibration information by ESO, feedback is compared to AUV controller input terminal and expectation target value
Compared with and obtain attitude error (e)；
ESO predicts to obtain error change amount (z2), it feeds back to system and generates the error rate (ec) after feedback.
3. the underwater robot fuzzy motion control method according to claim 2 based on ESO, it is characterised in that:
Include the following steps in the stage (3),
Fuzzy controller calculates adjustment PID coefficient according to attitude error (e) and error rate (ec), by fuzzy reasoning
To realize the self-adjusting of pid parameter；
AUV be adjusted after parameter, the desired value u of AUV vertical rudder, diving-plane angle is calculated by PID0(t)；
ESO carries out Interference Estimation and carries out control compensation (z to the control amount of AUV3/ b), obtain the practical control amount u (t) of AUV.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110147042A (en) * | 2019-05-28 | 2019-08-20 | 金力 | A kind of upright AGV car body control method based on fuzzy control combination PID control |
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| CN111446898A (en) * | 2020-03-09 | 2020-07-24 | 中国海洋大学 | Low-cost AUV speed estimation method based on fuzzy logic and extended state observer |
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| CN113805598A (en) * | 2020-06-11 | 2021-12-17 | 中国科学院沈阳自动化研究所 | Navigation control method for under-actuated AUV (autonomous underwater vehicle) |
| CN115562313A (en) * | 2022-10-17 | 2023-01-03 | 武汉理工大学 | Autonomous underwater vehicle motion control method for pier flaw detection |
| CN116774715A (en) * | 2023-05-31 | 2023-09-19 | 新兴际华(北京)智能装备技术研究院有限公司 | Underwater vehicle attitude control method and device |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103401501A (en) * | 2013-04-15 | 2013-11-20 | 湖南大学 | Permanent magnet synchronous motor (PMSM) servo system control method based on fuzzy and active disturbance rejection control |
| CN103973143A (en) * | 2014-05-09 | 2014-08-06 | 浙江大学 | Control method for restraining point potential fluctuation in three-level grid-connected inverter |
| CN104281055A (en) * | 2014-03-18 | 2015-01-14 | 江南大学 | Active-disturbance-rejection control method for temperature of a constant stirring polypropylene reaction kettle |
| CN104590253A (en) * | 2014-12-16 | 2015-05-06 | 电子科技大学 | Yaw angular velocity control method for four-wheel independent driving electric vehicle |
| CN105775092A (en) * | 2016-01-25 | 2016-07-20 | 武汉尼维智能科技有限公司 | Course control system and method for unmanned surface vehicle |
| US20160209816A1 (en) * | 2015-01-21 | 2016-07-21 | Linestream Technologies | Cascaded active disturbance rejection controllers |
| CN105892475A (en) * | 2016-05-04 | 2016-08-24 | 中国海洋大学 | Underwater glider attitude control algorithm based on fuzzy PID |
| CN106411183A (en) * | 2016-09-27 | 2017-02-15 | 淮阴工学院 | Linear optimization auto-disturbance-rejection compound Kalman filter control method of motor synchronous system |
| CN108241292A (en) * | 2017-12-07 | 2018-07-03 | 西北工业大学 | A kind of underwater robot sliding-mode control based on extended state observer |
-
2018
- 2018-12-24 CN CN201811580167.4A patent/CN109669345B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103401501A (en) * | 2013-04-15 | 2013-11-20 | 湖南大学 | Permanent magnet synchronous motor (PMSM) servo system control method based on fuzzy and active disturbance rejection control |
| CN104281055A (en) * | 2014-03-18 | 2015-01-14 | 江南大学 | Active-disturbance-rejection control method for temperature of a constant stirring polypropylene reaction kettle |
| CN103973143A (en) * | 2014-05-09 | 2014-08-06 | 浙江大学 | Control method for restraining point potential fluctuation in three-level grid-connected inverter |
| CN104590253A (en) * | 2014-12-16 | 2015-05-06 | 电子科技大学 | Yaw angular velocity control method for four-wheel independent driving electric vehicle |
| US20160209816A1 (en) * | 2015-01-21 | 2016-07-21 | Linestream Technologies | Cascaded active disturbance rejection controllers |
| CN105775092A (en) * | 2016-01-25 | 2016-07-20 | 武汉尼维智能科技有限公司 | Course control system and method for unmanned surface vehicle |
| CN105892475A (en) * | 2016-05-04 | 2016-08-24 | 中国海洋大学 | Underwater glider attitude control algorithm based on fuzzy PID |
| CN106411183A (en) * | 2016-09-27 | 2017-02-15 | 淮阴工学院 | Linear optimization auto-disturbance-rejection compound Kalman filter control method of motor synchronous system |
| CN108241292A (en) * | 2017-12-07 | 2018-07-03 | 西北工业大学 | A kind of underwater robot sliding-mode control based on extended state observer |
Non-Patent Citations (7)
| Title |
|---|
| D. WU; XIANKUI WANG; TONG ZHAO; W. LV: "Application of active disturbance rejection to tracking control of a fast tool servo system", 《PROCEEDINGS OF 2005 IEEE CONFERENCE ON CONTROL APPLICATIONS, 2005. CCA 2005.》 * |
| DONGXU ZHU; XIAOBO QIU; KAI WANG; YONGQIANG HOU: "Study on friction compensation for gun control system of tank based on ADRC", 《2011 SECOND INTERNATIONAL CONFERENCE ON MECHANIC AUTOMATION AND CONTROL ENGINEERING》 * |
| HE YAOBIN; YANG ZHIJUN; LI QIAN: "Comparison of performance between PID and LADRC algorithm in linear motion platform", 《2018 19TH INTERNATIONAL CONFERENCE ON ELECTRONIC PACKAGING TECHNOLOGY (ICEPT)》 * |
| 刘振业，刘伟，付明玉，施小成: "基于模糊自适应ADRC的全垫升气垫船航向控制", 《信息与控制》 * |
| 周勇，曾喆昭: "自学习非线性PID抗扰控制原理研究", 《控制工程》 * |
| 张晓燕: "基于扩张状态观测器的制导控制方法研究", 《中国优秀硕士学位论文全文数据库·工程科技Ⅱ辑》 * |
| 高秋华，曾喆昭: "基于ESO的NLPID神经网络控制器的设计", 《电子科技》 * |
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