CN117452806A - Course control method of underwater bionic fish robot - Google Patents
Course control method of underwater bionic fish robot Download PDFInfo
- Publication number
- CN117452806A CN117452806A CN202311734407.2A CN202311734407A CN117452806A CN 117452806 A CN117452806 A CN 117452806A CN 202311734407 A CN202311734407 A CN 202311734407A CN 117452806 A CN117452806 A CN 117452806A
- Authority
- CN
- China
- Prior art keywords
- bionic fish
- fish robot
- heading
- algorithm
- course
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 241000251468 Actinopterygii Species 0.000 title claims abstract description 164
- 239000011664 nicotinic acid Substances 0.000 title claims abstract description 157
- 238000000034 method Methods 0.000 title claims abstract description 100
- 241001282110 Pagrus major Species 0.000 claims abstract description 112
- 238000005457 optimization Methods 0.000 claims abstract description 63
- 230000005484 gravity Effects 0.000 claims abstract description 26
- 238000013178 mathematical model Methods 0.000 claims abstract description 25
- 230000007246 mechanism Effects 0.000 claims abstract description 24
- 230000008569 process Effects 0.000 claims description 36
- 230000006870 function Effects 0.000 claims description 19
- 230000006399 behavior Effects 0.000 claims description 13
- 238000011161 development Methods 0.000 claims description 11
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 10
- 230000003592 biomimetic effect Effects 0.000 claims description 9
- 238000013461 design Methods 0.000 claims description 7
- 210000004690 animal fin Anatomy 0.000 claims description 5
- 230000001133 acceleration Effects 0.000 claims description 3
- 238000012546 transfer Methods 0.000 claims description 3
- 230000007704 transition Effects 0.000 claims description 3
- 238000005315 distribution function Methods 0.000 claims 1
- 230000008859 change Effects 0.000 description 5
- 230000000694 effects Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 241000566153 Buteo jamaicensis Species 0.000 description 1
- 239000003795 chemical substances by application Substances 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000011217 control strategy Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 235000001968 nicotinic acid Nutrition 0.000 description 1
- 238000009304 pastoral farming Methods 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000006557 surface reaction Methods 0.000 description 1
- 230000009182 swimming Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B11/00—Automatic controllers
- G05B11/01—Automatic controllers electric
- G05B11/36—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential
- G05B11/42—Automatic controllers electric with provision for obtaining particular characteristics, e.g. proportional, integral, differential for obtaining a characteristic which is both proportional and time-dependent, e.g. P. I., P. I. D.
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Automation & Control Theory (AREA)
- Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
- Feedback Control In General (AREA)
Abstract
The invention provides a course control method of an underwater bionic fish robot, which belongs to the technical field of PID control, and specifically comprises the following steps: s1, converting the course PID control problem of the bionic fish robot into a mathematical model to be optimized; s2, optimizing a mathematical model to be optimized by utilizing an improved red tail optimization algorithm, and specifically comprising the following steps: d1, introducing a 'center disturbance' mechanism, initializing algorithm population, and then improving gravity factors in an algorithm exploration stageFinally, adding a dynamic refraction reverse learning strategy when each iteration of the algorithm is finished, and improving a red-tail optimization algorithm; d2, optimizing a heading PID controller of the bionic fish robot by using an improved red tail optimization algorithm to obtain optimal Kp, ki and Kd heading PID control parameters; s3, applying the optimized bionic fish robot course PID control method to a bionic fish robot course control system to realize the self-adaptive control of the bionic fish robot course.
Description
Technical Field
The invention relates to the technical field of PID control, in particular to a course control method of an underwater bionic fish robot.
Background
The bionic robot fish is an innovative underwater robot which integrates bionics and robot technology, and the appearance and the movement mode of the bionic robot fish imitate fishes in nature. Compared with the traditional underwater vehicle, the bionic robot fish exhibits more excellent maneuverability and adaptability, and benefits from the fact that the bionic robot fish can simulate the efficient swimming mechanism and perception capability of a real fish. However, at present, a plurality of robot fishes mainly adopt steering engines and power motors for control, and have problems such as loud noise, complicated control system design, slow response speed and excessively high energy consumption.
The underwater environment is complex, the water flow turbulence is high, the water level fluctuation is high, the vortex and the obstacle are many, and the conditions have high requirements on the control of the bionic fish robot; the underwater environment is typically nonlinear, whereas a standard PID controller is a linear control method, a PID control system may not provide adequate performance in the face of complex nonlinear water conditions; secondly, in an underwater environment, the bionic fish robot is easily interfered by water flow, vortex and the like, the interference possibly interferes with the performance of a PID control system, and the PID controller is generally fragile and has relatively weak resistance to external interference and parameter change.
Red tail algorithm (Red-tailed hawk algorithm, RTH) as a grazing agent, which simulates hunting behavior of Red tail , with actions taken at each search stage being presented and modeled, includes three stages of wing soaring, low-altitude flying, and bowing. The red-tailed optimization algorithm is strong in global search capability but weak in local search, meaning that the algorithm cannot find the optimal solution quickly, especially if the problem has multiple local optimal solutions. The red tail optimization algorithm requires a significant amount of computing resources. In dealing with large-scale problems, the computational effort of the algorithm may become a bottleneck, resulting in a slower algorithm running speed.
Disclosure of Invention
The invention aims at: in order to solve the problem that the PID control system cannot provide enough performance when facing complex nonlinear water conditions when the bionic fish robot performs tasks underwater and the resistance to external interference and parameter change is relatively weak, the invention solves the defect that the heading PID control system of the bionic fish robot cannot provide enough performance in complex water by improving the method of optimizing the heading PID controller of the bionic fish robot by using the red tail optimization algorithm, and solves the problems that the standard red tail optimization algorithm is easy to sink into local optimization and the optimizing speed is slow, so as to improve the control robustness of the bionic fish underwater robot.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
the course control method of the underwater bionic fish robot comprises a bionic fish robot course control system, wherein the system comprises a bionic fish tail power motor, a bionic fish fin steering engine and a course PID controller; comprising the following steps.
S1, converting the problem of PID control of the heading of the bionic fish robot into a mathematical model to be optimized.
S2, optimizing a mathematical model to be optimized by utilizing an improved red tail optimization algorithm, wherein the method comprises the following steps:
d1, introducing a 'center disturbance' mechanism, initializing algorithm population, and then improving gravity factors in an algorithm exploration stageFinally, adding a dynamic refraction reverse learning strategy when each iteration of the algorithm is finished, and improving a red-tail optimization algorithm;
d2, optimizing a PID controller of the bionic fish robot by using an improved red tail optimization algorithm to obtain optimal Kp, ki and Kd course PID control parameters;
s3, applying the optimized bionic fish robot course PID control method to a bionic fish robot course control system to realize the self-adaptive control of the bionic fish robot course.
Further, in the step S1, the mathematical model to be optimized has the formula:
；
in the method, in the process of the invention,is imitated byThe parameter vector of the course controller of the fish-producing robot,for the number of samples to be taken,is the actual angle value of the course of the bionic fish robot,the heading target angle value of the bionic fish robot is obtained.
Further, the mathematical model of the bionic fish robot course PID control reflects the effect of the bionic fish robot course control through the difference value of the actual course angle of the bionic fish robot and the target course angle value.
Further, in step S2, in step D1, a "central disturbance mechanism" is introduced to initialize the population, specifically: initializing a red tail population in a small range by taking a red tail individual with the current optimal fitness value as a center; the formula of the central disturbance mechanism is as follows:
；
in the method, in the process of the invention,a red tail population initialized for the "central perturbation mechanism",the red tail position that is the best for the current fitness value,is [0,1]Is a random number of (a) and (b),in order to initialize the maximum value of the range,is the initialization range minimum.
Further, the "center perturbation" mechanism initializes redThe tail population is selected from the group consisting of,the step length when the central disturbance mechanism initializes the red tail population is controlled, and the step length design of the central disturbance mechanism can expand the searching range of the optimal solution in the early stage of iteration, thereby providing more opportunities for the optimal solution; in the later iteration stage, the step length is smaller, and the eliminated red-tail individuals can be prevented from being brought into the search range during initialization, so that the integral optimization speed of the algorithm can be improved.
Further, in step S2, D1, the gravity factor of the algorithm exploration stage is improvedSpecifically, in the low-dive attack stage of the red tail , the low-dive attack stage is an algorithm development stage, and an improved self-adaptive gravity factor is introducedWherein, the method comprises the steps of, wherein,the mathematical model is:
；
in the method, in the process of the invention,for the current number of iterations,as a result of the total number of iterations,in order for the attenuation factor to be a factor,the self-adaptive gravity factor of the last iteration;
the attenuation factorThe formula is:
。
further, it should be emphasized that the standard red-tail optimization algorithm is strong in global searching capability, but weak in local searching, which means that the algorithm cannot find the optimal solution quickly; gravity factorControlling the optimizing speed of algorithm development stage and improving self-adaptive gravity factorThe optimization speed of the algorithm development stage can be improved, meanwhile, the attenuation factor can optimize the optimization precision of the algorithm, so that the global search and the local development of the algorithm are better balanced, and the robustness and the control precision of the system can be effectively improved.
Further, in step S2, the PID controller of the bionic fish robot is optimized by using an improved red tail optimization algorithm to obtain optimal Kp, ki and Kd heading PID control parameters, which comprises the following specific steps:
d21, taking a mathematical model to be optimized for the PID control of the heading of the bionic fish robot as an objective function for improving a red-tail optimization algorithm, wherein the objective function is taken as an fitness function;
d22, designing a course control transfer function of the bionic fish robot aiming at disturbance factors of the bionic fish robot under water, wherein a mathematical model formula is as follows:
；
in the method, in the process of the invention,the disturbance gain is controlled for the course of the bionic fish robot, the value is 1.5,is a complex frequency variable;
d23, encoding parameters Kp, ki and Kd of the heading PID controller of the bionic fish robot into a solution of a red tail search space, and along with iteration of an algorithm, obtaining the position of the red tail as the solution of the PID control parameter of the bionic fish robot; the coding vector is:
；
in the method, in the process of the invention,is the kp parameter of a heading PID controller of the bionic fish robot,is the ki parameter of a heading PID controller of the bionic fish robot,kd parameters of a heading PID controller of the bionic fish robot;
d24, initializing and improving a red tail optimization algorithm by using a central disturbance mechanism, wherein the optimization algorithm comprises a population scale N, a problem dimension D and an algorithm search space upper boundub、Algorithm search space lower bound lbMaximum number of iterations T max A red tail population initial position; the position of each individual in the red tail population is a solution group of parameters of a heading PID controller of the bionic fish robot;
d25, calculating individual fitness value of the current iterative red tail population according to the fitness function, recording the minimum fitness value, and recording the minimum fitness value asThe optimal fitness value is compared with the optimal fitness value of the population in the last iteration, and the optimal fitness value is reserved;
d26, simulating predation behaviors of red tail in three stages of high flight, low flight and dive, establishing a population position updating formula of an algorithm in a searching stage and a developing stage, solving an optimal individual position, and determining an optimal solution of parameters of a heading PID controller of the bionic fish robot;
d27, according to the high-flying-stage behavior of the red tail , searching a stage population position updating strategy by a design algorithm, and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（1）；
in the method, in the process of the invention,for red tail at the current iterationThe latest position in the time of the day,is the average value of the positions of the population,for the current best location of the population,as a function of the distribution,as a function of the transition factor(s),is a problem dimension;
d28, designing a population position updating strategy according to the low-flight-stage behavior of the red tail , and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（2）；
；
in the method, in the process of the invention,and (3) withAs the direction coordinate, the direction coordinate is used,for the current iterationStep length of time;
d29, according to the low-dive attack stage behavior of the red tail , introducing a self-adaptive gravity factorDesigning a population position updating strategy in an algorithm exploration stage, and calculating the latest parameter value of a heading PID controller of the bionic fish robot, wherein the formula is as follows:
（3）；
in the method, in the process of the invention,for the acceleration coefficient at the current iteration,for improving the gravity factor under the current iteration, the self-adaptive gravity factor controls the optimizing speed of the algorithm;
d210, introducing a dynamic refraction reverse learning strategy, wherein a better individual is selected from a leader and orthogonal refraction reverse individuals thereof to enter the next iteration;
；
in the method, in the process of the invention,is thatIs arranged in the refraction opposite direction of the lens,is a scaling factor;
d211, if the updated position of the red tail , namely the parameter solution of the heading PID controller of the bionic fish robot, is better than the position updated last time, reserving the current optimal solution;
；
in the method, in the process of the invention,the final red tail optimal position for the current iteration;
d212 current iteration numberThe self-adding step is carried out,judging whether the current iteration number reaches the maximum iteration numberT max If the current value reaches the preset value, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to parameters of a heading PID of the bionic fish robot; otherwise, the process returns to the step d24.
Further, in the fourth step, the PID controller adopts an improved position PID, and the mathematical model formula is:
；
in the method, in the process of the invention,is the output of the heading controller of the bionic fish robot, < ->Difference value of target angle and actual angle of course of bionic fish robot, < ->Respectively proportional, integral and differential coefficients, ki_limit is an integral limit value, and the value is 20 #>Is a filter, is a randomA factor of the time decay.
Further, the bionic fish robot course control system comprises a bionic fish tail power motor for providing power for the bionic fish robot, a bionic fish fin steering engine on two sides of the bionic fish for controlling the course of the bionic fish robot, and a course PID controller comprising a disturbance observer and an improved position PID controller.
Further, the input end of the bionic fish robot course control system is a target course angle input value, the output end is a steering engine, a difference amplifying circuit is adopted to calculate the difference between the actual steering engine angle value and the target course angle input value, a bionic fish robot course angle error signal is generated, the error signal is amplified through a control amplifier circuit to generate a control signal, the steering engine is controlled to rotate, and closed-loop control of the target course angle and the actual course angle of the bionic fish robot is achieved.
Further, the heading PID controller comprises a disturbance observer and an improved position type PID controller, and the disturbance observer is applied to the PID controller and comprises the following steps: firstly, connecting the output of a disturbance observer to the input of a heading PID controller; then, according to the actual condition of the system performance, parameters of a disturbance observer and a heading PID controller are adjusted; and finally, optimizing the disturbance observer and the heading PID controller by using an improved red tail optimization algorithm so as to improve the performance of the heading control system of the bionic fish robot.
According to the invention, firstly, a disturbance observer is combined with a heading controller of a bionic fish underwater robot, and then a red tail optimization algorithm is improved; in an algorithm initialization stage, a 'central disturbance mechanism' is utilized to initialize and improve a red-tail optimization algorithm, so that the optimization complexity of the algorithm is reduced, and the optimization speed of the algorithm is improved; secondly, improving a red tail development stage strategy, introducing a nonlinear composite self-adaptive inertia weight random choice factor, improving the local development capacity of a red tail optimization algorithm, and balancing the overall search speed of the algorithm; the performance of the standard red tail optimization algorithm is improved based on the improvement, the disturbance PID control of the bionic fish underwater robot is further optimized, the robustness of the PID controller is improved, the problem that the resistance to external interference and parameter change is relatively weak when the bionic fish underwater robot faces complex nonlinear water conditions in the background art is solved, and compared with the prior art, the method has the remarkable advantage that the red tail optimization algorithm optimizes the heading PID performance.
Drawings
Fig. 1 is a step diagram of a course control method of an underwater bionic fish robot.
Fig. 2 is a flowchart of an improved red tail optimization algorithm for optimizing the PID controller of the biomimetic fish robot.
Fig. 3 is a graph of the parameter variation of the standard red tail optimization algorithm (RTH) for optimizing the heading PID of the biomimetic fish robot.
Fig. 4 is a graph of the parameter variation of the heading PID of the improved red tail optimization algorithm (IRTH) optimized biomimetic fish robot.
Fig. 5 is a graph of the change in fitness value of the modified red-tail optimization algorithm versus the standard red-tail optimization algorithm.
Fig. 6 is a graph comparing the effects of improving the red-tail optimization algorithm and the standard red-tail optimization algorithm to optimize the course control of the bionic fish robot.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all embodiments of the invention; all other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1-6, the present invention provides a technical solution:
the course control method of the underwater bionic fish robot comprises a bionic fish robot course control system, wherein the system comprises a bionic fish tail power motor, a bionic fish fin steering engine and a course PID controller; as shown in fig. 1, the following steps are included.
S1, converting the problem of PID control of the heading of the bionic fish robot into a mathematical model to be optimized.
S2, optimizing a mathematical model to be optimized by utilizing an improved red tail optimization algorithm, wherein the method comprises the following steps of:
d1, introducing a 'center disturbance' mechanism, initializing algorithm population, and then improving gravity factors in an algorithm exploration stageFinally, adding a dynamic refraction reverse learning strategy when each iteration of the algorithm is finished, and improving a red-tail optimization algorithm;
d2, optimizing a PID controller of the bionic fish robot by using an improved red tail optimization algorithm to obtain optimal Kp, ki and Kd course PID control parameters;
s3, applying the optimized bionic fish robot course PID control method to a bionic fish robot course control system to realize the self-adaptive control of the bionic fish robot course.
Further, in the step S1, the mathematical model to be optimized has the formula:
；
in the method, in the process of the invention,is a parameter vector of a course controller of the bionic fish robot,for the number of samples to be taken,is the actual angle value of the course of the bionic fish robot,the heading target angle value of the bionic fish robot is obtained.
Further, the mathematical model of the bionic fish robot course PID control reflects the effect of the bionic fish robot course control through the difference value of the actual course angle of the bionic fish robot and the target course angle value.
Further, in step S2, in step D1, a "central disturbance mechanism" is introduced to initialize the population, specifically: initializing a red tail population in a small range by taking a red tail individual with the current optimal fitness value as a center; the formula of the central disturbance mechanism is as follows:
；
in the method, in the process of the invention,a red tail population initialized for the "central perturbation mechanism",the red tail position that is the best for the current fitness value,is [0,1]Is a random number of (a) and (b),in order to initialize the maximum value of the range,is the initialization range minimum.
Further, the "central perturbation mechanism" initializes the red tail population,the step length when the central disturbance mechanism initializes the red tail population is controlled, and the step length design of the central disturbance mechanism can expand the searching range of the optimal solution in the early stage of iteration, thereby providing more opportunities for the optimal solution; in the later iteration stage, the step length is smaller, and the eliminated red-tail individuals can be prevented from being brought into the search range during initialization, so that the integral optimization speed of the algorithm can be improved.
Further, in step S2, D1, the gravity factor of the algorithm exploration stage is improvedSpecifically, in the low-dive attack stage of the red tail , the low-dive attack stage is an algorithm development stage, and improved self-adaptive gravity is introducedFactors ofWherein, the method comprises the steps of, wherein,the mathematical model is:
；
in the method, in the process of the invention,for the current number of iterations,as a result of the total number of iterations,in order for the attenuation factor to be a factor,the self-adaptive gravity factor of the last iteration;
the attenuation factorThe formula is:
。
further, it should be emphasized that the standard red-tail optimization algorithm is strong in global searching capability, but weak in local searching, which means that the algorithm cannot find the optimal solution quickly; gravity factorControlling the optimizing speed of algorithm development stage and improving self-adaptive gravity factorThe optimizing speed of the algorithm development stage can be improved, and meanwhile, the attenuation factor can optimize the optimizing precision of the algorithm, so that the global searching and the local development of the algorithm are moreThe balance is good, and the robustness and the control precision of the system can be effectively improved.
Further, in step S2, the PID controller of the bionic fish robot is optimized by using the improved red tail optimization algorithm to obtain the optimal Kp, ki, kd heading PID control parameters, as shown in fig. 2, and the specific steps are as follows:
d21, taking a mathematical model to be optimized for the PID control of the heading of the bionic fish robot as an objective function for improving a red-tail optimization algorithm, wherein the objective function is taken as an fitness function;
d22, designing a course control transfer function of the bionic fish robot aiming at disturbance factors of the bionic fish robot under water, wherein a mathematical model formula is as follows:
；
in the method, in the process of the invention,the disturbance gain is controlled for the course of the bionic fish robot, the value is 1.5,is a complex frequency variable;
d23, encoding parameters Kp, ki and Kd of the heading PID controller of the bionic fish robot into a solution of a red tail search space, and along with iteration of an algorithm, obtaining the position of the red tail as the solution of the PID control parameter of the bionic fish robot; the coding vector is:
；
in the method, in the process of the invention,is the kp parameter of a heading PID controller of the bionic fish robot,is the ki parameter of a heading PID controller of the bionic fish robot,kd parameters of a heading PID controller of the bionic fish robot;
d24, initializing and improving a red tail optimization algorithm by using a central disturbance mechanism, wherein the optimization algorithm comprises a population scale N, a problem dimension D and an algorithm search space upper boundub、Algorithm search space lower bound lbMaximum number of iterations T max A red tail population initial position; the position of each individual in the red tail population is a solution group of parameters of a heading PID controller of the bionic fish robot;
d25, calculating individual fitness value of the current iterative red tail population according to the fitness function, recording the minimum fitness value, and recording the minimum fitness value asThe optimal fitness value is compared with the optimal fitness value of the population in the last iteration, and the optimal fitness value is reserved;
d26, simulating predation behaviors of red tail in three stages of high flight, low flight and dive, establishing a population position updating formula of an algorithm in a searching stage and a developing stage, solving an optimal individual position, and determining an optimal solution of parameters of a heading PID controller of the bionic fish robot;
d27, according to the high-flying-stage behavior of the red tail , searching a stage population position updating strategy by a design algorithm, and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（1）；
in the method, in the process of the invention,for red tail at the current iterationThe latest position in the time of the day,is the average value of the positions of the population,for the current best location of the population,as a function of the distribution,as a function of the transition factor(s),is a problem dimension;
d28, designing a population position updating strategy according to the low-flight-stage behavior of the red tail , and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（2）；
；
in the method, in the process of the invention,and (3) withAs the direction coordinate, the direction coordinate is used,for the current iterationStep length of time;
d29, according to the low-dive attack stage behavior of the red tail , introducing a self-adaptive gravity factorDesigning a population position updating strategy in an algorithm exploration stage, and calculating the latest parameter value of a heading PID controller of the bionic fish robot, wherein the formula is as follows:
（3）；
in the method, in the process of the invention,for the acceleration coefficient at the current iteration,for improving the gravity factor under the current iteration, the self-adaptive gravity factor controls the optimizing speed of the algorithm;
d210, introducing a dynamic refraction reverse learning strategy, wherein a better individual is selected from a leader and orthogonal refraction reverse individuals thereof to enter the next iteration;
；
in the method, in the process of the invention,is thatIs arranged in the refraction opposite direction of the lens,is a scaling factor;
d211, if the updated position of the red tail , namely the parameter solution of the heading PID controller of the bionic fish robot, is better than the position updated last time, reserving the current optimal solution;
；
in the method, in the process of the invention,the final red tail optimal position for the current iteration;
d212 current iteration numberThe self-adding step is carried out,judging whether the current iteration number reaches the maximumNumber of iterationsT max If the current value reaches the preset value, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to parameters of a heading PID of the bionic fish robot; otherwise, the process returns to the step d24.
Further, in the fourth step, the PID controller adopts an improved position PID, and the mathematical model formula is:
；
in the method, in the process of the invention,is the output of the heading controller of the bionic fish robot, < ->Difference value of target angle and actual angle of course of bionic fish robot, < ->Respectively proportional, integral and differential coefficients, ki_limit is an integral limit value, and the value is 20 #>Is a filter, which is a factor that decays over time.
Further, the bionic fish robot course control system comprises a bionic fish tail power motor for providing power for the bionic fish robot, a bionic fish fin steering engine on two sides of the bionic fish for controlling the course of the bionic fish robot, and a course PID controller comprising a disturbance observer and an improved position PID controller.
Further, the input end of the bionic fish robot course control system is a target course angle input value, the output end is a steering engine, a difference amplifying circuit is adopted to calculate the difference between the actual steering engine angle value and the target course angle input value, a bionic fish robot course angle error signal is generated, the error signal is amplified through a control amplifier circuit to generate a control signal, the steering engine is controlled to rotate, and closed-loop control of the target course angle and the actual course angle of the bionic fish robot is achieved.
Further, the heading PID controller comprises a disturbance observer and an improved position type PID controller, and the disturbance observer is applied to the PID controller and comprises the following steps: firstly, connecting the output of a disturbance observer to the input of a heading PID controller; then, according to the actual condition of the system performance, parameters of a disturbance observer and a heading PID controller are adjusted; and finally, optimizing the disturbance observer and the heading PID controller by using an improved red tail optimization algorithm so as to improve the performance of the heading control system of the bionic fish robot.
In order to verify the superiority of the improved red-tail optimization algorithm after optimizing the bionic fish robot course PID controller, the Matlab and Simulink are utilized to simulate the bionic fish robot course control system, the experimental verification of the design method is completed by comparing the Matlab and Simulink with the standard red-tail optimization algorithm, the population scale is set to be N=100, and the maximum iteration number is set=50, the algorithm searches for an upper bound ub=100, and for a lower bound lb=0, the problem dimension dim=3.
Comparing the course PID controller optimizing parameter change diagrams of fig. 3 and fig. 4, it can be found from the diagrams that fig. 4, when the course PID of the bionic fish robot is optimized by improving the red tail optimization algorithm (IRTH), the optimum course PID controller parameter can be found about 20 times of iteration, and when the course PID controller parameter optimizing of the bionic fish robot is optimized by the standard red tail optimization algorithm (RTH), the course PID performance of the bionic fish robot is optimized by the improved red tail optimization algorithm (IRTH) which tends to be stable about 40 times of iteration, and the side surface reaction is better.
FIG. 5 is a graph comparing the improved red tail optimization algorithm optimizing fitness value with the standard red tail optimization algorithm optimizing fitness value, wherein the smaller the fitness value, the better the algorithm performance, the better the parameters representing the PID control of the heading of the bionic fish robot are according to the criterion that the smaller the fitness value is, and the smaller the fitness value of the improved red tail optimization algorithm is compared with the standard red tail optimization algorithm, which shows that the improved red tail optimization algorithm optimizes the heading control parameters of the bionic robot better; and the time for finding the optimal parameter by the improved red-tail optimization algorithm is faster than that of the standard red-tail optimization algorithm, which proves that the sensitivity of the method provided by the invention in course control is stronger.
FIG. 6 shows the heading control effect of the bionic fish underwater robot in different PID control modes, and compared with the different PID control modes in FIG. 4, it is easy to find that the heading control of the bionic fish underwater robot has the minimum overshoot and shorter rise time and adjustment time and faster response speed in the method of optimizing the PID controller by improving the optimization algorithm of the red tail ; in the whole, compared with a control system combining a standard red tail algorithm and a disturbance PID and a common PID control system, the control strategy has better control performance and effect, and can better meet the heading control operation of the bionic fish underwater robot.
Claims (7)
1. The course control method of the underwater bionic fish robot comprises a bionic fish robot course control system, wherein the system comprises a bionic fish tail power motor, a bionic fish fin steering engine and a course PID controller;
the method is characterized in that: the method comprises the following steps:
s1, converting the course PID control problem of the bionic fish robot into a mathematical model to be optimized;
s2, optimizing a mathematical model to be optimized by utilizing an improved red tail optimization algorithm, wherein the method comprises the following steps of:
d1, introducing a 'center disturbance' mechanism, initializing algorithm population, and then improving gravity factors in an algorithm exploration stageFinally, adding a dynamic refraction reverse learning strategy when each iteration of the algorithm is finished, and improving a red-tail optimization algorithm;
d2, optimizing a PID controller of the bionic fish robot by using an improved red tail optimization algorithm to obtain optimal Kp, ki and Kd course PID control parameters;
s3, applying the optimized bionic fish robot course PID control method to a bionic fish robot course control system to realize the self-adaptive control of the bionic fish robot course.
2. The heading control method of the underwater biomimetic fish robot according to claim 1, characterized by: in the step S1, the mathematical model to be optimized has the formula:
；
in the method, in the process of the invention,parameter vector for heading controller of bionic fish robot, <' > for the bionic fish robot>For the number of samples +.>The heading actual angle value of the bionic fish robot is +.>The heading target angle value of the bionic fish robot is obtained.
3. The heading control method of the underwater biomimetic fish robot according to claim 2, characterized by: in step S2, in step D1, a central disturbance mechanism is introduced to initialize the population, specifically: initializing a red tail population in a small range by taking a red tail individual with the current optimal fitness value as a center; the formula of the central disturbance mechanism is as follows:
；
in the method, in the process of the invention,for the red tail population after "central perturbation mechanism" initialization, ++>Position of red tail, best for the current fitness value, +.>Is [0,1]Random number of->For initializing the range maximum, +.>Is the initialization range minimum.
4. A heading control method of an underwater biomimetic fish robot according to claim 3, characterized in that: in the step S2 and the step D1, the gravity factor of the algorithm exploration stage is improvedSpecifically, in the low-dive attack stage of the red tail , the low-dive attack stage is an algorithm development stage, and an improved self-adaptive gravity factor is introduced>Wherein->The mathematical model is:
；
in the method, in the process of the invention,for the current iteration number>For the total number of iterations +.>For attenuation factor->The self-adaptive gravity factor of the last iteration;
the attenuation factorThe formula is:
。
5. the heading control method of the underwater biomimetic fish robot according to claim 4, wherein: the step D2 of the step S2 comprises the following specific steps:
d21, taking a mathematical model to be optimized for the PID control of the heading of the bionic fish robot as an objective function for improving a red-tail optimization algorithm, wherein the objective function is taken as an fitness function;
d22, designing a course control transfer function of the bionic fish robot aiming at disturbance factors of the bionic fish robot under water, wherein a mathematical model formula is as follows:
；
in the method, in the process of the invention,the disturbance gain is controlled for the course of the bionic fish robot, and the value is 1.5 and the value of the disturbance gain is 1.5>Is a complex frequency variable;
d23, encoding parameters Kp, ki and Kd of the heading PID controller of the bionic fish robot into a solution of a red tail search space, and along with iteration of an algorithm, obtaining the position of the red tail as the solution of the PID control parameter of the bionic fish robot; the coding vector is:
；
in the method, in the process of the invention,kp parameter of PID controller for heading of bionic fish robot, < ->Ki parameter of PID controller for bionic fish robot course>Kd parameters of a heading PID controller of the bionic fish robot;
d24, initializing and improving a red tail optimization algorithm by using a central disturbance mechanism, wherein the optimization algorithm comprises a population scale N, a problem dimension D and an algorithm search space upper boundub、Algorithm search space lower bound lbMaximum number of iterations T max A red tail population initial position; the position of each individual in the red tail population is a solution group of parameters of a heading PID controller of the bionic fish robot;
d25, calculating individual fitness value of the current iterative red tail population according to the fitness function, recording the minimum fitness value, and recording the minimum fitness value asThe optimal fitness value is compared with the optimal fitness value of the population in the last iteration, and the optimal fitness value is reserved;
d26, simulating predation behaviors of red tail in three stages of high flight, low flight and dive, establishing a population position updating formula of an algorithm in a searching stage and a developing stage, solving an optimal individual position, and determining an optimal solution of parameters of a heading PID controller of the bionic fish robot;
d27, according to the high-flying-stage behavior of the red tail , searching a stage population position updating strategy by a design algorithm, and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（1）；
in the method, in the process of the invention,for red tail at the current iteration +.>The latest position in time->Is the average value of the positions of the population,for the current best position of the population, < > is>For distribution function +.>As a transition factor function->Is a problem dimension;
d28, designing a population position updating strategy according to the low-flight-stage behavior of the red tail , and calculating the latest parameter value of the heading PID controller of the bionic fish robot, wherein the formula is as follows:
（2）；
；
in the method, in the process of the invention,and->For the direction coordinates +.>For the current iteration->Step length of time;
d29, according to the low-dive attack stage behavior of the red tail , introducing a self-adaptive gravity factorDesigning a population position updating strategy in an algorithm exploration stage, and calculating the latest parameter value of a heading PID controller of the bionic fish robot, wherein the formula is as follows:
（3）；
in the method, in the process of the invention,for the acceleration coefficient at the current iteration, +.>For improving the gravity factor under the current iteration, the self-adaptive gravity factor controls the optimizing speed of the algorithm;
d210, introducing a dynamic refraction reverse learning strategy, wherein a better individual is selected from a leader and orthogonal refraction reverse individuals thereof to enter the next iteration;
；
in the method, in the process of the invention,is->Is opposite to the refraction of>Is a scaling factor;
d211, if the updated position of the red tail , namely the parameter solution of the heading PID controller of the bionic fish robot, is better than the position updated last time, reserving the current optimal solution;
；
in the method, in the process of the invention,the final red tail optimal position for the current iteration;
d212 current iteration numberSelf-adding (adding) of (removing) the root>Judging whether the current iteration number reaches the maximum iteration numberT max If the current value reaches the preset value, exiting the cycle, outputting a global optimal solution, and distributing the global optimal solution to parameters of a heading PID of the bionic fish robot; otherwise, the process returns to the step d24.
6. The heading control method of the underwater biomimetic fish robot according to claim 5, characterized in that: in the step d21, the heading PID control of the bionic fish robot adopts an improved position PID, and the mathematical model formula is as follows:
；
in the method, in the process of the invention,is a bionic fishOutput of heading controller of robot, +.>Difference value of target angle and actual angle of course of bionic fish robot, < ->Respectively proportional, integral and differential coefficients, ki_limit is an integral limit value, and the value is 20 #>Is a filter, which is a factor that decays over time.
7. The heading control method of the underwater biomimetic fish robot according to any one of claims 1 to 6, characterized in that: the bionic fish tail power motor provides power for the bionic fish robot, the steering engine of the fish fins on two sides of the bionic fish controls the course of the bionic fish robot, and the course PID controller comprises a disturbance observer and an improved position PID controller.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311734407.2A CN117452806B (en) | 2023-12-18 | 2023-12-18 | Course control method of underwater bionic fish robot |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311734407.2A CN117452806B (en) | 2023-12-18 | 2023-12-18 | Course control method of underwater bionic fish robot |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117452806A true CN117452806A (en) | 2024-01-26 |
| CN117452806B CN117452806B (en) | 2024-03-19 |
Family
ID=89595092
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311734407.2A Active CN117452806B (en) | 2023-12-18 | 2023-12-18 | Course control method of underwater bionic fish robot |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117452806B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117850213A (en) * | 2024-03-08 | 2024-04-09 | 岳正检测认证技术有限公司 | Enhanced PID control optimization method for killing robot |
| CN118170003A (en) * | 2024-05-14 | 2024-06-11 | 济南大学 | PID parameter optimization method based on improved corner lizard optimization algorithm |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101435739A (en) * | 2007-11-15 | 2009-05-20 | 中国科学院自动化研究所 | Bionic long fin fluctuating propulsion experimental apparatus |
| CN101698301A (en) * | 2009-10-16 | 2010-04-28 | 北京工业大学 | Multi-planar-degree-of-freedom robot |
| CN102114450A (en) * | 2011-03-04 | 2011-07-06 | 上海市古美学校 | Automatic spraying robot for elevated roads and bridges and control method thereof |
| CN102637036A (en) * | 2012-05-08 | 2012-08-15 | 北京理工大学 | Combined type bionic quadruped robot controller |
| CN110909859A (en) * | 2019-11-29 | 2020-03-24 | 中国科学院自动化研究所 | Bionic robot fish motion control method and system based on antagonistic structured control |
| CN112000116A (en) * | 2020-07-24 | 2020-11-27 | 西北工业大学 | Heading angle control method of autonomous underwater vehicle based on improved firefly PID method |
| CN112637807A (en) * | 2020-12-24 | 2021-04-09 | 河南大学 | Sensor selection method based on disturbance and environment, distance and energy constraints |
| CN115437389A (en) * | 2022-08-12 | 2022-12-06 | 广州大学 | Underwater robot based on sea flatworm bionics and anti-interference control method |
| CN115599108A (en) * | 2022-11-03 | 2023-01-13 | 威海海洋职业学院（Cn） | Bionic underwater robot control method based on carangidae model |
| CN116400585A (en) * | 2023-06-06 | 2023-07-07 | 济南大学 | Marine rescue flying wing control optimization method based on improved rime optimization algorithm |
| CN116449687A (en) * | 2023-06-16 | 2023-07-18 | 济南大学 | Improved-type marine rescue flying wing disturbance observation PID control method |
| CN116841303A (en) * | 2023-03-29 | 2023-10-03 | 紫光云技术有限公司 | Intelligent preferential high-order iterative self-learning control method for underwater robot |
-
2023
- 2023-12-18 CN CN202311734407.2A patent/CN117452806B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101435739A (en) * | 2007-11-15 | 2009-05-20 | 中国科学院自动化研究所 | Bionic long fin fluctuating propulsion experimental apparatus |
| CN101698301A (en) * | 2009-10-16 | 2010-04-28 | 北京工业大学 | Multi-planar-degree-of-freedom robot |
| CN102114450A (en) * | 2011-03-04 | 2011-07-06 | 上海市古美学校 | Automatic spraying robot for elevated roads and bridges and control method thereof |
| CN102637036A (en) * | 2012-05-08 | 2012-08-15 | 北京理工大学 | Combined type bionic quadruped robot controller |
| CN110909859A (en) * | 2019-11-29 | 2020-03-24 | 中国科学院自动化研究所 | Bionic robot fish motion control method and system based on antagonistic structured control |
| CN112000116A (en) * | 2020-07-24 | 2020-11-27 | 西北工业大学 | Heading angle control method of autonomous underwater vehicle based on improved firefly PID method |
| CN112637807A (en) * | 2020-12-24 | 2021-04-09 | 河南大学 | Sensor selection method based on disturbance and environment, distance and energy constraints |
| CN115437389A (en) * | 2022-08-12 | 2022-12-06 | 广州大学 | Underwater robot based on sea flatworm bionics and anti-interference control method |
| CN115599108A (en) * | 2022-11-03 | 2023-01-13 | 威海海洋职业学院（Cn） | Bionic underwater robot control method based on carangidae model |
| CN116841303A (en) * | 2023-03-29 | 2023-10-03 | 紫光云技术有限公司 | Intelligent preferential high-order iterative self-learning control method for underwater robot |
| CN116400585A (en) * | 2023-06-06 | 2023-07-07 | 济南大学 | Marine rescue flying wing control optimization method based on improved rime optimization algorithm |
| CN116449687A (en) * | 2023-06-16 | 2023-07-18 | 济南大学 | Improved-type marine rescue flying wing disturbance observation PID control method |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117850213A (en) * | 2024-03-08 | 2024-04-09 | 岳正检测认证技术有限公司 | Enhanced PID control optimization method for killing robot |
| CN117850213B (en) * | 2024-03-08 | 2024-05-24 | 羽禾人工智能(嘉兴)有限公司 | Enhanced PID control optimization method for killing robot |
| CN118170003A (en) * | 2024-05-14 | 2024-06-11 | 济南大学 | PID parameter optimization method based on improved corner lizard optimization algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117452806B (en) | 2024-03-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117452806B (en) | Course control method of underwater bionic fish robot | |
| Su et al. | Attitude control of underwater glider combined reinforcement learning with active disturbance rejection control | |
| CN110806756B (en) | Unmanned aerial vehicle autonomous guidance control method based on DDPG | |
| CN105425612B (en) | A kind of method for optimizing of Adaptive System of Water-Turbine Engine control parameter | |
| CN116449687B (en) | Improved-type marine rescue flying wing disturbance observation PID control method | |
| CN110377034B (en) | Global robust sliding mode control method for track tracking of surface ship based on dragonfly algorithm optimization | |
| CN110552961A (en) | Active magnetic bearing control method based on fractional order model | |
| CN111273544B (en) | Radar pitching motion control method based on prediction RBF feedforward compensation type fuzzy PID | |
| CN117369244A (en) | Welding gun position control optimization method based on welding robot | |
| Mahesh et al. | Control law synthesis for flutter suppression using linear quadratic Gaussian theory | |
| CN113703319A (en) | Joint module inequality constraint optimal robust control method based on reinforcement learning | |
| CN113093526A (en) | Overshoot-free PID controller parameter setting method based on reinforcement learning | |
| CN115329459A (en) | Underwater vehicle modeling method and system based on digital twinning | |
| Fan et al. | Distributed finite-time adaptive consensus tracking control for multiple AUVs with state constraints | |
| Hasan et al. | Controller design for underwater robotic vehicle based on improved whale optimization algorithm | |
| CN115373415A (en) | Unmanned aerial vehicle intelligent navigation method based on deep reinforcement learning | |
| CN113419424B (en) | Modeling reinforcement learning robot control method and system for reducing overestimation | |
| CN114637191A (en) | PID controller parameter setting method, system, device and storage medium | |
| CN115248591A (en) | UUV path planning method based on hybrid initialization Hui wolf particle swarm algorithm | |
| CN115655279A (en) | Marine unmanned rescue airship path planning method based on improved whale algorithm | |
| CN116100539A (en) | Mechanical arm autonomous dynamic obstacle avoidance method and system based on deep reinforcement learning | |
| CN115303455A (en) | Underwater bionic robot motion control method, device, equipment and storage medium | |
| CN112904719B (en) | Annular area tracking control method suitable for underwater robot position | |
| CN110703792B (en) | Underwater robot attitude control method based on reinforcement learning | |
| CN113848982A (en) | Method for planning and tracking control of perching and stopping moving track of quad-rotor unmanned aerial vehicle |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |