CN112230545A - AUV course angle control method based on PPGA adaptive optimization PID parameter - Google Patents

AUV course angle control method based on PPGA adaptive optimization PID parameter Download PDF

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CN112230545A
CN112230545A CN202011092449.7A CN202011092449A CN112230545A CN 112230545 A CN112230545 A CN 112230545A CN 202011092449 A CN202011092449 A CN 202011092449A CN 112230545 A CN112230545 A CN 112230545A
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population
auv
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pid
fitness
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刘明雍
赵格睿
崔海英
牛云
向举苗
游粮根
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Northwestern Polytechnical University
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Abstract

The invention provides an AUV course angle control method based on PPGA (pentatricopeptide genetic algorithm) adaptive optimization PID (proportion integration differentiation) parameters, aiming at the problems of low response speed, uncertain parameters and easy generation of premature phenomenon and low convergence speed in the traditional PID method and the problems of optimization of the traditional genetic algorithm, and the three parameters of the optimal PID in a control system are searched through a pseudo-parallel genetic algorithm so as to realize AUV course angle motion control. The algorithm has good global optimization capability, and can find out the optimal solution in a feasible domain through genetic optimization to obtain an optimal control scheme, so that the efficiency and the precision of the controller are greatly improved.

Description

AUV course angle control method based on PPGA adaptive optimization PID parameter
Technical Field
The invention relates to an AUV course angle control method based on PPGA adaptive optimization PID parameters, and belongs to the field of AUV control.
Background
Autonomous underwater robots (AUVs) have received much attention in recent decades and have achieved a number of related results. An Autonomous Underwater Vehicle (AUV) is an Autonomous Underwater Vehicle (AUV) which manages and controls the AUV to complete a predetermined task by relying on the autonomous ability of the AUV and can be used in the fields of marine scientific investigation, port security monitoring, underwater search and rescue, naval application deployment and the like. The motion control technology is one of the key technologies of the underwater robot, and the good motion control technology is the premise and guarantee that the underwater robot can complete specific tasks. With the expansion of the application range of the underwater robot, the requirements for the autonomy, the precision and the stability of the motion control are gradually increased, so how to improve the control performance of the underwater robot is an important subject of current research.
The currently mainly adopted AUV motion control technologies are as follows: PID control, fuzzy control, genetic algorithm optimization PID control and the like, and the traditional PID control algorithm is the most widely applied control algorithm at present, but has the defects of slow response, easy overshoot, poor anti-interference capability and the like. The selection of a plurality of fuzzy variables and membership functions of the fuzzy control needs expert experience knowledge with good effect which is verified by practice to guide the design, and the fuzzy variables of a new design can be utilized without experience at all; the self-adapting process of the neural network needs time, and particularly when the amplitude and the period of external interference are close to the motion amplitude and the period of the underwater robot, the learning of the neural network generates a hysteresis phenomenon, so that the control generates oscillation. In view of the above problems, research on adaptive stability and fast control of AUV has become an important part of AUV motion control research.
Disclosure of Invention
The invention aims to overcome the defects that the traditional control algorithm is slow in response, the PID parameters are uncertain, and the traditional genetic algorithm is easy to generate prematurity and slow in operation speed, and provides an AUV course angle control method for adaptively optimizing the PID parameters based on a pseudo-parallel genetic algorithm (PPGA). The algorithm has good global optimization capability, and can find out the optimal solution in a feasible domain through genetic optimization to obtain an optimal control scheme, so that the efficiency and the precision of the controller are greatly improved.
The technical scheme of the invention is as follows:
the AUV course angle control method based on the PPGA adaptive optimization PID parameter comprises the following steps:
step 1: respectively representing AUV course angle PID control parameters Kp, Ki and Kd by adopting L-bit binary coding; taking PID control parameters represented by binary codes as individuals, randomly forming an initialization population P, and dividing the population P into M sub-populations
Step 2: for each individual in the population, the individual is used as a PID controller parameter to carry out course angle control of the AUV, and the fitness f is calculated according to a control result;
and step 3: after the fitness value of each individual is obtained through calculation, judging whether iteration termination is achieved, if so, terminating iteration, taking the individual with the highest fitness value in the current population as an optimal solution, and taking the optimal solution as a PID controller parameter to carry out course angle control of the AUV, otherwise, entering the next step;
and 4, step 4: and (3) independently carrying out selection, crossing and mutation genetic operations on each sub-population:
and 5: calculating the fitness of the individuals in each sub-population subjected to selection, crossing and variant genetic operations to find out the individual with the best fitness, injecting the individual into each sub-population, and replacing the worst individual in each sub-population to form a next generation population; and then returns to step 2.
Further, in step 2, the following process is adopted to calculate the fitness value f of the individual:
Figure BDA0002722598550000021
wherein ITAE ═ e | tdt, and the error value e is the deviation between the actual heading angle and the expected heading angle obtained when the corresponding individual is used as a PID controller parameter to perform heading angle control of the AUV.
Further, in step 4,
the selection operation adopts a roulette method;
the self-adaptive cross probability operator selected by the cross operation is as follows:
Figure BDA0002722598550000022
wherein f ist iFor the greater fitness value of the two cross-paired individuals, ft，min、ft，maxIs the maximum and minimum value of fitness in the sub-population, kcIs a set constant;
Figure BDA0002722598550000031
wherein, delta represents the diversity degree of the t generation population; dtAs the fitness variance of the population, DmaxMaximum fitness variance from the 1 st generation population to the t generation population;
the mutation operation selects an adaptive mutation probability operator as follows:
Figure BDA0002722598550000032
wherein k ismTo set a constant.
Advantageous effects
The invention provides an AUV course angle control method based on PPGA (pentatricopeptide genetic algorithm) adaptive optimization PID (proportion integration differentiation) parameters, aiming at the problems of low response speed, uncertain parameters and easy generation of premature phenomenon and low convergence speed in the traditional PID method and the traditional genetic algorithm optimization, the invention provides the method for optimizing by using a pseudo-parallel genetic algorithm, thereby improving the accuracy and the rapidity of AUV course angle control.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1: a PPGA adaptive optimization PID control algorithm flow chart;
FIG. 2: AUV course control schematic diagram;
FIG. 3: traditional PID course control simulation diagram;
FIG. 4: a course control simulation diagram optimized by a traditional genetic algorithm;
FIG. 5: and (3) a course angle simulation diagram based on PPGA optimization.
Detailed Description
The following detailed description of embodiments of the invention is intended to be illustrative, and not to be construed as limiting the invention.
The flow chart of the AUV course angle control method based on the Pseudo Parallel Genetic Algorithm (PPGA) self-adaptive optimization PID parameter is shown in figure 1, and the specific steps of utilizing the algorithm to realize the AUV course angle control are as follows:
(1) binary coding is adopted, PID parameters Kp, Ki and Kd are respectively expressed by three binary numbers with the length of 17 bits, the total population is set as 100, and the initialized population P is formed randomly.
(2) The population P is divided into M sub-populations, and this example is divided into five sub-populations, each of which has 20 individuals.
(3) A desired value Q is set, and in the course angle control of the AUV, the desired course angle is set to 30 °, that is, Q is 30. The difference value between the course angle and the expected course angle is the error value e of the current moment, and the error is differentiated to obtain the error change rate e of the current momentr。
(4) According to the error value e and the error change rate erThe rudder angle can be input into an AUV model to control the course angle in the simulation process.
(5) Calculating ITAE performance index
ITAE＝∫|e|tdt (1)
The performance of the PID controller can be evaluated, the performance represents the accumulation of errors in the control process, and the smaller the value, the better the control performance.
(6) And calculating the fitness of the current individual, wherein the fitness function is selected as follows:
Figure BDA0002722598550000041
(7) and repeating the third step to the sixth step until each individual in the sub-population is calculated once to obtain the fitness value of each individual. Judging a termination condition, wherein the termination condition of the embodiment is set to iterate for 50 times, namely when the population reaches 50 generations, the inheritance is terminated and the individual with the highest fitness value in the current population is taken as an optimal solution; if the termination condition is not satisfied, step 8 is performed.
(8) Each sub-population independently performs genetic operations such as selection, crossing, mutation and the like.
The selection operation adopts a roulette method;
the crossover operation selects an adaptive crossover probability operator as:
Figure BDA0002722598550000051
wherein f ist iFor the greater fitness value of the two cross-paired individuals, ft，min、ft，maxAs the maximum and minimum of sub-population fitness, kcIs constant, k is taken in this examplec＝1；
Figure BDA0002722598550000052
Wherein, delta is an index and represents the diversity degree of the t generation population; dtAs the fitness variance of the population, DmaxMaximum fitness variance from the population of generation 1 to the population of generation t.
The mutation operation selects an adaptive mutation probability operator as follows:
Figure BDA0002722598550000053
wherein L isLength of chromosome, kmIs a constant, take km＝3。
(9) And (3) carrying out fitness calculation on the individuals in each sub-population subjected to selection, crossing and variant genetic operations, finding out the individual with the best fitness, injecting the individual into each sub-population, and replacing the worst individual in each sub-population to form a next generation population. And repeating the second step to the eighth step, and performing loop iteration until a termination condition is met.
And finally, the optimal solution obtained through PPGA optimization is the solution with the best PID control effect.
In the embodiment, the expected course angle is set to be 30 degrees, and the simulation is respectively carried out by using the traditional PID control, the traditional genetic algorithm optimized PID control and the PPGA self-adaptive optimized PID control. As can be seen from the simulation results shown in fig. 3 to 5, the control result obtained by the PID control based on the PPGA adaptive optimization has a better effect than the former two methods, and an optimal control effect can be obtained.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention.

Claims (3)

1. An AUV course angle control method based on PPGA adaptive optimization PID parameters is characterized by comprising the following steps: the method comprises the following steps:
step 1: respectively representing AUV course angle PID control parameters Kp, Ki and Kd by adopting L-bit binary coding; taking PID control parameters represented by binary codes as individuals, randomly forming an initialization population P, and dividing the population P into M sub-populations
Step 2: for each individual in the population, the individual is used as a PID controller parameter to carry out course angle control of the AUV, and the fitness f is calculated according to a control result;
and step 3: after the fitness value of each individual is obtained through calculation, judging whether iteration termination is achieved, if so, terminating iteration, taking the individual with the highest fitness value in the current population as an optimal solution, and taking the optimal solution as a PID controller parameter to carry out course angle control of the AUV, otherwise, entering the next step;
and 4, step 4: and (3) independently carrying out selection, crossing and mutation genetic operations on each sub-population:
and 5: calculating the fitness of the individuals in each sub-population subjected to selection, crossing and variant genetic operations to find out the individual with the best fitness, injecting the individual into each sub-population, and replacing the worst individual in each sub-population to form a next generation population; and then returns to step 2.
2. The AUV course angle control method based on PPGA adaptive optimization PID parameter of claim 1, characterized in that: in the step 2, the fitness value f of the individual is calculated by adopting the following process:
Figure FDA0002722598540000011
wherein ITAE ═ e | tdt, and the error value e is the deviation between the actual heading angle and the expected heading angle obtained when the corresponding individual is used as a PID controller parameter to perform heading angle control of the AUV.
3. The AUV course angle control method based on PPGA adaptive optimization PID parameter of claim 1, characterized in that: in the step 4, the process of the method,
the selection operation adopts a roulette method;
the self-adaptive cross probability operator selected by the cross operation is as follows:
Figure FDA0002722598540000012
wherein f ist iFor the greater fitness value of the two cross-paired individuals, ft,min、ft,maxIs the maximum and minimum value of fitness in the sub-population, kcIs a set constant;
Figure FDA0002722598540000021
wherein, delta represents the diversity degree of the t generation population; dtAs the fitness variance of the population, DmaxMaximum fitness variance from the 1 st generation population to the t generation population;
the mutation operation selects an adaptive mutation probability operator as follows:
Figure FDA0002722598540000022
wherein k ismTo set a constant.
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CN112859891A (en) * 2021-01-29 2021-05-28 西北工业大学 AUV course angle control method for optimizing self-adaptive sliding mode control parameters based on particle swarm optimization
CN113485106A (en) * 2021-07-07 2021-10-08 山西大学 Method for controlling concentration of nitrogen oxide in thermal power plant

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CN106094843A (en) * 2016-08-02 2016-11-09 哈尔滨工程大学 A kind of adaptive fuzzy submarine navigation device control method using genetic algorithm optimizing
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112859891A (en) * 2021-01-29 2021-05-28 西北工业大学 AUV course angle control method for optimizing self-adaptive sliding mode control parameters based on particle swarm optimization
CN112859891B (en) * 2021-01-29 2023-03-03 西北工业大学 AUV course angle control method for optimizing self-adaptive sliding mode control parameters based on particle swarm optimization
CN113485106A (en) * 2021-07-07 2021-10-08 山西大学 Method for controlling concentration of nitrogen oxide in thermal power plant

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Application publication date: 20210115