Research On Fuzzy PID Control Strategy Based On Improved BBO Algorithm

Because of its simple control principle and easy operation,PID control is suitable for most industrial control sites with low control requirements.However,it is difficult to satisfy the nonlinear and time-varying complex control systems,and the parameters cannot be self-tuned.The fuzzy control can measure and identify the complicated control situation,and then make decisions through knowledge reasoning to realize the control of the controlled object.It is very suitable for the control system that can not obtain the accurate function model such as nonlinear and time-varying.According to the characteristics of the two,researchers often combine fuzzy control and PID control to form a fuzzy PID control to achieve self-tuning of PID parameters.However,as membership functions and fuzzy control rule tables in fuzzy control rely only on expert knowledge and on-site personnel experience,it is impossible to avoid deviations caused by human factors,resulting in unsatisfactory control results.Biogeography-Based Optimization(BBO)is an intelligent evolutionary algorithm proposed by Dan Simon to solve optimization problems.The algorithm has the characteristics of novel mechanism,simple structure and good convergence.This paper studies the BBO algorithm and combines it with fuzzy PID,which is used to self-optimize parameters such as membership function and fuzzy rules in fuzzy control to avoid artificially affecting the controller and improving the control performance of the controller.The main contents of this article are:1)Introduced the research status of PID control and fuzzy control,analyzed the advantages and disadvantages of fuzzy PID controller,and gave detailed design steps of fuzzy PID controller.2)Introduced a novel intelligent evolution algorithm-biogeographic optimization algorithm,and used this algorithm to optimize the parameter optimization of fuzzy PID controller membership function and fuzzy rules.Through MATLAB simulation platform,using BBO algorithm to optimize fuzzy PID controller parameters,and compared with conventional fuzzy PID controller and conventional PID controller simulation experiments,the results show that the control effect is more effective.3)For the insufficiency of the slow convergence rate of the BBO algorithm in the later period,the cloud model theory is used to improve the most important migration model in the migration operation of the algorithm,and then the improved BBO algorithm is compared with the original BBO algorithm and the genetic algorithm(GA).Particle swarm optimization(PSO)performs six benchmark function test comparisons.Finally,these four algorithms are used to optimize the parameters of the fuzzy PID controller holding system and compare the simulation experiments.The results show that the improved biogeographic optimization fuzzy PID controller has better simulation results.4)The improved BBO algorithm is used in representative servo control fields in industrial production.The AC servo control system model was built by MATLAB/Simulink,and then the fuzzy PID controller based on the improved BBO algorithm was used to simulate the experiment and compared with the conventional fuzzy PID controller.The results show that the former is used for servo system speed control to make the system have better stability and anti-jamming capability.