Research On Intelligent Control And Optimization Algorithm Based On Biological Regulation Mechanism

As the process flow of modern industrial production is more and more complex and people's increasing demands of control objectives,the traditional control technology has been unable to meet people's requirements.Therefore,we need to study more intelligent and practical intelligent control technology.In this paper,there are intelligent controllers designed with biological physiological regulation mechanism based on the basic intelligent control algorithm,and the parameters of two intelligent controllers are optimized by improved clonal selection optimization algorithm.(1)Self-tuning fuzzy controller design based on immune regulation mechanism.Based on the analysis of the defects of traditional fuzzy controllers,the principle of T-cell regulation in the immune system is used to adjust the parameters of fuzzy controller,so that it can be adaptively changed during the control process;the antigen presentation mechanism in the immune system is used to perform non-linear processing on control deviations to enhance the controller's sensitivity and improve the steady-state accuracy of the control system.The designed controller is compared with the traditional PID controller and fuzzy controller.The simulation results prove the superiority of the controller's control performance.(2)Design of bio-intelligent controller inspired by blood glucose regulation mechanism.On the basis of analyzing the regulation mechanism of blood glucose concentration,the enhancement and suppression unit of BP neural network output based on conjugate gradient method is designed to improve the dynamic performance of the controller;the steady state control unit algorithm with integral action is designed to ensure the stable operation of the control system;the fuzzy cooperative control unit is designed to coordinate the working time of each unit of the controller.The designed controller is compared with the traditional PID controller and the BP neural network controller.The simulation experiment proves the superiority of the controller's control performance.(3)Research and improvement of immune clonal selection algorithm.On the basis of studying the basic immune clonal selection algorithm,the existing defects are improved.The initial antibody population of the clonal selection algorithm was generated by the chaotic sequence;the concept of antibody survival was introduced into the clone operator,and the influence factor weight of the antibody survival was adaptively adjusted by using the hormone secretion rules;the adaptive mutation probability was introduced into the immune mutation operator;improving the convergence speed and precision of the algorithm.The performance of the improved algorithm is verified on typical test functions.(4)Optimize intelligent controller parameters.The improved clonal selection algorithm was used to optimize the parameters of the two intelligent controllers and applied them in the same bioreactor temperature control system.The control performance of the two intelligent controllers was compared and analyzed based on the simulation results.Based on the traditional fuzzy algorithm and BP neural network algorithm,this paper designs two intelligent controllers combined with biological regulation mechanism.In addition,this paper improves the immune clonal selection algorithm and it was used in the parameter optimization of intelligent controllers.The simulation results of temperature control in bioreactor verify that the controller has better control performance,and provides a new way for the study of the fusion of biological mechanism and intelligent control.