Research On Intelligent Optimization Of PID Controller Parameters For Cricket System

As an experimental platform for verifying various control algorithms,ball and plate system is a typical nonlinear and coupled control system.This paper mainly aims to improve the control precision and stability of the ball in position control and trajectory tracking control.Based on the research of PID control algorithm and RBF(Radial Basis Function)Neural Network,two control algorithms based on RBF-PID control improvement are proposed.Firstly,the basic structure and working principle of the ball and plate system are introduced.The dynamic analysis of the ball and plate system is completed.The Lagrangian modeling method is used to establish the mathematical model with the angle as the control quantity,and the ball and plate system is linear.Decoupling and decomposing biaxial motion into uniaxial motion in the X and Y directions.After that,aiming at the problem that PID control can not meet the control requirements in the ball and plate system,the PID control and RBF Neural Network are combined to self-tune the PID parameters to improve the control effect of the ball and plate system.RBF-PID control achieves a certain degree of optimization with respect to PID control,but it still cannot achieve high-precision control.Aiming at the problem of poor control precision of RBF-PID control in ball and plate system,the Particle Swarm Optimization(PSO)algorithm is combined with RBF neural network to design controller.Aiming at the problem of poor stability of RBF-PID control in ball and plate system,the genetic algorithm(GA)was introduced and combined with RBF neural network to design controller.The experiment was completed in the MATLAB environment to verify the feasibility of applying the control algorithm to the ball and plate system.Finally,the particle swarm optimization RBF-PID control algorithm and genetic neural adaptive PID control algorithm are applied to the GBP2001 ball and plate system,and a single fixed-point position control experiment and multiple fixed-point position control experiments are completed.The experimental results show that the proposed two improved algorithms based on RBF-PID control are feasible and havegood control effects.Selecting the PID parameters with better results in the position control experiment to complete the square trajectory tracking control experiment in the ball and plate system.The comparison of the two experimental results shows that the RBF-PID control algorithm based on particle swarm optimization is controlled in the ball and plate system.The accuracy is the highest,and the genetic neural adaptive PID control algorithm has the highest control stability in the ball and plate system.