Networked Control System Based on PSO-RBF Neural Network Time-Delay Prediction Model

Abstract

To satisfy the requirement of real-time and accurate control of the system, a time-delay prediction control system based on the PSO-RBF neural network model is established to solve the effect of time delay on the control system’s performance. Firstly, a network control model with a time delay is established to predict the control system’s output to solve the uncertainty of the output time delay. Secondly, an improved offline prediction model of RBF networks is proposed to solve the problem of the low accuracy of time-delay prediction in PSO-RBF networks. To solve the problem that the PSO algorithm is prone to fall into local optimality, a nonlinear adjustment formula for the parameters of the PSO algorithm based on the number of iterations is proposed, and the TS algorithm is used to make the optimal global solution. Finally, in order to compensate for the problem of time delay, an online RBF network prediction controller is designed, the parameters of the online RBF network are adjusted by the gradient descent method, and a target function with the differential component is proposed to evaluate the optimization effect of the rolling optimization stage. The results from the true-time simulation platform show that the delay prediction control system based on the PSO-RBF network model proposed in this paper improves the IAE by 59.9% and 31.7%, respectively, compared to the traditional PID controller and fuzzy PID control under the influence of uncertainty disturbances. Therefore, the time-delay prediction control system proposed in this paper has good control capability for the time-delay compensation problem and system output.