A Study of the Prediction of Parameter Optimization of a PID Controller Based on Chaotic Theory

Realistic industrial processes frequently suffer from an external interference,and tend to assume slow and time-variation features,making it difficult for a traditional PID (proportional-integral-differential) controller to timely track the changes of a system and overcome noise interference.The authors have studied the application of chaos-based optimization theory in various control processes.By the combined use of a prediction control and PID controller in the control of a reheat steam temperature system and with a neural network serving as a system prediction model,an on-line optimization has been performed of PID parameters through a chaos-based optimization algorithm.A computer-based simulation test has verified the effectiveness of the algorithm.Compared with the traditional PID control,the PID prediction control based on chaotic theory is capable of timely tracking system changes and overcoming any outside perturbations.As a result,achieved is a good control effectiveness and the exhibition of a very strong robustness performance.