Optimizing PID tuning parameters using grey prediction algorithm

Abstract

This paper considered a new way to tune the PID controller parameters using the optimization method and grey prediction algorithm. The grey prediction algorithm has the ability to predict the output or the error of the system depending on a small amount of data. In this paper the grey prediction algorithm is used to predict and estimate the errors of the system for a defined period of time and then the average of the estimated error is calculated. A mat lab program is developed using simulink to find the average of the estimated error for the system whose process is modeled in first order lag plus dead time (FOLPD) form. In the other hand optimization method with mat lab software program was used to find the optimum value for the PID controller gain (Kc (opt)) which minimizes specific performance criteria (ITAE performance criteria). The main goal of the optimization method is to achieve most of the systems requirements such as reducing the overshoot, maintaining a high system response, achieving a good load disturbances rejection and maintaining robustness. Those two parameters (the average of the estimated error and the PID controller gain (Kc (opt))) were used to calculate the PID controller parameters (gain of the controller (Kc), integral time (Ti) and the derivative time (Td)). Simulations for the proposed algorithm had been done for different process models. A comparison between the proposed tuning rule and a well performance tuning rule is done through the Matlab software to show the efficiency of the new tuning rule.