Speed Control for DC Motor Drive based on Fuzzy and Genetic PI Controller - A Comparative Study

摘要

A Proportional-Integral (P-I) controller is a generic control loop feedback mechanism, which is widely used in industrial control systems due to that its structure is simplex and it can almost ensure zero error in steady-state when its gains are properly tuned. A PI controller calculates an 'error' value as the difference between the desired set point and the measured output process variable. The conventional PI controller has fixed gains and is based on the mathematical model of the system being controlled. Moreover, a disadvantage of the conventional PI controller is that for optimum output quality and performance (overshoot, rise time and settling time), the controller gains must be tuned according to the nature of the system. Unfortunately, it has been quite difficult to tune the gains properly of PI controllers because many industrial plants are often burdened with problems such as high order, time delays and nonlinearities [1]. An alternative method for the speed control of a DC motor is based on fuzzy logic controller (FLC). While conventional controllers depend on the accuracy of the system model and parameters, FLCs use a different approach. Instead of using a system model, the operation of a FLC is based on heuristic knowledge and linguistic description [2]. The advantage of fuzzy control is that it can integrate the knowledge of anthropologists into the design process of controllers, without the need of the design process of controllers, without the need of accurate mathematical models [3]. However, building a FLC from a ground-up may not provide good results or sometime even a worse result than a conventional controller if there is not enough knowledge of the system. Therefore, the performance of a FLC can be improved by adjusting the rules, scaling coefficients and membership functions [4]. GA is a stochastic global adaptive search optimization technique based on the mechanisms of natural selection. In recent years, GA has been recognized as an effective and efficient technique to solve optimization problems compared with other optimization techniques [5]. A controller is decomposed into a set of parameters, which the GA attempts to optimize by using simulation based fitness evaluation of candidate controllers in the closed-loop systems [6]. This study applies genetic PI control to design a fast-response controller and improve traditional PI controllers with poorer response. The results of applying the genetic PI controller to the DC motor drive system have been compared to those obtained by the application of a fuzzy controller. The computer simulation results show that the genetic PI controller provides improved dynamic performance than the fuzzy controller. It is also observed that the genetic PI controller shows better transient performance when the system parameters are changed.