Genetic-Neuro-Fuzzy Controllers for Second Order Control Systems

Abstract

Overshoot, settling and rise time define the timing parameters of a control system. The main challenge is to attempt to reduce these parameters to achieve good control performances. The target is to obtain the optimal timing values. In this paper, three different approaches are presented to improve the control performances of second order control systems. The first approach is related to the design of a PID controller based on Ziegler-Nichols tuning formula. An optimal fuzzy controller optimized through Genetic Algorithms represents the second approach. Following this way, the best membership functions are chosen with the help of the darwinian theory of natural selection. The third approach uses the neural networks to achieve adaptive neuro-fuzzy controllers. In this way, the fuzzy controller assumes self-tuning capability. The results show that the designed PID controller has a very slow rise time. The genetic-fuzzy controller gives good values of overshoot and settling time. The best global results are achieved by neuro-fuzzy controller which presents good values of overshoot, settling and rise time. Moreover, our neuro-fuzzy controller improves the results of some conventional PID and fuzzy controllers.