Particle swarm optimization of fuzzy supervisory controller for nonlinear position control system

Abstract

PID controllers with fixed parameters cannot produce satisfactory results for systems with nonlinear or complex characteristics. Fuzzy Supervisory control (FSC) is a proper method to modify the PIP controller to form a nonlinear Self-Tuning Fuzzy PID controller. In this type of controllers, the fuzzy supervisory controller placed in the upper level, makes the supervisory decision to the PID controller placed in the lower level. The supervisory fuzzy rule set is used for on-line tuning of the PID controller to achieve better performance resulting in an adaptive controller. The main drawback of fuzzy logic control (FLC) is that, the design becomes more difficult and very time consuming when the number of its inputs and outputs is increased such as in case of FSC. Also, the fuzzy rule bases are dependent on the characteristics of the controlled plant and were determined from the practical experience. This paper introduces a method for designing fuzzy supervisory controller using particle swarm optimization technique, to obtain the optimal rule base, scaling factors, membership function parameters and the optimal range for tuning Kp, Ki and Kd of the PID controller, placed in the forward control loop of a nonlinear DC motor position control system including backlash nonlinearity.