Evolved Design of a Nonlinear Proportional Integral Derivative (NPID) Controller

Abstract

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the gain parameters of a conventional PID controller. This fuzzy system's rule base was heuristically evolved using an Evolutionary Algorithm (Differential Evolution). Our results show that a NPID controller can restore a moderately or a heavily under-damped DC motor system under consideration to a desired behavior (slightly under-damped).