Tuning of PID controllers for unstable processes based on gain and phase margin specifications: a fuzzy neural approach

Abstract

This paper presents a PID tuning method for unstable processes using an adaptive-network-based-fuzzy-inference system (ANFIS) for given gain and phase margin (GPM) specifications. PID tuning methods are widely used to control stable processes. However, PID controller for unstable processes is less common. In this paper, the PID controller parameters can be determined by the ANFIS. Because the definitions of gain and phase margin equations are complex, an analytical tuning method for achieving specified the gain and phase margins is not yet available. In this paper, the ANFIS is adopted to identify the relationship between the gain-phase margin specifications and the PID controller parameters. Then, it is used to automatically tune the PID controller parameters for different gain and phase margin specifications so that neither numerical methods nor graphical methods need be used. A simple method is also developed to estimate the stabilizing region of PID controller parameters and valid region for gain-phase margin. Even for unreasonable specifications, out of the valid region, the ANFIS can still find suitable PID controller to guarantee the stability of the closed-loop system. Simulation results show that the ANFIS can achieve the specified values efficiently.