Intelligent Adaptive PID Control Using Fuzzy Broad Learning System: An Application to Tool-Grinding Servo control Systems

Abstract

This paper presents an intelligent adaptive proportional-integral-derivative (PID) control method using fuzzy broad learning system (FBLS) and investigates how the method can be applied to control a tool-grinding servo control (TGSC) system. Due to accuracy, quality and geometric errors which are often difficult to capture the dynamics of the controlled plants or systems, fixed-gain PID controllers without good three-term parameters cannot meet the stringent control performance specifications of nonlinear industrial systems and servomechanisms. To accomplish better control, an adaptive PID control strategy based on the FBLS, or abbreviated as FBLS-APPID, is rigorously proposed by integrating an online parameter learning FBLS identifier together with an adaptive predictive PID control law using FBLS, to eliminate tracking error and achieve fast-tracking and disturbance rejection. Numerical simulations on the two existing discrete-time nonlinear time-delay processes are performed to show the merits and superiority of the constructed FBLS-APPID by comparing to three existing adaptive PID methods. Finally, the applicability of the proposed method is well exemplified by conducting comparatively experimental results on a servo control loop of a real TGSC machine with fixed PID gains tuned by the proposed FBLS-APPID method.