Intelligent Adaptive PID Control Using Fuzzy Broad Learning System: An Application to Tool-Grinding Servo control System
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Intelligent Adaptive PID Control Using Fuzzy Broad Learning System: An Application to Tool-Grinding Servo control Systems Ching-Chih Tsai1 • Chun-Chieh Chan1 • Yi-Chang Li2 • Feng-Chun Tai1
Received: 8 November 2019 / Revised: 30 April 2020 / Accepted: 22 June 2020 Taiwan Fuzzy Systems Association 2020
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 & Ching-Chih Tsai [email protected] Chun-Chieh Chan [email protected] Yi-Chang Li [email protected] Feng-Chun Tai [email protected] 1
Department of Electrical Engineering, National Chung Hsing University, Taichung, Taiwan
2
Num Taiwan Ltd, 7F-2, No. 536, Sec. 2, Taiwan Boulevard, Taichung 40353, Taiwan
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. Keywords Adaptive PID control Fuzzy broad learning system (FBLS) Identifier Predictive control Process control Tool-grinding servo control (TGSC) systems
1 Introduction Conventional fixed-gain PID controllers have been widely used in industry and machine tool industry due to their simple control structure, ease of tuning low cost and high robustness [1–3]. PID parameter optimization for such PID controllers has been regarded as an important parameter search problem in academia and industry. To deal with the problem, Wu [4] presented an RGA-based PSO optimization method for unconstrained problems and Hsu [5] proposed a PSO-RGA algorithm to tune the PID gains in the inner and outer loops of the warm water supply systems with solar heat pumps, which are modeled by simple firstorder system model with time delays. However, fixed-gain PID controllers may not provide satisfactory control performance or meet stringent performance requirements for many industrial nonlinear dynamic systems. Due to the ease of use and engineering efficiency, several self-tuning or adaptive
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