Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Design and Simulation of Adaptive PID Controller Based on Fuzzy Q-Learning Algorithm for a BLDC Motor

Version 1 : Received: 17 November 2020 / Approved: 19 November 2020 / Online: 19 November 2020 (10:09:42 CET)

How to cite: Ardeshiri, R.R.; Nabiyev, N.; Band, S.S.; Mosavi, A. Design and Simulation of Adaptive PID Controller Based on Fuzzy Q-Learning Algorithm for a BLDC Motor. Preprints 2020, 2020110482 (doi: 10.20944/preprints202011.0482.v1). Ardeshiri, R.R.; Nabiyev, N.; Band, S.S.; Mosavi, A. Design and Simulation of Adaptive PID Controller Based on Fuzzy Q-Learning Algorithm for a BLDC Motor. Preprints 2020, 2020110482 (doi: 10.20944/preprints202011.0482.v1).

Abstract

Reinforcement learning (RL) is an extensively applied control method for the purpose of designing intelligent control systems to achieve high accuracy as well as better performance. In the present article, the PID controller is considered as the main control strategy for brushless DC (BLDC) motor speed control. For better performance, the fuzzy Q-learning (FQL) method as a reinforcement learning approach is proposed to adjust the PID coefficients. A comparison with the adaptive PID (APID) controller is also performed for the superiority of the proposed method, and the findings demonstrate the reduction of the error of the proposed method and elimination of the overshoot for controlling the motor speed. MATLAB/SIMULINK has been used for modeling, simulation, and control design of the BLDC motor.

Subject Areas

Q-learning; Fuzzy logic; Adaptive controller; BLDC motor

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