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Algorithms 2018, 11(10), 148; https://doi.org/10.3390/a11100148

Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control

Department of Industrial Design and Production Engineering, University of West Attica, GR-12241 Egaleo-Athens, Greece
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Received: 31 August 2018 / Revised: 20 September 2018 / Accepted: 29 September 2018 / Published: 30 September 2018
(This article belongs to the Special Issue Algorithms for PID Controller)
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Abstract

This paper proposes a hybrid Zeigler-Nichols (Z-N) reinforcement learning approach for online tuning of the parameters of the Proportional Integral Derivative (PID) for controlling the speed of a DC motor. The PID gains are set by the Z-N method, and are then adapted online through the fuzzy Q-Learning agent. The fuzzy Q-Learning agent is used instead of the conventional Q-Learning, in order to deal with the continuous state-action space. The fuzzy Q-Learning agent defines its state according to the value of the error. The output signal of the agent consists of three output variables, in which each one defines the percentage change of each gain. Each gain can be increased or decreased from 0% to 50% of its initial value. Through this method, the gains of the controller are adjusted online via the interaction of the environment. The knowledge of the expert is not a necessity during the setup process. The simulation results highlight the performance of the proposed control strategy. After the exploration phase, the settling time is reduced in the steady states. In the transient states, the response has less amplitude oscillations and reaches the equilibrium point faster than the conventional PID controller. View Full-Text
Keywords: reinforcement learning (RL); PID controller; on line tuning; DC motor control; fuzzy Q-Learning agent reinforcement learning (RL); PID controller; on line tuning; DC motor control; fuzzy Q-Learning agent
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Kofinas, P.; Dounis, A.I. Fuzzy Q-Learning Agent for Online Tuning of PID Controller for DC Motor Speed Control. Algorithms 2018, 11, 148.

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