A theoretical demonstration for reinforcement learning of PI control dynamics for optimal speed control of DC motors by using Twin Delay Deep Deterministic Policy Gradient Algorithm

• Published in: Expert systems with applications Vol. 213; p. 119192
• Main Authors: Tufenkci, Sevilay, Baykant Alagoz, Baris, Kavuran, Gurkan, Yeroglu, Celaleddin, Herencsar, Norbert, Mahata, Shibendu
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-03-2023
• Subjects: DC motor; Deep reinforcement learning; Metaheuristic optimization; PI controller; Twin-delayed deep deterministic policy gradient; Twin-delayed deep deterministic policy gradient; Metaheuristic optimization; Deep reinforcement learning; DC motor; PI controller
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2022.119192

•The paper benefits from Reinforcement Learning in industrial control applications.•TD3 policy gradient is implemented to learn optimal PI controller dynamics.•The actor-network learns the optimal PI dynamics using the reward mechanism.•The performance of the optimal PI dynamics is compared with the other results.
To benefit from the advantages of Reinforcement Learning (RL) in industrial control applications, RL methods can be used for optimal tuning of the classical controllers based on the simulation scenarios of operating conditions. In this study, the Twin Delay Deep Deterministic (TD3) policy gradient method, which is an effective actor-critic RL strategy, is implemented to learn optimal Proportional Integral (PI) controller dynamics from a Direct Current (DC) motor speed control simulation environment. For this purpose, the PI controller dynamics are introduced to the actor-network by using the PI-based observer states from the control simulation environment. A suitable Simulink simulation environment is adapted to perform the training process of the TD3 algorithm. The actor-network learns the optimal PI controller dynamics by using the reward mechanism that implements the minimization of the optimal control objective function. A setpoint filter is used to describe the desired setpoint response, and step disturbance signals with random amplitude are incorporated in the simulation environment to improve disturbance rejection control skills with the help of experience based learning in the designed control simulation environment. When the training task is completed, the optimal PI controller coefficients are obtained from the weight coefficients of the actor-network. The performance of the optimal PI dynamics, which were learned by using the TD3 algorithm and Deep Deterministic Policy Gradient algorithm, are compared. Moreover, control performance improvement of this RL based PI controller tuning method (RL-PI) is demonstrated relative to performances of both integer and fractional order PI controllers that were tuned by using several popular metaheuristic optimization algorithms such as Genetic Algorithm, Particle Swarm Optimization, Grey Wolf Optimization and Differential Evolution.