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TUFENKCI, S. ALAGOZ, B. B. KAVURAN, G. YEROGLU, C. HERENCSÁR, N. MAHATA, S.
Originální název
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
Typ
článek v časopise ve Web of Science, Jimp
Jazyk
angličtina
Originální abstrakt
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 con-ditions. 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 envi-ronment. 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 simu-lation 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 Opti-mization, Grey Wolf Optimization and Differential Evolution.
Klíčová slova
Deep reinforcement learning; DC motor; PI controller; Twin -delayed deep deterministic policy; gradient; Metaheuristic optimization
Autoři
TUFENKCI, S.; ALAGOZ, B. B.; KAVURAN, G.; YEROGLU, C.; HERENCSÁR, N.; MAHATA, S.
Vydáno
1. 3. 2023
Nakladatel
PERGAMON-ELSEVIER SCIENCE LTD
Místo
OXFORD
ISSN
0957-4174
Periodikum
EXPERT SYSTEMS WITH APPLICATIONS
Ročník
213,Part C
Číslo
March 2023
Stát
Spojené státy americké
Strany od
1
Strany do
16
Strany počet
URL
https://www.sciencedirect.com/science/article/pii/S0957417422022102
Plný text v Digitální knihovně
http://hdl.handle.net/11012/208567
BibTex
@article{BUT179598, author="Sevilay {Tufenkci} and Baris Baykant {Alagoz} and Gürkan {Kavuran} and Celaleddin {Yeroglu} and Norbert {Herencsár} and Shibendu {Mahata}", title="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", journal="EXPERT SYSTEMS WITH APPLICATIONS", year="2023", volume="213,Part C", number="March 2023", pages="16", doi="10.1016/j.eswa.2022.119192", issn="0957-4174", url="https://www.sciencedirect.com/science/article/pii/S0957417422022102" }