Detail publikace

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

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

16

URL

Plný text v Digitální knihovně

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"
}