Tuning of PID Controllers Using Reinforcement Learning for Nonlinear System Control

• Published in: Processes Vol. 13; no. 3; p. 735
• Main Authors: Bujgoi, Gheorghe, Sendrescu, Dorin
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 03.03.2025
• Subjects: Actors; Actresses; Aircraft; Algorithms; Approximation; Artificial intelligence; Comparative analysis; Control algorithms; Control systems; Control tasks; Controllers; Data mining; Design; Digital signal processors; Dynamic programming; Dynamical systems; Field programmable gate arrays; Machine learning; Methods; Nonlinear control; Nonlinear dynamics; Nonlinear systems; Process controls; Proportional integral derivative; Reinforcement; Robotics; Tuning
• ISSN: 2227-9717; 2227-9717
• DOI: 10.3390/pr13030735

This paper presents the application of reinforcement learning algorithms in the tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of the PID controllers is performed with the help of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which presents a series of advantages compared to other similar methods from machine learning dedicated to continuous state and action spaces. The TD3 algorithm is an off-policy actor–critic-based method and is used as it does not require a system model. Double Q-learning, delayed policy updates and target policy smoothing make TD3 robust against overestimation, increase its stability, and improve its exploration. These enhancements make TD3 one of the state-of-the-art algorithms for continuous control tasks. The presented technique is applied for the control of a biotechnological system that has strongly nonlinear dynamics. The proposed tuning method is compared to the classical tuning methods of PID controllers. The performance of the tuning method based on the TD3 algorithm is demonstrated through a simulation, illustrating the effectiveness of the proposed methodology.