Version 1
: Received: 15 March 2024 / Approved: 15 March 2024 / Online: 15 March 2024 (12:12:48 CET)
How to cite:
Bujgoi, G.; Sendrescu, D. Tuning of PID Controllers using Reinforcement Learning for Nonlinear Systems Control. Preprints2024, 2024030914. https://doi.org/10.20944/preprints202403.0914.v1
Bujgoi, G.; Sendrescu, D. Tuning of PID Controllers using Reinforcement Learning for Nonlinear Systems Control. Preprints 2024, 2024030914. https://doi.org/10.20944/preprints202403.0914.v1
Bujgoi, G.; Sendrescu, D. Tuning of PID Controllers using Reinforcement Learning for Nonlinear Systems Control. Preprints2024, 2024030914. https://doi.org/10.20944/preprints202403.0914.v1
APA Style
Bujgoi, G., & Sendrescu, D. (2024). Tuning of PID Controllers using Reinforcement Learning for Nonlinear Systems Control. Preprints. https://doi.org/10.20944/preprints202403.0914.v1
Chicago/Turabian Style
Bujgoi, G. and Dorin Sendrescu. 2024 "Tuning of PID Controllers using Reinforcement Learning for Nonlinear Systems Control" Preprints. https://doi.org/10.20944/preprints202403.0914.v1
Abstract
The numerical implementation of the controllers allows the use of very complex algorithms with ease. However, in practice, due to its proven advantages, the PID controller (and its variants) is widely used in industrial control systems as well as in many other applications that require continuous control. Most of the methods for tuning the parameters of PID controllers are based on time-invariant linear models of the processes, which in practice can lead to poor performance of the control system. The paper presents an application of reinforcement learning algorithms in tuning of PID controllers for the control of some classes of continuous nonlinear systems. Tuning the parameters of PID controllers is done with the help of Twin Delayed Deep Deterministic Policy Gradients (TD3) algorithm which presents a series of advantages compared to other similar methods from Machine Learning dedicated to continuous state and action spaces. TD3 algorithm is an off-policy Actor-Critic based method and was used as it does not require a system model. The presented technique is applied for control of a biotechnological system which has a strongly nonlinear dynamic. 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 simulation illustrating the effectiveness of the proposed methodology.
Keywords
learning-based control; nonlinear systems control; PID controller; bioprocess
Subject
Engineering, Control and Systems Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
