Combining hybrid metaheuristic algorithms and reinforcement learning to improve the optimal control of nonlinear continuous-time systems with input constraints

This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is...

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Bibliographic Details
Published in:Computers & electrical engineering Vol. 116; p. 109179
Main Authors: Khalili Amirabadi, Roya, Solaymani Fard, Omid
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.05.2024
Subjects:
Actor-critic neural network
Hybrid metaheuristic algorithms
Nonlinear systems
Optimal tracking control
Reinforcement learning
Hybrid metaheuristic algorithms
Optimal tracking control
Nonlinear systems
Actor-critic neural network
Reinforcement learning
ISSN:0045-7906, 1879-0755
Online Access:Get full text
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Summary:This paper proposes an innovative method for achieving optimal tracking control in nonlinear continuous-time systems with input constraints. The method combines reinforcement learning and hybrid metaheuristics to enhance the controller’s performance. Specifically, a hybrid metaheuristic algorithm is employed to optimize the hyperparameters of a critic neural network, which serves as the system’s controller. The proposed approach is evaluated through extensive simulation studies on a nonlinear system with input constraints. Results demonstrate its superiority over traditional control techniques in terms of accuracy, timeliness, and stability. Notably, the approach effectively eliminates overshoot and steady-state error while providing precise and prompt tracking and showcasing remarkable robustness against model uncertainties. By synergistically integrating reinforcement learning and hybrid metaheuristics, this approach represents a significant advancement in enhancing the control performance of complex nonlinear systems. The simulation studies confirm superiority of the proposed approach over existing techniques, offering a promising solution for achieving optimal tracking control in nonlinear systems with input constraints. This approach holds potential for a wide range of applications, including robotics, aerospace, and manufacturing, where precise and prompt tracking control is critical.
ISSN:0045-7906
1879-0755
DOI:10.1016/j.compeleceng.2024.109179