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A study of event-triggered control systems using deep reinforcement learning
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Abstract: (19 Views) |
In recent years, event-triggered control (ETC) has been widely adopted due to its advantage of reducing sample and controller design costs compared to conventional excitation-time-based control. Recently, the application of machine learning methods, especially reinforcement learning (RL) and deep reinforcement learning (DRL), for controlling various systems has attracted significant attention. The purpose of this study is to review past research in the field of ETC systems based on RL and DRL. DRL can solve optimal control problems for nonlinear systems that are difficult to address with traditional methods. In this study, an overview of the research conducted in the field of ETC systems is presented. Then, the methods used in RL and DRL for controlling different systems, with a focus on ETC systems, are examined. Finally, based on the previous works, suggestions are made to improve the performance of such controllers.
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| Keywords: Event-triggered control, Reinforcement learning, Deep learning, Deep reinforcement learning. |
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Full-Text [PDF 1014 kb]
(25 Downloads)
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Type of Study: Scientific-extension |
Subject:
Special
Received: 2026/05/10 | Accepted: 2026/03/16 | Published: 2026/03/16
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