Hybrid Event-Triggered Control With Stability Analysis

Ding Wang; Lingzhi Hu; Junfei Qiao

doi:10.1109/JAS.2024.125067

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > In Press, Accepted Manuscript

D. Wang, L. Hu, and J. Qiao, “Hybrid event-triggered control with stability analysis,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1464–1474, Jul. 2025. doi: 10.1109/JAS.2024.125067

Citation:

D. Wang, L. Hu, and J. Qiao, “Hybrid event-triggered control with stability analysis,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1464–1474, Jul. 2025. doi: 10.1109/JAS.2024.125067

D. Wang, L. Hu, and J. Qiao, “Hybrid event-triggered control with stability analysis,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1464–1474, Jul. 2025. doi: 10.1109/JAS.2024.125067

Citation:

D. Wang, L. Hu, and J. Qiao, “Hybrid event-triggered control with stability analysis,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 7, pp. 1464–1474, Jul. 2025. doi: 10.1109/JAS.2024.125067

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Hybrid Event-Triggered Control With Stability Analysis

doi: 10.1109/JAS.2024.125067

Funds: This work was supported in part by the National Natural Science Foundation of China (62222301, 62473012, 62021003), the National Science and Technology Major Project (2021ZD0112302, 2021ZD0112301), and the Beijing Natural Science Foundation (JQ19013)

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Author Bio:
Ding Wang (Senior Member, IEEE) received the B.S. degree in mathematics from Zhengzhou University of Light Industry in 2007, the M.S. degree in operations research and cybernetics from Northeastern University in 2009, and the Ph.D. degree in control theory and control engineering from the Institute of Automation, Chinese Academy of Sciences in 2012. From 2015 to 2017, he was a Visiting Scholar with the Department of Electrical, Computer, and Biomedical Engineering, University of Rhode Island, Kingston, USA. He was an Associate Professor with the State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences. He is currently a Full Professor with the School of Information Science and Technology, Beijing University of Technology. He has authored or co-authored over 150 journal and conference papers and five monographs. His research interests include adaptive critic control with industrial applications, reinforcement learning, and intelligent systems. Dr. Wang was selected as a Clarivate Highly Cited Researcher and Elsevier Most Cited Chinese Researcher. He is a member of IEEE/CAA Journal of Automatica Sinica Early Career Advisory Board. He currently serves as an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, IEEE Transactions on Automation Science and Engineering, Neural Networks, Engineering Applications of Artificial Intelligence, ISA Transactions, International Journal of Robust and Nonlinear Control, and Acta Automatica Sinica

Lingzhi Hu received the B.E. degree in automation from Heilongjiang University of Science and Technology in 2019, and the M.E. degree in control science and engineering from Beijing University of Technology in 2023. Currently, he is pursuing the Ph.D. degree in control science and engineering from with the School of Information Science and Technology, Beijing University of Technology. His doctoral research is centered on advanced topics such as adaptive dynamic programming, event-triggered control, and reinforcement learning. His research interests are diverse and cutting-edge, covering adaptive dynamic programming, a method used to optimize complex systems with changing conditions. His work in event-triggered control focuses on improving the efficiency and accuracy of control systems by adjusting operations based on specific events rather than continuous monitoring. Additionally, his research in reinforcement learning explores how machines can learn autonomously by interacting with their environment

Junfei Qiao (Senior Member, IEEE) received the B.E. and M.E. degrees in control engineering from Liaoning Technical University in 1992 and 1995, respectively, and the Ph.D. degree from Northeastern University China, in 1998. He is currently a Professor with with the School of Information Science and Technology, Beijing University of Technology where he is also the Director of the Beijing Key Laboratory of Computational Intelligence and Intelligent Systems and Beijing Laboratory of Smart Environmental Protection. His research interests include neural networks, intelligent systems, self-adaptive systems, and process control
Corresponding author: Ding Wang, e-mail: dingwang@bjut.edu.cn
Received Date: 2024-10-06
Accepted Date: 2024-11-19

Available Online: 2025-05-26

Abstract

Abstract

In this paper, a novel hybrid event-triggered control (ETC) method is developed based on the online action-critic technique, which aims at tackling the optimal regulation problem of discrete-time nonlinear systems. In order to ensure the normal execution of the online learning algorithm, a stability criterion condition is created to obtain the initial admissible control policy by using an offline iterative method under the time-triggered control framework. Subsequently, a general triggering condition is designed based on the uniform ultimate boundedness of the controlled system. In order to determine a constant interval which can ensure the system stability, another triggering condition is introduced and the asymptotic stability of the closed-loop system satisfying this condition is analyzed from the perspective of the input-to-state stability. The designed online hybrid ETC method not only further improves control efficiency, but also avoids the continuous judgment of the corresponding triggering condition. In addition, the event-based control law can approach the optimal control input within a finite approximation error. Finally, two experimental examples with physical background are conducted to indicate the present results.
- Adaptive critic control,
- discrete-time nonlinear systems,
- hybrid event-triggered mechanism,
- initial admissible policies,
- stability analysis

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References

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Hybrid Event-Triggered Control With Stability Analysis

doi: 10.1109/JAS.2024.125067

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