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Open AccessArticle
Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators
by
Yuling Liang
Yuling Liang
Dr. Yuling Liang is currently at the Artificial Intelligence College at Shenyang University of in in [...]
Dr. Yuling Liang is currently at the Artificial Intelligence College at Shenyang University of Technology in Shenyang. She completed her M.S. degree in control theory and control engineering from Shenyang Aerospace University in 2016 and her Ph.D. degree from the College of Information Science and Engineering at Northeastern University in 2021. Her research interests include nonlinear control, neural network control, reinforcement learning, optimal control, and machine learning.
Mengjia Xie
Mengjia Xie
Juan Zhang
Juan Zhang
Zhongyang Ming
Zhongyang Ming
Zhongyang Ming is an associate professor in the School of Information Science and Engineering at He [...]
Zhongyang Ming is an associate professor in the School of Information Science and Engineering at Northeastern University. He earned a Bachelor of Science in Mathematics and Applied Mathematics from Shenyang Normal
University in 2018, a Master of Science in Fundamental Mathematics in 2020, and a Doctor of Philosophy in Control Science and Engineering in 2024, all from Northeastern University. His research interests include adaptive dynamic programming, reinforcement learning, integrated energy systems, and motor optimization control.
Zhiyun Gao
Zhiyun Gao
1
School of Artificial Intelligence, Shenyang University of Technology, Shenyang 110870, China
2
School of Information Science and Engineering, Northeastern University, Shenyang 110819, China
3
School of Transportation Science and Engineering, Civil Aviation University of China, Tianjin 300300, China
*
Author to whom correspondence should be addressed.
Actuators 2025, 14(10), 506; https://doi.org/10.3390/act14100506 (registering DOI)
Submission received: 3 September 2025
/
Revised: 5 October 2025
/
Accepted: 17 October 2025
/
Published: 19 October 2025
Abstract
This study explores a stochastic guarantee cost control (GCC) for time-varying systems with random parameters and asymmetric saturation actuators by employing the integral reinforcement learning (IRL) method in the dynamic event-triggered (DET) mode. Firstly, a modified Hamilton–Jacobi–Isaac (HJI) equation is formulated, and then the worst-case disturbance policy and the asymmetric saturation optimal control signal can be obtained. Secondly, the multivariate probabilistic collocation method (MPCM) is used to evaluate the value function at designated sampling points. The purpose of introducing the MPCM is to simplify the computational complexity of stochastic dynamic programming (SDP) methods. Furthermore, the DET mode is utilized to solve the SDP problem to reduce the computational burden on communication resources. Finally, the Lyapunov stability theorem is applied to analyze the stability of time-varying systems, and the simulation shows the feasibility of the designed method.
Share and Cite
MDPI and ACS Style
Liang, Y.; Xie, M.; Zhang, J.; Ming, Z.; Gao, Z.
Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators. Actuators 2025, 14, 506.
https://doi.org/10.3390/act14100506
AMA Style
Liang Y, Xie M, Zhang J, Ming Z, Gao Z.
Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators. Actuators. 2025; 14(10):506.
https://doi.org/10.3390/act14100506
Chicago/Turabian Style
Liang, Yuling, Mengjia Xie, Juan Zhang, Zhongyang Ming, and Zhiyun Gao.
2025. "Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators" Actuators 14, no. 10: 506.
https://doi.org/10.3390/act14100506
APA Style
Liang, Y., Xie, M., Zhang, J., Ming, Z., & Gao, Z.
(2025). Integral Reinforcement Learning-Based Stochastic Guaranteed Cost Control for Time-Varying Systems with Asymmetric Saturation Actuators. Actuators, 14(10), 506.
https://doi.org/10.3390/act14100506
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