Model-Free Coordinated Optimal Regulation for Rigidly Connected Dual-PMSM Systems via Adaptive Dynamic Programming

Jianguo Zhao; Linna Zhou; Weinan Gao; Hai Wang; Chunyu Yang

doi:10.1109/JAS.2025.125207

Volume 12 Issue 10

Oct. 2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2025 > 12(10): 2138-2149

J. Zhao, L. Zhou, W. Gao, H. Wang, and C. Yang, “Model-free coordinated optimal regulation for rigidly connected dual-PMSM systems via adaptive dynamic programming,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2138–2149, Oct. 2025. doi: 10.1109/JAS.2025.125207

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J. Zhao, L. Zhou, W. Gao, H. Wang, and C. Yang, “Model-free coordinated optimal regulation for rigidly connected dual-PMSM systems via adaptive dynamic programming,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 10, pp. 2138–2149, Oct. 2025. doi: 10.1109/JAS.2025.125207

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Model-Free Coordinated Optimal Regulation for Rigidly Connected Dual-PMSM Systems via Adaptive Dynamic Programming

doi: 10.1109/JAS.2025.125207

Funds: This work was supported by the National Natural Science Foundation of China (62073327, 62403467, 62373090, 62273350, 62521001), the Natural Science Foundation of Jiangsu Province (BK20241635), the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation (CPSF) (GZB20240827), Jiangsu Funding Program for Excellent Postdoctoral Talent (2024ZB604), and the China Postdoctoral Science Foundation (2024M763545, 2025T054ZGMK)

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Author Bio:
Jianguo Zhao (Member, IEEE) received the B.S. degree in automation from Lanzhou University of Technology in 2017, the M.S. and Ph.D. degrees in control science and engineering from China University of Mining and Technology in 2020 and 2023, respectively. Currently, he is a Postdoc with the School of Information and Control Engineering, China University of Mining and Technology. He was a Visiting Ph.D. Student with the Department of Electrical Engineering, Yeungnam University, South Korea from September 2022 to September 2023. His research interests include output regulation, optimal control, reinforcement learning, singular perturbation theory with applications to power, industrial, and mechatronic systems. He is the recipient of the best paper award in IEEE International Conference on Control, Automation and Information Sciences (ICCAIS) in 2023

Linna Zhou received the B.S. degree in technical economy from the Department of Water Conservancy Engineering, North China Institute of Water Conservancy and Hydroelectric Power in 2001, and the Ph.D. degree in control theory and control engineering from Northeastern University in 2007. She is currently an Associate Professor at the School of Information and Control Engineering, China University of Mining and Technology. Her research interests include singularly perturbed systems, multi-motor coordinated control, network control, and fuzzy control

Weinan Gao (Senior Member, IEEE) received the B.Sc. degree in automation from Northeastern University in 2011, the M.Sc. degree in control theory and control engineering from Northeastern University in 2013, and the Ph.D. degree in electrical engineering from New York University, USA in 2017. He is a Professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University. Previously, he was an Assistant Professor of mechanical and civil engineering at Florida Institute of Technology, USA, an Assistant Professor of electrical and computer engineering at Georgia Southern University, USA, and a Visiting Professor of Mitsubishi Electric Research Laboratory (MERL), USA. His research interests include reinforcement learning, adaptive dynamic programming (ADP), optimal control, cooperative adaptive cruise control (CACC), intelligent transportation systems, sampled-data control systems, and output regulation theory.He is the recipient of the best paper award in IEEE International Conference on Control, Automation and Information Sciences (ICCAIS) in 2023, IEEE Data Driven Control and Learning Systems (DDCLS) Conference in 2023, IEEE International Conference on Real-time Computing and Robotics (RCAR) in 2018 and the David Goodman Research Award at New York University in 2019. He is an Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, IEEE/CAA Journal of Automatica Sinica, Control Engineering Practice, and IEEE Transactions on Circuits and Systems II: Express Briefs, a Member of Editorial Board of Neural Computing and Applications, and a Technical Committee Member in IEEE Control Systems Society on Nonlinear Systems and Control and in IFAC TC 1.2 Adaptive and Learning Systems

Hai Wang received the B.S. degree in electrical engineering and automation, the M.S. degree in control engineering, from China University of Mining and Technology in 2020 and 2023, respectively. From 2023, he is working for the Ph.D. degree in control engineering from Nankai University. His research interests include tracking control and motor control

Chunyu Yang (Senior Member, IEEE) received the B.S. degree in applied mathematics and the Ph.D. degree in control theory and control engineering from Northeastern University, in 2002 and 2009, respectively. Currently, he is a Professor with the School of Information and Control Engineering, China University of Mining and Technology. His research interests include singularly perturbed systems, industrial process operational control, cyber physical systems, and robust control
Corresponding author: Linna Zhou, e-mail: linnazhou@cumt.edu.cn
Received Date: 2024-08-11
Revised Date: 2024-12-11
Accepted Date: 2025-01-19

Abstract

Abstract

In this article, a novel model-free coordinated optimal regulation design methodology is proposed for the rigidly connected dual permanent magnet synchronous motor (PMSM) system via adaptive dynamic programming (ADP). First, we adopt the classical master-slave structure to maintain torque synchronization by virtue of field-oriented control. Then, a reduced-order model of the dual-PMSM system is established through the application of singular perturbation theory (SPT), which is of significance to decrease the learning time and computational complexity in the outer speed loop design. Afterwards, we design a coordinated adaptive optimal regulator in framework of ADP to drive the speed of girth gear asymptotic tracking the reference signal and accommodate the load torque disturbance, which is independent of the knowledge of model parameters of the system. According to SPT, we analyze the suboptimality, closed-loop stability, and robustness properties of the obtained controller under mild conditions. Finally, comprehensive experimental studies are provided to verify that the proposed control strategy can achieve the speed regulation and the torque synchronization, as well as ameliorate the transient response.
- Adaptive dynamic programming (ADP),
- optimal control,
- output regulation,
- permanent magnet synchronous motor (PMSM),
- singular perturbation theory (SPT)

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References(39)

References

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Model-Free Coordinated Optimal Regulation for Rigidly Connected Dual-PMSM Systems via Adaptive Dynamic Programming

doi: 10.1109/JAS.2025.125207

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