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Adaptive parameter active-disturbance rejection deep reinforcement learning control strategy for permanent magnet synchronous linear motors
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Lin SONG, Ziling NIE, Jun SUN, Yangwei ZHOU, Huayu LI*
Journal of National Niversity of Defense Technology | 2025, 47(6) : 119 - 131
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Journal of National Niversity of Defense Technology | 2025, 47(6): 119-131
• State Monitoring Technology for Electric Machine System •
Adaptive parameter active-disturbance rejection deep reinforcement learning control strategy for permanent magnet synchronous linear motors
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Lin SONG, Ziling NIE, Jun SUN, Yangwei ZHOU, Huayu LI*
Affiliations
  • National Key Laboratory of Science and Technology on Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
Published: 2025-12-28 doi: 10.11887/j.issn.1001-2486.25010043
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An adaptive active disturbance rejection control strategy integrating DRL(deep reinforcement learning)with enhanced PSO(particle swarm optimization)was presented, aiming to improve the speed and thrust control performance of PMSLMs(permanent magnet synchronous linear motors).A mathematical model of the motor was established to analyze its dynamic characteristics, followed by the design of a DRLPSO control framework.This framework leveraged reward mechanisms in reinforcement learning to interact with the environment, dynamically optimized ADRC(active disturbance rejection controller)parameters to accommodate varying operating conditions and external disturbances.The modified PSO algorithm incorporated partitioned inertia weights and cyclically utilized historical global optimal data to iteratively update control policies, refining neural network weights and thereby enhancing search efficiency and optimization accuracy.Experimental results show that the proposedDRLPSO-ADRCmethod achieves significantly higher tracking precision in position and velocity, along with improved system stability and resistance to thrust disturbances, compared to conventionalPSO-ADRCalgorithms.These findings validate the effectiveness of the innovative control strategy.

permanent magnet synchronous linear motor  /  active disturbance rejection control  /  deep reinforcement learning  /  particle swarm optimization  /  fluctuation suppression
Lin SONG, Ziling NIE, Jun SUN, Yangwei ZHOU, Huayu LI. Adaptive parameter active-disturbance rejection deep reinforcement learning control strategy for permanent magnet synchronous linear motors[J]. Journal of National Niversity of Defense Technology, 2025 , 47 (6) : 119 -131 . DOI: 10.11887/j.issn.1001-2486.25010043
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doi: 10.11887/j.issn.1001-2486.25010043
  • Receive Date：2025-01-26
  • Online Date：2026-04-16
  • Published：2025-12-28
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  • Received：2025-01-26
Affiliations
    National Key Laboratory of Science and Technology on Electromagnetic Energy, Naval University of Engineering, Wuhan 430033, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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