Research on the High Stability of an Adaptive Controller Based on a Neural Network for an Electrolysis-Free-Capacitor Motor Drive System

Danyang Bao, Haorui Shen, Wenxiang Ding, Hao Yuan, Yingying Guo, Zhendong Song () and Tao Gong ()
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Danyang Bao: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China
Haorui Shen: School of Mechanical and Electrical Engineering, Harbin Institute of Technology University, Shenzhen 518055, China
Wenxiang Ding: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China
Hao Yuan: School of Mechanical and Electrical Engineering, Harbin Institute of Technology University, Shenzhen 518055, China
Yingying Guo: School of Mechanical and Electrical Engineering, Harbin Institute of Technology University, Shenzhen 518055, China
Zhendong Song: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China
Tao Gong: School of Mechanical and Electrical Engineering, Shenzhen Polytechnic University, Shenzhen 518005, China

Energies, 2025, vol. 18, issue 8, 1-16

Abstract: The electrolytic capacitor-less PMSM drive system presents complex nonlinear characteristics. Since electrolytic capacitor-less systems exhibit low inertia due to the absence of energy storage components, traditional controllers struggle to achieve the dynamic optimization of phase and amplitude margins, resulting in power transmission mismatches that trigger DC bus voltage surges. This severely limits the dynamic response capability and reliable operation of the system across full operating conditions, leading to an insufficient wide-speed-range performance and disturbance rejection. This study investigates the stable operation mechanism under intermittent working conditions by analyzing DC bus voltage transient characteristics. It optimizes control parameters for stable intermittent operations and establishes a neural network-based adaptive controller model. By modeling the correlation between hardware parameters and control parameters in drive systems under frequent start–stop conditions, this research achieves dynamic controllability of the controller during intermittent operations. This approach enhances the computational accuracy of the drive system control model, ultimately improving system-wide operational reliability and adaptability. Experimental validation confirms the effectiveness of this approach, showing significant reliability improvements in capacitor-less variable-frequency speed-control systems. Key innovations include: (1) BP neural network integration for dynamic parameter optimization, (2) impulse voltage suppression through adaptive control matching, and (3) enhanced transient response via machine learning-enhanced speed regulation. The test results demonstrate a 63% reduction in bus voltage fluctuations and 35% improvement in load transition responses compared to conventional PID-based systems, proving the strategy’s practical viability for industrial drive applications.

Keywords: electrolytic capacitor-less; PMSM; nonlinear system; control-parameter data; instantaneous stable operation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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