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

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.