Model Predictive Control Strategy Based on Adaptive Adjustment of Virtual Resistance for ECL Drive System

Aimed at mitigating DC bus voltage fluctuations in electrolytic capacitor-less (ECL) motor drive systems caused by insufficient damping, conventional model predictive control (MPC) offers a fast dynamic response but fails to enhance the inherent damping or fully suppress such voltage variations. To address this limitation, this paper proposes a model predictive control strategy with adaptive virtual resistance adjustment (AVR-MPC). First, a virtual resistance loop is embedded into the active power decoupling circuit to reshape the system impedance and improve the damping characteristics at the model level. Subsequently, the state equations incorporating the virtual resistance are derived using small-signal modeling, and a Lyapunov function is constructed to determine its stable operating range. Based on this analysis, a dynamic relationship between the virtual resistance and the predicted current deviation is established, enabling adaptive tuning of the virtual resistance in response to the current deviation, thereby enhancing system stability under transient conditions. Finally, experimental results validate the effectiveness of the proposed control strategy.