Motor Model Predictive Control Strategy and Dynamic Response Optimization for LCI-Driven Synchronous Motor Systems

In this paper, a non-cascading model predictive control (MPC) approach is proposed to improve the dynamic response and anti-disturbance performance of load commutator inverter (LCI) powered synchronous motor systems to overcome the inherent limitations of traditional dual PI control architectures. Firstly, based on the LCI mathematical model and motor motion equation, the state space prediction equation with the bridge trigger Angle as the control output is constructed. Then, the incremental prediction model is adopted, and the speed and trigger Angle constraints are directly incorporated into the objective function. Finally, a constrained quadratic programming (QP) solver is used to iteratively optimize the control sequence in the rolling time domain. Through MATLAB/Simulink simulation, compared with the dual PI strategy, the proposed method achieves significant improvement in both transient performance and robustness against load disturbance.