ESO-Based Model Predictive Current Control for Permanent Magnet Synchronous Motors

Aiming at the dynamic response degradation caused by parameter drift in conventional MPCC in the Drive systems for permanent magnet synchronous motor, this study proposes an enhanced predictive control framework (MPCC-ESO) incorporating an expanded state observer (ESO), which significantly improves the parameter robustness through the real-time perturbation observation and compensation mechanism. The method observes and compensates the perturbations caused by parameter changes in real time through ESO, which reduces the dependence on motor parameters and simplifies the control parameter adjustment process. Firstly, the mathematical model of the PMSM in the dq-axis coordinate system is established, and the predictive current control strategy is designed based on the discretisation of the first-order Eulerian method; secondly, the ESO is constructed by expanding the state variables of the system to realise the on-line estimation of the rotational speed and the nonlinear perturbations, so as to enhance the robustness of the system. The simulation and experimental results show that the dynamic response speed, steady-state accuracy and computational efficiency of MPCC-ESO are better than the traditional MPCC method under the parameter accuracy and mismatch, and it has stronger adaptability to the motor parameter variations, which provides a new solution for the efficient control of PMSM drive system.