Sensorless model predictive control of permanent magnet synchronous motor based on hybrid parallel observer under parameter uncertainty

Model predictive control (MPC)-based permanent magnet synchronous motor (PMSM) drive system has high dependence on parameter accuracy, while the traditional single observer architecture makes it difficult to solve the multi-parameter observation problem. The authors propose the hybrid observer architecture for MPC in this paper. By constructing an adaptive parameter observer and a sliding mode observer (SMO) working in parallel, the variable exchange mechanism between the observers is designed and the current prediction model is reconstructed. The sensorless control of the PMSM under parameter uncertainty is achieved without affecting the dynamic and steady-state performance. Compared with the single SMO architecture, the proposed algorithm improves the position identification precision under the uncertainty of the motor resistance and inductance, reducing the dependence of model parameters and improving the performance of sensorless MPC under parameter uncertainty. The experimental results verify the effectiveness of the proposed algorithm.