Improved non-cascaded continuous-set model-free predictive control scheme for PMSM drives

Abstract

This article presents an improved multiple-step model-free predictive control (MS-MFPC) method for permanent magnet synchronous motor (PMSM) drives. Specifically, a reduced-order MS-MFPC based on an ultra-local model, which employs only the input and output of the plant without addressing any motor parameters, is developed to enhance the robustness and reliability of the non-cascaded PMSM system in the presence of parametric uncertainties. Meanwhile, the decoupled form of algebraic ultra-local model is derived to extend the prediction horizon to multiple steps without increasing the computational burden. Besides, in order to improve the dynamic performance, an online adaptive modification approach is embedded into this suggested design to enhance the cost function calculation for the first time. Compared with the conventional continuous control set-model predictive control (CCS-MPC) method, our development not only achieves the smaller overshoot, settling time and steady-state error, but also attenuates the inherent issue of system uncertainties in widely existing industrial application. Finally, the proposed MS-MFPC methodology is experimentally assessed for a PMSM test bench, where steady-state and transient-state performance tests confirm the interest of the proposal.