Incremental model predictive control of PMSM based on parameter tuning of multi-layer perceptron neural network and disturbance observer

To enhance the control performance of permanent magnet synchronous motors (PMSM) under complex operating conditions such as sudden load changes and parameter perturbations caused by strong external disturbances, this paper proposes an incremental model predictive control (IMPC) strategy based on a disturbance observer (DOB), referred to as DOB-IMPC. Firstly, an incremental predictive model of PMSM is constructed, taking advantage of its inherent integral characteristic to improve the system’s robustness. Secondly, a DOB is introduced to estimate the aggregated disturbances, including load disturbances and parameter uncertainties in real time, and the estimated values are fed forward to compensate for the predictive model. Subsequently, an internal optimization process of IMPC is designed based on this model, and the optimal control input sequence is obtained by solving a constrained quadratic programming problem. For the parameter setting of the IMPC itself and the additional tuning burden introduced by the DOB, conventional approaches relying on empirical expertise and repeated trial-and-error are inefficient and cannot readily guarantee optimal control performance. This paper develops a parameter-tuning mechanism based on a multi-layer perceptron (MLP) neural network, which integrates the real-time state variables of the PMSM with the overall structural information of the DOB-IMPC framework to achieve online tuning of key control parameters. Simulation and experimental results demonstrate that, compared with conventionally manually tuned MPC, the MLP-based tuning strategy reduces the speed root mean square error (RMSE) by 46.2% and the maximum speed fluctuation by 29.0% while maintaining zero overshoot. With the further incorporation of the DOB, the maximum speed fluctuation under sudden load disturbance is further reduced by 31.2% compared with MPC, and the speed RMSE under parameter perturbation conditions is reduced by 75.8%. Collectively, these results confirm that the proposed strategy delivers measurable gains in dynamic response, disturbance rejection, and parametric resilience—without compromising stability or implementation feasibility.