Performance analysis of a robust MF-PTC strategy for induction motor drive

Abstract

The load model-based conventional predictive torque control (PTC) strategy performs as a high-performance controller at transient and steady-state conditions. However, its performance is poor on the issue of parameters variation. A robust model-free PTC (MF-PTC) strategy has been proposed in this paper to overcome the aforementioned drawback. An auto-regressive exogenous (ARX) model instead of a load model has been used to establish a model-free controller. This model is usually formed based on their input-output transfer function. A recursive least square algorithm has been employed to estimate the unknown parameters of the ARX model. Then, an observable canonical state-space model uses those estimated parameters to achieve an accurate prediction of the control variables. The performance of the proposed scheme can be affected by the variation of resistance. A resistance estimator based on the model reference adaptive system observer has been applied to improve the robustness of the system against variation of the stator and rotor resistance, and inductance measurement uncertainty. Simulation results show that the proposed MF-PTC scheme is robust against parameters uncertainty and works well at transient and steady-state conditions.