Model Predictive Position Control for a Planar Switched Reluctance Motor Using Parametric Regression Model

Abstract

A model predictive position control (MPPC) method based on a parametric regression model is proposed in this paper, to achieve high-precision positioning for a planar switched reluctance motor (PSRM) developed in the laboratory. First, the mechanism model of the PSRM system represented by a discrete-time state space model is given. To reduce modeling error caused by the uncertainty, a two-order parametric regression model is then used to describe the PSRM. With the thrust force input signal and the position output signal, the parameters of this model are obtained by using a recursive least squares method with forgetting factor. Based on the built model, a predictive model is established to predict the future position. By defining a cost function, an optimized control action sequence is obtained with the predictive model. Additionally, a comparison is performed experimentally. The experimental results verify the effectiveness of the proposed MPPC for high-precision positioning.