Prediction Error Compensation-based Model Predictive Position Control of Double-sided Linear Switched Reluctance Motor

In this paper, the model predictive control method based on prediction error compensation is proposed for the double-sided linear switched reluctance motor (DLSRM), to solve the negative effect of an inaccurate prediction model and to improve the precision of position control. Firstly, the structure and the discrete state space model of the DLSRM are given. Then, the position prediction model is established using the discrete state space model and the cost function is defined to develop the model predictive control (MPC) method for the DLSRM. In addition, two kinds of prediction error compensators based on the proportional-integral control method are constructed, where one uses the error between the actual and prediction positions as input, and the other applies the error between the reference and prediction positions as input. The outputs of the error compensation and the MPC comprise the control action of the DLSRM. Finally, the effectiveness of the proposed methods is verified via simulation and experiment results.

I. Introduction

Compared with rotary motors, linear motors have advantages such as low wear, simple structure, low noise, and fast response speed. Linear switched reluctance motors (LSRMs) are based on the principle of minimum reluctance and feature a simple yet robust structure with low cost, high temperature resistance, and high reliability. These characteristics make LSRMs have broad market application prospects. However, due to their unique double salient pole structure and the influence of switching mode and magnetic circuit saturation nonlinearity, LSRMs suffer from significant thrust pulsation issues that result in poor control effectiveness. This limitation severely hampers the development and application of LSRMs in high-precision position control fields.