A novel method of model predictive control on permanent magnet synchronous machine with Laguerre functions

Abstract

This paper aims to speed up the response and alleviate the computing load of model predictive control (MPC) of permanent magnet synchronous machine (PMSM). For this purpose, the Laguerre functions were extended to MPC, creating novel linear controller (LMPC) for the PMSM model. Firstly, the differences of control variables between adjacent sampling periods were regarded as a unit impulse response of a stable system, which can be approximated by a few terms of Laguerre polynomials. In this way, the difference of control variables can converge to zero quickly through parameter adjustment. Then, the current and voltage constraints in the form of Laguerre functions were applied on PMSM to improve the current response to sudden changes of speed or load. Furthermore, the torque load was applied to the state space model of PMSM to optimize the response to external load. Simulation results show that the proposed LMPC controller can effectively reduce the computing complexity that restricts the application of MPC on PMSM.

Keywords

• Permanent magnet synchronous machine (PMSM)
• model predictive control (MPC)
• Laguerre functions
• Constraints