Classical Model Predictive Control of a Permanent Magnet Synchronous Motor

TL;DR

This paper presents a novel model predictive control scheme for a permanent magnet synchronous motor that optimizes torque and efficiency through advanced optimization techniques, enabling fast, smooth, and efficient high-speed operation.

Contribution

It introduces a suboptimal MPC algorithm based on differential flatness and linear programming for PMSMs, enhancing constraint handling and dynamic field-weakening capabilities.

Findings

Achieves fast and smooth torque control with optimized efficiency.

Demonstrates improved constraint handling through dynamic field-weakening.

Validates performance with experimental and numerical results.

Abstract

A model predictive control (MPC) scheme for a permanent-magnet synchronous motor (PMSM) is presented. The torque controller optimizes a quadratic cost consisting of control error and machine losses repeatedly, accounting the voltage and current limitations. The scheme extensively relies on optimization, to meet the runtime limitation, a suboptimal algorithm based on differential flatness, continuous parameterization and linear programming is introduced. The multivariable controller exploits cross-coupling effects in the long-range constrained predictive control strategy. The optimization results in fast and smooth torque dynamics while inherently using field-weakening to improve the power efficiency and the current dynamics in high speed operation. As distinctive MPC feature, constraint handling is improved, instead of just saturating the control input, field weakening is applied dynamically to bypass the voltage limitation. The performance of the scheme is demonstrated by experimental and numerical results.