Control of Permanent Magnet Motors with Actuation Bounds using Convex Optimization

TL;DR

This paper introduces a convex optimization-based nonlinear control method for permanent magnet motors that respects actuation bounds, improves response time, and enhances analysis under uncertainties.

Contribution

It proposes a novel feedback linearization control approach using convex optimization to handle actuation bounds efficiently in permanent magnet motors.

Findings

Effective adherence to current and voltage bounds.

Improved response time through convex optimization.

Robustness analysis under model uncertainty and noise.

Abstract

This paper presents a nonlinear control algorithm for speed control of a permanent magnet motor. The idea relies on a feedback linearization technique which also ensures adherence to current and voltage bounds. These bounds arise from practical limitations of the power source. The feedback linearization law is computed using a convex optimization routine to minimize response time as well. The aid of convex optimization leads to computational efficiency. Moreover, the mathematical tractability of the approach also aids analysis of the system performance under model uncertainty and feedback measurement noise. Simulations and computations corroborate the proposed idea.