Unified Nonlinear Optimization-Based Sensorless Control for Switched Reluctance Machine Drives

Abstract

Rotor position estimation of switched reluctance machines (SRMs) is the main focus of this work. Rotor position sensors are a crucial component of optimal motor controls. Fail-safe operation and system cost reduction have been extensively researched and implemented in industry and academia. Position sensorless control on switched reluctance machines introduces a new challenge due to high nonlinearity under different operating conditions.

A comprehensive review of SRM analytical modeling is presented, detailing each technique's main advantages and drawbacks. A least square-based analytical model (LSA) is proposed, which provides a simpler implementation and improved performance when compared to the methods commonly used in the literature. A literature review of rotor position sensor technology, position sensor fail modes, and position sensorless control is presented, providing a good roadmap of potential development and current limitations of the current technology. A wide speed range sensorless control is usually required when considering fail-safe techniques, fail detection methods, and low-cost applications. A unified nonlinear optimization-based sensorless control is proposed in this thesis, where a single method is used for startup, low and high speeds, with reduced memory allocation where a look-up table is not required, optimal transient response due to the elimination of a phase-locked-loop (PLL), and robustness against parameter variation. The method is validated at a wide speed range and torque conditions, thus showing the performance against conventional methods.