Speed Tracking of a Linear Induction Motor - Enumerative Nonlinear Model Predictive Control

TL;DR

This paper introduces a novel model predictive control method for linear induction motors that improves speed tracking accuracy while significantly reducing inverter switching frequency, addressing key limitations of traditional direct torque control.

Contribution

The paper proposes a new enumerative nonlinear model predictive control approach that directly manages inverter switches, enhancing performance and reducing switching frequency compared to existing methods.

Findings

Achieves comparable speed tracking performance to traditional methods.

Reduces inverter switching frequency by approximately 95%.

Easily implementable in real-time applications.

Abstract

Direct torque control is considered as one of the most efficient techniques for speed and/or position tracking control of induction motor drives. However, this control scheme has several drawbacks: the switching frequency may exceed the maximum allowable switching frequency of the inverters, and the ripples in current and torque, especially at low speed tracking, may be too large. In this paper we propose a new approach that overcomes these problems. The suggested controller is a model predictive controller which directly controls the inverter switches. It is easy to implement in real time and it outperforms all previous approaches. Simulation results show that the new approach has as good tracking properties as any other scheme, and that it reduces the average inverter switching frequency about 95% as compared to classical direct torque control.