Model Predictive Control of Induction Motor Drives for High-Dynamic Industrial Motion Systems

Abstract

Induction motor drives have been widely utilised in the industrial motion control systems because they are hardy, reliable and cost effective. Nonetheless, the high dynamic performance of operations at a changing load with fast rates is a significant challenge of conventional control methods. The classic proportional integrated (PI) controllers are usually associated with slower transient response, overshoot and lower disturbance rejection ability thereby limiting their performance in highs performance industrial use. This paper introduces a model predictive approach to control of induction motor drives to enhance the dynamic behaviour and speed control in industrial motion systems. The given method suggests the use of a discrete-time mathematical model of the induction motor in order to predict the further system behaviour based on the finite prediction horizon. The optimal control action minimising the speed tracking error whilst holding the motor action constant is determined using an optimization-based cost function. The predictive controller exists as a MATLAB/Simulink simulation platform to measure the system behaviour under different operating conditions, such as a change in the reference speed, and load perturbation. The outcomes of simulations prove that the drive system using MPC is more effective with respect to faster speed tracking, lower overshoot, and shorter settling time than the traditional PI-based drive system. The outcomes also mean better disturbance rejection and less bumpy torque reaction. These results verify that model predictive control offers a California solution in terms of effectiveness and reliability in terms of control over highly dynamic industrial motion systems that need high accuracy and robustness in motor drive operation.