Online Parameters Estimation and Autotuning of a Discrete-Time Model Predictive Speed Controller for Induction Motor Drives

Abstract

This paper proposes a new method that can online and automatically estimate and fine-tune the parameters of a discrete-time model predictive controller for providing high-performance speed control in an induction motor (IM) drive. The suggested control system combines the model reference adaptive method with the fuzzy-logic technique, and its operation can be initiated without requiring any human intervention or the knowledge of the motor drive parameters. Therefore, no extra work by the user is needed to adjust the controller parameters according to the operating conditions, and also high performance is attained because any variations of the system model can be considered through a fine-tuning procedure. The proposed autoadaptive discrete-time model predictive control (ADMPC) system is based on the optimization of an objective function that considers the reference and the real speed as well as the acceleration of the IM drive by using the state-space model. The implementation of the proposed ADMPC scheme is easy, since no additional hardware is required, but only the replacement of the firmware of the IM drive. Selective simulation and experimental results are presented to validate the effectiveness of the proposed ADMPC system and demonstrate the high performance of the motor drive.