A Discrete Linearization-Based Predictive Intelligence Technique for Direct Torque and Flux Control of Induction Motor Drive with Feedback Linearization

Abstract

This article describes a linearization-based model predictive intelligence control (MPIC) approach incorporated with a feedback linearization (FBL)-based direct torque and flux control (DTFC) of induction motor (IM) drive. This FBL DTFC with MPIC is employed here to analyze the dynamic performance of the IM drive and is compared with the conventional MPIC-based DTFC technique. The intuitive FBL approach develops the decoupled components of torque and flux. Hence, the specific intuitive linearized model-based DTFC-SVM not only preserves all advantages of DTFC but also improves the transient response with a reduced settling time of 30 to 40% along with a substantial reduction in torque and flux ripple by around 78% and 68%, respectively. However, the stability problem to design such a controller is a significant issue, and therefore, it is examined by estimating that the control law is monotonically converging with time. Hence, the proposed solution is highly justifiable and flexible. This proposed controller-based feedback linearized drive is modeled and its dynamic response and robust performance are analyzed by using MATLAB/SIMULINK software as well as extensive experimental results. It is observed from the experimental result that there is a significant improvement in torque and flux ripple of around 78% and 67%, respectively.