Finite-Control Set Model Predictive Control Method for Torque Control of Induction Motors Using a State Tracking Cost Index

Abstract

This paper presents a novel torque control method for two-level-inverter-fed induction motor drives. The control principle is based on a finite-control set model predictive control (FCS-MPC) using a state tracking cost index. In the online procedure of the proposed FCS-MPC, the optimal voltage vector and its corresponding optimal modulation factor are determined based on the principle of torque and rotor flux error minimization. In this method, a reference state is determined in a systematic way so that the reference torque tracking with maximum torque per ampere and flux-limited operation could be achieved. In addition, a weighting matrix for the state tracking error is optimized in offline using the linear matrix inequality based optimization problem. The efficacy of the proposed FCS-MPC method is proved by the simulation and experimental results at different working circumstances. The comparison of the presented control system with the conventional FCS-MPC and with other reported FCS-MPC with modulation control is made. The proposed algorithm yields fast dynamic performance and minimum torque and current ripples at different speed and torque levels.