Robust Adaptive Backstepping Control for Efficiency Optimization of Induction Motors with Uncertainties

Abstract

In this paper, a robust adaptive backstepping control is developed for efficiency optimization of induction motors with uncertainties. The proposed control scheme consists of efficiency flux control (EFC) using a sliding mode adaptive flux observer and robust speed control (RSC) using a function approximation for mechanical uncertainties (such as load torque, friction force and moment of inertia etc.). In EFC, it is important to find the flux reference to minimize power losses of induction motors. Therefore, we proposed the optimal flux reference using the electrical power loss function. The sliding mode flux observer is designed to estimate rotor fluxes and variation of inverse rotor time constant. In RSC, the unknown function approximation technique employs nonlinear disturbance observer (NDO) using fuzzy neural networks (FNNs). The proposed controller guarantees both speed tracking and flux tracking. Simulation results are presented to illustrate the effectiveness of the approaches proposed.