Single-neuron adaptive double-power super-twisting sliding mode control for induction motor

Abstract

Direct torque control is a widely used control method for induction motors because it offers rapid dynamic response and relatively simple implementation. However, it presents high torque and flux ripples and variable switching frequencies. To overcome these constraints, the double-power super-twisting sliding mode (DPSTSM) control approach has been proposed, integrating the advantages of the super-twisting algorithm designed to reduce chattering with those of the double power convergence law aimed to improve system speed and dynamic quality. However, the optimal tuning of the sliding mode gains of the double-power super-twisting sliding mode controller represents a considerable challenge. To address this issue, we proposed an improvement to the DPSTSM algorithm through the integration of a single-neuron adaptive algorithm. The single-neuron adaptive double-power super-twisting sliding mode control approach aims to dynamically adjust the controller gains, while delivering superior performance in terms of chattering reduction, improved dynamic response, and enhanced robustness to load disturbances. A detailed investigation was carried out via MATLAB/Simulink simulations to determine the effectiveness of the proposed control strategy.