Model reference robust speed control for induction-motor drive with time delay based on neural network

Abstract

Proposes a novel model-reference robust speed control with a load torque estimator and feedforward compensation based on a neural network (NN) for induction motor drives with time delay. First, a two-layer neural network torque estimator (NNTE) is used to provide real-time identification for an unknown load torque disturbance. The backpropagation algorithm was used as the learning algorithm. In order to guarantee the system's convergence and to obtain faster NN learning ability, a Lyapunov function is also employed to find the bounds of the learning rate. Since the performance of the closed-loop controlled induction motor drive is influenced greatly by the presence of the inherent system dead-time during a wide range of operations, a dead-time compensator (DTC) and a model-reference-following controller (MRFC) using a NN proportional controller (NNPC) are proposed to enhance the robustness of the PI controller. A theoretical analysis, simulation and experimental results all demonstrate that the proposed model-reference robust control scheme can improve the performance of an induction motor drive with time delay, and can reduce its sensitivity to system parameter variations and load torque disturbances.