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

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