Optimal speed tracking control of induction motor using artificial intelligence techniques

This paper presents a novel neural network based control architecture which use on line training to identify and control the nonlinear induction motor. The aim of this control is to force the shaft speed to follow a prescribed trajectory. The architecture incorporates two artificial neural networks and a fuzzy logic controller. Accepting that not all elements of the state are measurable, the first ANN is used as observer to give an estimate of the state. A state space description is applied, and the trained nonlinear innovation state space model of the motor is used. Since the motor is nonlinear, and since the observer, as well as, the controller (second ANN) are trained based on optimal criteria, the method is named non linear quadratic Gaussian. A fuzzy logic controller is used to provide an inner loop inspired by conventional vector control strategy. Simulated results are presented to validate the proposed architecture showing that speed control is stable, rapid to stabilize, and insensitive to parameter uncertainty and load disturbance.