Robust backstepping control of induction motors using neural networks

In this paper, we present a new robust control technique for induction moto rs using neural networks (NNs), The method is systematic and robust to para meter variations. Motivated by the well-known backstepping design technique , we first treat certain signals in the system as fictitious control inputs to a simpler subsystem, A two-layer NN is used in this stage to design the fictitious controller. Then we apply a second two-layer NN to robustly rea lize the fictitious NN signals designed in the previous step. A new tuning scheme is proposed which can guarantee the boundedness of tracking error an d weight updates, A main advantage of our method is that we do not require regression matrices, so that no preliminary dynamical analysis is needed, A nother salient feature of our NN approach is that the off-line learning pha se is not needed. Full state feedback is needed for implementation. Load to rque and rotor resistance can be unknown but bounded.