Adaptive recurrent neural control for nonlinear system tracking
Miguel Bernal, Edgar Nelson Sanchez · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · 2000
We present a new indirect adaptive control law based on recurrent neural networks, which are linear on the input. For the identifier, we adapt a recently published algorithm to fit the neural network type used for identification; this algorithm ensures exponential stability for the identification error. The proposed controller is based on sliding mode techniques. Our main result, stated as a theorem, concerns tracking error asymptotic stability. Applicability of the proposed scheme is tested via simulations.