Adaptive neural control of non-strict feedback system with actuator failures and time-varying delays
Abstract This paper focuses on an adaptive neural control for a category of nonlinear uncertain time-delay systems with actuator failures. By employing the structural characteristics of radial basis function (RBF) neural networks (NNs), a backstepping design method is extended from strict-feedback systems to a category of more general nonlinear systems. By applying the approximation ability of neural network systems, an integrated adaptive controller is constructed, which can adapt to both system uncertainties and unknown actuator failures. The proposed adaptive neural controller guarantees that the system output converges into a small neighborhood of the reference signal, and all the signals of the closed-loop system remain bounded. A numerical example is given to verify the validity of the proposed approach.