Adaptive Output Feedback Control for a Class of Uncertain Nonlinear Systems with Actuator Failures

In this paper we proposed an adaptive output feedback control for actuator failures compensation for a class of uncertain nonlinear systems. Firstly, we assume full state availability and design an adaptive state feedback control scheme based on dynamic surface control (DSC) technique; where radial basis function neural networks (RBF NNs) are incorporated to compensate system uncertainties. Then, based on the separation principle, an adaptive output feedback controller is obtained replacing in the state feedback control law, the states of the system with the states estimation provided by a high-gain observer. We concern about systems with uncertain locally Lipschitz nonlinearities. It is proven that all closed-loop signals are uniformly ultimately bounded (UUB) and the system output tracks a reference signal with small error, for all but one failed actuators. A simulation example is carried out to illustrate the performance of the control scheme.