Observer Based Adaptive Fault-Tolerant Control of Switched Nonlinear Systems

An adaptive fault-tolerant control strategy is proposed for a class of switched nonlinear systems with unknown functions and unavailable states by using the improved average dwell time technique and backstepping method. We use radial basis function neural networks (RBFNNs) to approximate the unknown functions and a switched nonlinear state observer is designed to estimate the unavailable states. In order to solve the explosion of the complexity problem, dynamic surface control technique is used. The presented fault-tolerant controllers and update laws guarantee the boundedness of all signals in the closed-loop system under a class of switching signals with average dwell time. Furthermore, the system output can track a given reference signal and the estimation errors converge to some small compact sets. Finally, an example is employed to illustrate the effectiveness of the proposed method.