No abstract

In this paper, a finite‐time adaptive fault‐tolerant control (FTC) scheme is researched for a class of nonlinear systems with unmodeled dynamics, where the dynamic system is switched. Due to the complexity of the system and switching characteristics, it is not easy to solve the tracking problem in a finite time adopting existing approaches. In this study, a new FTC method is presented. This is completed by using the backstepping technology, common Lyapunov function method, and radial basis function (RBF) neural networks (NNs) which are utilized to estimate unknown functions. It is shown that all signals in the closed‐loop system are semi‐global bounded under arbitrary switching, and the tracking error converges to a small region around the origin in a finite time. An effective simulation example is given to prove the practicability of the designed scheme in the end.

No abstract

No abstract

In this article, an adaptive neural fault-tolerant decentralized tracking control problem is investigated for switched stochastic large-scale uncertain nonlinear systems, in which time-varying delay and unknown control coefficient are under consideration. By incorporating the quadratic Lyapunov function and Lyapunov Krasovskii function into the backstepping approach, an adaptive neural fault-tolerant decentralized control strategy is proposed, which ensures that signals in the closed-loop system are stable in probability and the tracking error converges to a small neighborhood of the origin under arbitrary switching. At last, to verify the presented strategy is feasible, a simulation example is given.