An adaptive fuzzy-based fault tolerant control method is first proposed for the discrete-time switched nonlinear systems. In this article, the actuator fault is considered, which contains the loss of effectiveness fault and bias fault. Both the effectiveness factor and the bias signal are unknown but bounded. Moreover, there are unknown internal dynamics in the considered system. In order to solve this problem, the fuzzy logic systems are exploited to provide an approximate construction for the unknown dynamics. Then, under arbitrary switching signals, the designed controller and adaptive laws ensure the boundedness of signals appearing in the considered system, and the system states can track the reference signals. Finally, an example shows the efficiency of the developed approach.

In this paper, an adaptive sliding mode control method based on neural networks is presented for a class of manipulator systems. The main characteristic of the discussed system is that the output variable is required to keep within a constraint set. In order to ensure that the system output meets the time-varying constraint condition, the asymmetric barrier Lyapunov function is selected in the design process. According to Lyapunov stability theory, the stability of the closed-loop system is analyzed. It is demonstrated that all signals in the resulted system are bounded, the tracking error converges to a small compact set, and the system output limits in its constrained set. Finally, the simulation example is used to show the effectiveness of the presented control strategy.