This article considers the tracking control of nonlinear system with state constraints and faults. A fuzzy tolerant control approach on adaptive sliding-mode observer is proposed to accomplish good tracking performance. Firstly, for the purpose of getting the unknown state, a state estimator based on the fuzzy logic systems (FLSs) is designed. Secondly, a fuzzy observer by variable structure method is posed to approximate the faults, and the finite time convergence of observation error is proven. Furthermore, a two-layer nested adaptive law is constructed to improve the observer. Thirdly, a fuzzy backstepping tolerant controller based on super-twisting differentiator is proposed. In which, the barrier Lyapunov function is designed to counter the state constraints, and the super-twisting estimator is employed to estimate the derivative of virtual control input. Finally, two simulation results are analyzed to demonstrate the potential of the developed schemes.

In this paper, the problem of tracking control of networked control systems with false data injection attacks is studied. A new terminal integral adaptive sliding mode control method is proposed. Firstly, the mathematical model of networked control system with the actuator and sensor false data injection attacks is modeled. Secondly, an augmented state observer is designed in order to estimate the augmented states, in which, a discontinuous input term is designed to defenses the actuator attack, and the convergence of the estimation error is proved. Thirdly, the tracking output errors are introduced to design the terminal integral sliding mode control method. Furthermore, in order to reduce the oscillation induced by the sensor false data injection attack, the terminal integral adaptive sliding mode control algorithm using the estimation error as adaptive factor is structured, and the stability of the algorithm are proved by using Lyapunov theory. Finally, a numerical simulation and a practical experiment are executed to verify the superiority of proposed control strategies.