This paper investigates the robust control problem of nonlinear systems with unknown time-varying actuator faults over digital communication networks. In the study, a new adaptive sliding mode control (SMC) scheme is developed for the investigated nonlinear systems, where the unknown nonlinearity is approximated via the adaptive fuzzy mechanism in the presence of signal quantization. The proposed control law can compensate the time-varying faults and the quantization errors completely by injecting the quantizer parameter into the controller gain. Moreover, in this design, the adaptive fuzzy law is constructed via the quantized state information instead of using the exact original information. Under the proposed quantized SMC scheme, the closed-loop control systems are guaranteed to be asymptotically stable, and the reachability of the sliding surface can be ensured strictly. Finally, two examples are presented to show the effectiveness of the method.

This paper is concerned with the fault estimation (FE) problem for continuous-time descriptor switched systems with unknown external disturbances, actuator, and sensor faults. In this paper, the multiplicative actuator fault is decoupled into the normal term and the fault term, where the actuator fault term and sensor faults can be extended into a new augmented vector. Then, a novel FE approach is developed for descriptor switched systems subject to the switching actions and state-inconsistence phenomena. By the parameter design, the proposed method can eliminate the effect of faults and disturbances, and achieve the accurate estimations of states and faults simultaneously. Finally, an example of circuit system is presented to show the effectiveness of the proposed method.

In the paper, an adaptive fault estimation approach is proposed for a class of switched nonlinear systems. The considered system is assumed to possess unknown nonlinearities, unmeasured states, and simultaneous sensor and actuator faults. Fuzzy logic systems are applied to approximate the unknown nonlinear terms. Two new adaptive fuzzy observers are designed where the sensor and actuator faults can be estimated separately. On the basis of average dwell time approach and Lyapunov stability theory, the resulting error system is proved to be bounded stable with designed parameters. Finally, a simulation example is provided to illustrate the effectiveness of the designed observers.

This paper is concerned with the fault reconstruction observer design problem with unknown nonlinearities, external disturbances and faults. First, a neural-network-based fault estimation approach is developed to generate the estimations of the actuator failures. In this design, the neural network strategy is utilized to approximate the totally unknown nonlinear functions. Then, an iterative adaptive observer is designed to offer the accurate estimations of the sensor faults, where the estimations in the previous iteration are applied in the current iteration to guarantee the convergence of sensor fault estimation errors. The developed neural network observer design approach can reconstruct the states, actuator and sensor faults for the unmanned surface vessel simultaneously. Finally, a practical example of the unmanned surface vessel is presented to illustrate the effectiveness and the potential of the proposed observer technique.

This article addresses the simultaneous actuator and sensor fault estimation (FE) problem for a class of Markovian jump systems (MJSs) with nondifferentiable actuator failures. In order to overcome the difficulties brought by the nondifferentiable actuator failures, we construct an extended vector composed of states, sensor faults, and disturbances, where the derivatives of actuator failures are not required in this augmented system. Then, two novel observer-based approaches are developed for the augmented descriptor system to cope with the FE problem. The first one is a reduced-order FE observer, where the actuator failures can be estimated by the algebraic input reconstruction strategy. The second one refers to an iterative learning observer (ILO) design method, which can obtain the accurate FE result by integrating the estimations in the iterative processes. The two proposed FE observer design methods can avoid the sliding surface switching problem produced by sliding-mode observers in the area of MJSs. Finally, a practical example of the F-404 aircraft engine system is presented to show the validity of the proposed FE observer design techniques.

This study is concerned with the adaptive neural network (NN) observer design problem for continuous-time switched systems via quantized output signals. A novel NN observer is presented in which the adaptive laws are constructed using quantized measurements. Then, persistent dwell time (PDT) switching is considered in the observer design to describe fast and slow switching in a unified framework. Accurate estimations of state and actuator efficiency factor can be obtained by the proposed observer technique despite actuator degradation. Finally, a simulation example is provided to illustrate the effectiveness of the developed NN observer design approach.