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Summary This paper proposes a novel adaptive backstepping control method for parametric strict‐feedback nonlinear systems with event‐sampled state and input vectors via impulsive dynamical systems tools. In the design procedure, both the parameter estimator and the controller are aperiodically updated only at the event‐sampled instants. An adaptive event sampling condition is designed to determine the event sampling instants. A positive lower bound on the minimal intersample time is provided to avoid Zeno behavior. The closed‐loop stability of the adaptive event‐triggered control system is rigorously proved via Lyapunov analysis for both the continuous and jump dynamics. Compared with the periodic updates in the traditional adaptive backstepping design, the proposed method can reduce the computation and the transmission cost. The effectiveness of the proposed method is illustrated using 2 simulation examples.

This paper is concerned with the adaptive event-triggered control problem of nonlinear continuous-time systems in strict-feedback form. By using the event-sampled neural network (NN) to approximate the unknown nonlinear function, an adaptive model and an associated event-triggered controller are designed by exploiting the backstepping method. In the proposed method, the feedback signals and the NN weights are aperiodically updated only when the event-triggered condition is violated. A positive lower bound on the minimum intersample time is guaranteed to avoid accumulation point. The closed-loop stability of the resulting nonlinear impulsive dynamical system is rigorously proved via Lyapunov analysis under an adaptive event sampling condition. In comparing with the traditional adaptive backstepping design with a fixed sample period, the event-triggered method samples the state and updates the NN weights only when it is necessary. Therefore, the number of transmissions can be significantly reduced. Finally, two simulation examples are presented to show the effectiveness of the proposed control method.

This paper studies the fuzzy adaptive output feedback fault-tolerant control problem for a class of single-input and single-output (SISO) uncertain nonlinear systems with timevarying non-affine nonlinear faults in strict-feedback form. In the design procedure, filtered signals are adopted to circumvent algebraic loop problems on implementing the usual controllers. By using fuzzy logic systems to approximate the unknown nonlinearity effects and changes in model dynamics due to faults, a fuzzy state observer is first presented to estimate the unmeasured states. Based on the online estimating information from the adaptive mechanism, an observer-based dynamic output feedback fault tolerant controller is designed via the backstepping technique. It is shown that the stability and tracking performances of the closed-loop system can be achieved even in the presence of unknown nonlinear faults. In comparison with the existing approaches, the FTC scheme can handle the non-affine nonlinear faults effectively. Finally, a simulation example is included for validating the advantages of the proposed approaches.Index Terms-Non-affine nonlinear faults, fuzzy logic systems (FLSs), fuzzy state observer, fault-tolerant control (FTC), output feedback.

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