This study considers the periodic event-triggered prescribed tracking problem for stochastic nonlinear systems, whose output is available only at sampling time. With the limited sampled data of output, a state observer via neural-network approximation is constructed to estimate the unmeasurable states, and then a novel event-triggered mechanism is designed by monitoring the estimated states at sampling time to avoid the continuous communication. The negative deviation effects between the event-triggered controller and the continuous controller are eliminated by introducing two intermediate sampling deviation terms. Moreover, a performance function is introduced to achieve more flexible tracking performance. This function represents different performance behaviors and addresses the issue of redesigning controllers. By determining an allowable sampling period, it is proven that all states of the closed-loop system are semiglobally uniformly ultimately bounded, and the tracking error satisfies a flexible prescribed performance. Finally, two examples verify the effectiveness.

This article addresses the secure finite-time tracking problem via event-triggered command-filtered control for nonlinear time-delay cyber physical systems (CPSs) subject to cyber attacks. Under the attack circumstance, the output and state information of CPSs is unavailable for the feedback design, and the classical coordinate conversion of the iterative process is incompetent in relation to the tracking task. To solve this, a new coordinate conversion is proposed by considering the attack gains and the reference signal simultaneously. By employing the transformed variables, a modified fractional-order command-filtered signal is incorporated to overcome the complexity explosion issue, and the Nussbaum function is used to tackle the varying attack gains. By systematically constructing the Lyapunov–Krasovskii functional, an adaptive event-triggered mechanism is presented in detail, with which the communication resources are greatly saved, and the finite-time tracking of CPSs under cyber attacks is guaranteed. Finally, an example demonstrates the effectiveness.

In this paper, the issue of finite-time fault-tolerant tracking control via event-triggered strategy is investigated for a general class of non-affine systems with a semi-bounded structure. The differentiable condition imposed on the non-affine terms is removed. A new model transformation method is used to transform the non-affine system into a pseudo-affine one, which generates an uncertain model with the undesired overflowed variables. We utilize the neural networks to approximate the uncertain functions and separate the overflowed variables to guarantee the solvability of virtual controllers in the iterative design. In addition, the tracking error is limited to a specified range within a finite time by the introduced prescribed performance. By considering the actuator fault and the limited communication resources, an event-triggered fault-tolerant control scheme is proposed, which not only ensures finite-time tracking, but also avoids the Zeno behavior and reduces the waste of communication resources. Finally, the proposed method is used for the microgrid systems.

In this article, by using the neural-networks (NNs) separation and approximation technique, an adaptive scheme is presented to deliver the prescribed tracking performance for a class of unknown nonaffine switched nonlinear time-delay systems. The nonaffine terms are indifferentiable and the controllability condition is not required for each subsystem, which allows the considered tracking problem to not be efficiently solved by the traditional adaptive control algorithms. To solve the problem, NNs are utilized to separate and approximate the nonaffine functions, and then the dynamic surface control and convex combination method are utilized to construct a controller and a switching strategy. In addition, an adaptive law is considered for each subsystem to reduce the conservativeness. Under the designed controller and switching strategy, all the signals of the resulting closed-loop system are bounded, and the tracking performance is achieved with a prescribed level.

This article considers the problem of adaptive prescribed performance tracking control via slow switching for a general class of uncertain switched nonlinear systems (SNSs) with unmodeled dynamics (UDs) and nonstrict-feedback structure. The UDs are not in their form of the input-to-state practical stability and their state information is unmeasurable. By reassigning the function variables, the coupling effects between UDs and the prescribed performance function are eliminated through the iterative process. In virtue of the neural networks (NNs) approximation capability, a novel adaptive backstepping procedure is proposed without adding extra first-order filters. By choosing an appropriate slow switching law, all the signals of the closed-loop system are bounded, and the system output tracks the reference signal with a prescribed performance level (PPL).

This paper solves the problem of fuzzy adaptive robust output feedback control using average dwell-time switching for a class of stochastic switched nonstrict feedback nonlinear systems with more general uncertainties, including unmodeled dynamics, unknown control coefficients, unknown drift/diffusion terms, and unknown output functions. The unmodeled dynamics and drift/diffusion terms are allowed to depend on the whole unmeasurable states, whose effect is difficult to counteract. By virtue of the fuzzy approximation capability and variable partition technique, an adaptive backstepping procedure is proposed to compensate these uncertainties. Based on the constructed switched observer, which shows its strong robustness to the unknown control coefficients and output functions, an output feedback controller is designed to render all the signals of the closed-loop system bounded in probability under an appropriately chosen average dwell time.