This article investigates the switching control problem of active vehicle suspension systems (AVSSs) operating over a bandwidth-limited controller area network (CAN). To characterize an AVSS under varying operating conditions, it is modeled as a multimode switched system, where each mode corresponds to a specific vehicle body height. On this basis, a design framework relying on multiple time-varying Lyapunov functions is developed to construct a sampled-data, state-dependent switching (SDS) law that alleviates the communication burden on the CAN while improving suspension performance. To further enhance the utilization of communication resources, a dynamic event-triggered scheme (ETS) is proposed, in which data transmission is scheduled using only sampled state information, thereby eliminating the need for continuous monitoring of the triggering condition. Under the proposed switching law and dynamic ETS, a set of numerically tractable sufficient conditions are established that simultaneously guarantees the desired suspension performance and communication efficiency. The proposed strategy is validated through detailed numerical simulations and hardware-in-the-loop (HIL) experiments, which demonstrate notable improvements in ride comfort and a significant reduction in communication load. In particular, the HIL experiments show that, under pulse road excitation, the proposed controller achieves a 69.05% reduction in vehicle body acceleration compared with a passive suspension.

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.

This paper proposes a novel dynamic event-triggered mechanism (DETM) of a switched linear DC microgrid with intermittent denial-of-service (DoS) attacks. In a switched linear system based on DETM, the subsystem mode is immediately updated along with the occurrence of switching signals, while the controller mode is updated after a successful event-triggering. This means both communication interruptions and periodic sampling may cause asynchronous switching situations, which together pose a challenge for stability analysis. To this end, a multiple Lyapunov function that can simultaneously describe subsystem mode and controller mode is adopted, and a new DETM based on acknowledgement character technique (ACK) is studied to ensure input-to-state stability (ISS) of the system. Meanwhile, the maximum allowed DoS duration is first studied to further analyze the relationship between DoS parameters, event-triggered parameters, and system stability. By utilizing ACK, the system can detect the end of the DoS attack and remain ISS under dynamic event-triggered control. Finally, simulation verification is conducted in a switched DC microgrid.

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).