The asynchronous sliding mode control (SMC) problem is investigated for networked Markovian jump Lur’e systems, in which the information of system modes is unavailable to the sliding mode controller but could be estimated by a mode detector via a hidden Markov model (HMM). In order to mitigate the burden of data communication, an event-triggered protocol is proposed to determine whether the system state should be released to the controller at certain time-point according to a specific triggering condition. By constructing a novel common sliding surface, this paper designs an event-triggered asynchronous SMC law, which just depends on the hidden mode information. A combination of the stochastic Lur’e-type Lyapunov functional and the HMM approach is exploited to establish the sufficient conditions of the mean square stability with a prescribed ${H}_{\infty }$ performance and the reachability of a sliding region around the specified sliding surface. Moreover, the solving algorithm for the control gain matrices is given via a convex optimization problem. Finally, an example from the dc motor device system is provided.

An Event-Triggered Approach to Sliding Mode Control of Markovian Jump Lur’e Systems Under Hidden Mode Detections

In this paper, we consider the distributed filtering problem for continuous-time stochastic systems over sensor networks subject to Markovian switching topologies. Due to limited communication energy and bandwidth, an event-based communication scheme is proposed with the aim to decrease the transmission frequency. An individual triggering condition is put forward to regulate the communication rates for each component of the system state in order to better reflect the engineering requirements. The aim of this paper is to design a distributed filter over sensor networks with Markovian switching topologies such that the dynamics of the estimation error is exponentially mean-square bounded. It is shown that, with the proposed event-based distributed filtering algorithm, the exponential mean-square boundedness of the estimation errors is guaranteed if the sensor network is distributively detectable and the combined communication topology is strongly connected. A numerical example is presented to illustrate the usefulness of the developed algorithm.

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Event-Based Distributed Filtering Over Markovian Switching Topologies

Synchronization criteria for inertial memristor-based neural networks with linear coupling.

Exponential stabilization of switched time-varying systems with delays and disturbances

In this article, the event-triggered-based adaptive neural network control problem is studied for a class of nonlinear time-delay systems with nonstrict-feedback structures and unknown control directions. First, a compensation system is introduced to handle the input delay and an observer is also designed to estimate the unmeasurable states. Then, by employing the neural networks and the variable separation approach, the adaptive backstepping method is applied to control the nonlinear systems with nonstrict-feedback structures. By codesigning the adaptive controller and the triggering mechanism, the input-to-state stability (ISS) assumption with respect to the measurement error is removed. Finally, it is shown that the proposed event-triggered adaptive controller can ensure the semiglobal boundedness of all the states in the closed-loop systems.

Yanan Li

Papers

New criteria for exponential stability of switched time-varying systems with delays and nonlinear disturbances

Exponential stabilization of switched time-varying systems with delays and disturbances