This paper addresses the event-triggered dynamic output feedback control for switched linear systems with frequent asynchronism. Different from existing work, which limits at most once switching during an inter-event interval, we adopt the average dwell time approach without limiting the minimum dwell time of each subsystem, and thus frequent switching is allowed to happen in an inter-event interval. Since the difficulty in acquiring the full information of system states, the dynamic output feedback controller is taken into account to stabilize the switched system. By employing a controller-mode-dependent Lyapunov functional, stability criterion is proposed for the resulting closed-loop system, based on which the dynamic output feedback controller together with the mode-dependent event-triggered mechanism is co-designed. Besides, the existence of the lower bound on inter-event intervals is attentively discussed, which gets rid of the Zeno behaviour. Finally, the effectiveness of the proposed method is illustrated by numerical simulations.

This paper is concerned with the exponential synchronization for master-slave chaotic delayed neural network with event trigger control scheme. The model is established on a network control framework, where both external disturbance and network-induced delay are taken into consideration. The desired aim is to synchronize the master and slave systems with limited communication capacity and network bandwidth. In order to save the network resource, we adopt a hybrid event trigger approach, which not only reduces the data package sending out, but also gets rid of the Zeno phenomenon. By using an appropriate Lyapunov functional, a sufficient criterion for the stability is proposed for the error system with extended (X , Y, Z)-dissipativity performance index. Moreover, hybrid event trigger scheme and controller are codesigned for network-based delayed neural network to guarantee the exponential synchronization between the master and slave systems. The effectiveness and potential of the proposed results are demonstrated through a numerical example.

This work is concerned with dynamic output feedback control and filtering problem for a class of switched systems with mode-dependent average dwell time switching. By constructing a quasi-time-dependent Lyapunov function, the issues of global uniform asymptotic stability and $\mathcal{l}-2$-gain analysis for the switched system are addressed firstly. Then a set of reduced-order output feedback controller is designed, which is both mode-dependent and quasi-time-dependent, and the quasi-time-dependent filter is also presented. Compared with time-independent criteria, the new results greatly reduce the conservatism of the existing results. The effectiveness and merits of the proposed methods are illustrated with three examples.

The H∞ filter design for a class of switched Takagi-Sugeno (T-S) fuzzy systems with persistent dwell time (PDT) is investigated in this paper. The considered switched fuzzy systems contain a limited number of subsystems and each local subsystem is represented by the well-known T-S fuzzy model. Compared with the dwell time (DT) switching or average DT switching that attracted quantities of interests over the last decade, the PDT switching considered in this paper is known to be more general. The stability and L₂-gain analysis for switched systems with PDT switching are derived first, based on which a set of full-order H∞ filter is designed to guarantee the global uniform asymptotic stability with a prescribed nonweighted H∞ noise attenuation performance for the resulting filtering error system. Finally, the effectiveness of the provided method is illustrated with an example.

Controllable distributed resources can offer great potential benefits to the power systems since they possess considerable operation flexibility. However, a high-dimensional mathematical problem is emerged when modeling the massive distributed resources (DRs) with heterogeneous parameters. In light of this, based on the aggregation and disaggregation of massive DRs of small capacity, a model predictive control (MPC) based strategy considering incremental operation cost of various controllable devices is proposed for the real-time secondary frequency regulation in an islanded microgrid. The proposed strategy is implemented in a distributed framework using a neurodynamic-based approach, which only requires the information exchange among neighboring units. Simulation results illustrate that the proposed strategy can efficiently manage massive DRs to maintain the system frequency and achieve a satisfactory economic performance, which indicates its promising application value in the field of microgrid frequency regulation.