This paper is concerned with the sensor-network-based distributed control of large-scale systems with the power constraint. In the underlying system, the measurement is firstly sampled under nonuniform sampling periods, which are varying in a given set. Then, the measurement size reduction technique and communication rate reduction method are used to save the constrained power in sensor networks. Specifically, only one element of sampled measurement is chosen at each sampling time instant, and it is then quantized and transmitted to the neighbouring controllers. Based on the switched system approach, a unified model is presented to capture the nonuniform sampling, the measurement size reduction, the transmission rate reduction and the controller failure phenomenon. A new sufficient condition is obtained such that the filtering error system is exponentially stable in the mean-square sense with a prescribed H 1 performance level. Based on this condition, the controller gains are designed by using the cone complementarity linearization algorithm. Finally, a simulation study is given to show the effectiveness of the proposed new design technique. design algorithms have been presented. Also, the switched system approach was proposed in [11] to model the NCS with time-delay, and the explicit relation between the control performance and the occurrence frequency of long delay has been established. For more latest works on NCSs, readers can refer to [12][13][14][15][16][17][18][19][20][21][22][23] and the references therein.On the other hand, the last decades have witnessed an increase interest in modelling and controlling the dynamical systems with large scale and complexity. A large-scale system is often considered as a set of interconnected subsystems and referred to as large-scale interconnected systems. The so-called large-scale systems arise in electric power grids, transportation networks and industrial process systems [24]. There are three main algorithms to control the large-scale systems, that is, the centralized, decentralized and distributed controls. In recent years, the distributed control has emerged as an attractive control methodology to handle the scale and interactions of largescale complex systems, see [25,26] and the references therein. Specifically, thanks to the recent advances of communication technology, the distributed stabilization of network-based large-scale systems has received much research attention. In [27], the communication delay and packet dropout were the main concerns in the stability of networked large-scale system. A model-based networked distributed control framework was proposed to stabilize the system consisting of discrete-time subsystems interconnected through their states. To compensate for the adverse effects of the aforementioned two network-induced shortcomings, an interaction estimator was provided in each local controller unit. The estimator used the explicit model of the subsystems to estimate the evolution of the states of interacting subsystems, when information about ...
