H_∞filtering for discrete-time singular networked systems with communication delays and data missing

H∞ deconvolution filter design for uncertain descriptor hybrid delay systems with limited communication capacity and unknown transition rates

“…Until now, some significant topics on descriptor systems like stability and stabilization, controller problem, Correspondence to: Guangming Zhuang, E-mail: zgmtsg@ 126.com filtering problem, etc, have been widely studied and lots of meaningful results have been reported, for instance, [13][14][15][16][17][18], and the references therein. Recently, descriptor hybrid system, also known as singular Markovian jump systems (SMJSs) have attracted much attention due to the fact that the structures or parameters of singular systems sometimes changes abruptly, much important literature have been published to introduce the related results such as [11,12,[19][20][21][22][23][24][25][26][27][28][29], and so on.…”

“…Due to a given singular system must be not only stable but also regular and impulse free or causal at the same time, the investigation for singular time delay systems, especially singular time-varying delay systems, is much more complicated than that for state-space systems [14,16,[18][19][20][21]24].…”

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

“…In [107], a H ∞ dynamic output feedback control is presented to handle random communication delays. In [108], a H ∞ control strategy has been put forward to solve the problems associate with communication delay and data loss. Moreover, a H ∞ state feedback control is proposed in [109] to improve the system stability with random delays.…”

“…Model predictive control [90][91][92][93] ·Reduce data loss ·Good robustness at large delay error ·Can handle fixed and random delay ·Complex algorithm ·Large computation load ·Low dynamic response Smith predictor [90,94,95] ·Fast dynamic response ·Can handle fixed delay and random delay ·Model uncertainties and external disturbances are existed Neural network predictive control [96] ·Linearized ·High robustness ·Not handling random delay Weighted average predictive control [97,98] ·Improve consensus convergence ·Fast tracking speed ·Strong anti-interference ability ·Influence of practical factors on prediction are not considered ·Not handling random delay Gain scheduling method [99][100][101][102][103][104][105][106] ·Provide a general modeling approach ·Cost reduction ·Good power sharing performance in delay margin ·High robustness ·Gain coefficient and integral term are difficult to be selected ·Not handling random delay H∞ control method [107][108][109] ·Handle fixed and random delay ·Large computation ·Complex algorithm ·Low robustness Sliding mode control [110][111][112][113] ·Simple implementation ·Handle fixed and random delay ·Fast dynamic response ·Robustness under parameter variation and disturbance ·Chattering exists…”

“…It is worth mentioning that, delays are mainly divided into the fixed communication delays and random communication delays, and various delay compensation schemes for fixed communication delays are compared in this paper, such as the neural network predictive control [96], weighted average predictive control [97,98], the gain scheduling [99][100][101][102][103][104][105][106] and synchronization schemes using multi-timer model [114]. Moreover, some compensation methods for random communication delays are also discussed, such as the generalized predictive control (GPC) [84,85], networked predictive control (NPC) [86][87][88][89], model predictive control (MPC) [90][91][92][93], Smith predictor (SP) [90,94,95], H ∞ control [107][108][109] and sliding mode control [110][111][112][113], etc. In addition, if one microgrid plugs in or plug out from the microgrid clusters, the topology and structure of the system will be inevitably different.…”