Abstract
ABSTRACTIn recent years, the set-membership filtering problem has received extensive research interests due to its engineering significance to estimate a set which includes the true state of the system rather than a single vector. What's more, the communication protocol has been widely applied in the control systems because of its ability to prevent from data collisions and reduce the burden of the network. From the engineering practice, it is of vital importance to investigate the set-membership filtering under communication protocols. In this paper, a bibliographical review is provided on the set-membership filtering for networked control systems (NCSs) under communication protocols. Next, the concept of the NCS is briefly introduced. Some recent advances on the set-membership filtering problem for NCSs are summarized. Then, some well-known scheduling schemes are presented and the results of the set-membership filtering for NCSs under communication protocols are reviewed. Finally, some concluding remarks are given and some potential future research directions are pointed out.
Highlights
The past decades have witnessed that the filtering problem has been a seductive research focus attracting constant attention in the field of control communities and signal processing
A bibliographical review is provided on the set-membership filtering for networked control systems (NCSs) under communication protocols
The idea of the ellipsoidal state estimation is to give a set of state estimates by assuming hard bounds in state space instead of stochastic descriptions on the system noises which always affect the true state of the system
Summary
The past decades have witnessed that the filtering problem has been a seductive research focus attracting constant attention in the field of control communities and signal processing. Some common filtering methods include Kalman filtering (Li, Kar, Alsaadi, Dobaie, & Cui, 2015; Safarinejadian & Yousefi, 2015), extended Kalman filtering (Ahmada & Namerikawa, 2013; Chatterjee, Fournier, Nait-Ali, & Siarry, 2010; Hu, Wang, Gao, & Stergioulas, 2012; Kluge, Reif, & Brokate, 2010), H∞ filtering (Dong, Wang, Ding, & Gao, 2016; Dong, Wang, & Gao, 2013; Dong, Wang, Shen, & Ding, 2016; Shen, Wang, Hung, & Chesi, 2011), H2 filtering (Gao, Lam, Xie, & Wang, 2005; Liu, Liu, Shi, & Wang, 2014; Sahebsara, Chen, & Shah, 2007) and so on Most of these filtering methods require the system noise including process noise and measurement noise to be in a random framework and provide a probabilistic state estimation. The origin of set-membership filtering can be traced back to 1960s (Witsenhausen, 1968) and the corresponding problems have attracted the growing interest of many researchers
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