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\section{Introduction}\label{introduction}

\indent Model predictive control(MPC) is a popular control strategy for academy and engineering, such as petrochemical processes, crystallization processes, pharmaceutical processes and stratospheric airships \cite{BGJ2016, WYGJFI2019}. MPC is a model-based closed-loop optimization control scheme, which solves the complex optimization problem with state and control variables constraints \cite{Vatankhah2017JFI, LaHC2017AC, Liu2018JFI, Garg2019CCE, GallegoG2019CEP,Sopasakis2019AC}. However, in general, there often exists the model mismatch and inevitable uncertainty in the real world. In order to deal more effectively with these issues, a robust model predictive control(RMPC) method has been proposed \cite{Kothare1996AC,Cuzzola2002AC,Bumroongsri2017IJC,Nodozi2017JPC,KimOh2019JPC,Yang2019IS}. The RMPC preserves the characteristic of receding optimization of MPC and takes advantage of the idea of robust control to cope with these uncertainties. As is well known, some RMPC are designed based on state feedback \cite{Lib2022InforS}. However, in real systems, the state of the system is often unmeasurable. Therefore, Output feedback robust model predictive control (OFRMPC) have achieved a lot of research results \cite{XubinPing2017AJC,WeilinYang-2017-AJC}. \\


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A.~R.~Benson, D.~F.~Gleich and J.~Leskovec, Higher-order organization of complex networks, \emph{Science}, vol.~353, no.~6295, pp.~163--166, 2016.


\bibitem{WYGJFI2019}
Jianhua Wang, Yan Song, Guoliang Wei. Dynamic output-feedback RMPC for systems with polytopic uncertainties under Round-Robin protocol, Journal of the Franklin Institute, 2019, 356, 2421–2439.

\bibitem{SYHCEP2018}
Shiqian Liu, Yuanjun Sang, Hongbin Jin. Robust model predictive control for stratospheric airships using LPV design. Control Engineering Practice, 2018, 81: 231-243.

\bibitem{Vatankhah2017JFI}
Bahareh Vatankhah, Mohammad Farrokhi. Nonlinear model-predictive control with disturbance rejection property using adaptive neural networks. Journal of the Franklin Institute, 2017, 354(13): 5201-5220.

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Huu Chuong La, Andreas Potschka, Hans Georg Bock, Partial stability for nonlinear model predictive control. Automatica, 2017, 78: 14-19.

\bibitem{Liu2018JFI}
Xiaotao Liu, Mingxi Liu, Yang Shi. Event triggered model predictive control: A less conservative result. Journal of the Franklin Institute, 2018,  355(18): 9053-9071.

\bibitem{Garg2019CCE}
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\bibitem{GallegoG2019CEP}
Antonio J. Gallego, Gonzalo M. Merello, Manuel Berenguel, Eduardo F. Camacho. Gain-scheduling model predictive control of a Fresnel collector field. Control Engineering Practice, 2019, 82: 1-13.

\bibitem{Sopasakis2019AC}
Pantelis Sopasakis, Domagoj Herceg, Alberto Bemporad, Panagiotis Patrinos. Risk-averse model predictive control. Automatica, 2019, 100: 281-288.

\bibitem{Kothare1996AC}
Mayuresh V. Kothare, Venkataramanan Balakrishnan, Manfred Morari, Robust constrained model predictive control using linear matrix inequalities. Automatica, 1996, 32(10): 1361-1379.

\bibitem{Cuzzola2002AC}
Francesco A. Cuzzola, Jose C. Geromel, Manfred Morari, An improved approach for constrained robust model predictive control. Automatica, 2002, 38(7): 1183-1189.

\bibitem{Bumroongsri2017IJC}
Pornchai Bumroongsri, Soorathep Kheawhom, Robust model predictive control with time-varying tubes. International Journal of Control, Automation and Systems, 2017, 15(4): 1479-1484.
%
\bibitem{Nodozi2017JPC}
Iman Nodozi, Mehdi Rahmani, LMI-based model predictive control for switched nonlinear systems. Journal of Process Control, 2017, 59: 49-58.

\bibitem{KimOh2019JPC}Yeonsoo Kim, Tae Hoon Oh, Taekyoon Park, Jong Min Lee. Backstepping control integrated with Lyapunov-based model predictive control. Journal of
Process Control, 2019, 73: 137-146.

\bibitem{Yang2019IS}
Weilin Yang, Dezhi Xu, Changzhu Zhang, Wenxu Yan. A novel robust model predictive control approach with pseudo terminal designs. Information Sciences, 2019, 481: 128-140.

\bibitem{Lib2022InforS}
Bin Li,Xinglian Zhou,Zhaoke Ning,Xiaoyi Guan,Ka-Fai Cedric Yiu. Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach. Information Sciences, 2022, 612, 384-398.

\bibitem{XubinPing2017AJC}
Xubin Ping. Output feedback robust MPC based on off-line observer for LPV systems via quadratic boundedness. Asian Journal of Control, Vol. 19, No. 4, pp. 1641–1653.2017.

\bibitem{WeilinYang-2017-AJC}
Weilin Yang, Jianwei Gao, Gang Feng, TieJun Zhang. An optimal approach to output-feedback robust model predictive control of LPV systems with disturbances. International Journal of Robust and Nonlinear Control, 2016,26,15,3253-3273.

\bibitem{LiuD2018IS}
Dan Liu, Guanghong Yang. Robust event-triggered control for networked control systems. Information Sciences, 2018, 459: 186-197.

\bibitem{Liang2018AC}
Xiao Liang, Juanjuan Xu. Control for networked control systems with remote and local controllers over unreliable communication channel. Automatica, 2018, 98: 86-94.

\bibitem{Yu2018ISA}
Jimin Yu, Chenchen Yang, Xiaoming Tang, Ping Wang. $H_{\infty}$ control for uncertain linear system over networks with Bernoulli data dropout and actuator saturation. ISA Transactions, 2018, 74: 1-13.

\bibitem{Liuyan2018JFI}
Zili Liu, Weisheng Yan, Huiping Li, Michael Small. Cooperative output regulation problem of multi-agent systems with stochastic packet dropout and time-varying communication delay. Journal of the Franklin Institute, 2018, 355(17): 8664-8682.


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