The week's pick
Reseach Article
Robust Model Predictive Control with One Free Control Move for NCSs with Data Missing
by
Jimin Yu,
Xiaogang Gong
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 152 - Number 3 |
| Year of Publication: 2016 |
| Authors: Jimin Yu, Xiaogang Gong |
10.5120/ijca2016911825
|
Jimin Yu, Xiaogang Gong . Robust Model Predictive Control with One Free Control Move for NCSs with Data Missing. International Journal of Computer Applications. 152, 3 ( Oct 2016), 1-8. DOI=10.5120/ijca2016911825
@article{
10.5120/ijca2016911825,
author = {
Jimin Yu,
Xiaogang Gong
},
title = { Robust Model Predictive Control with One Free Control Move for NCSs with Data Missing },
journal = {
International Journal of Computer Applications
},
issue_date = { Oct 2016 },
volume = { 152 },
number = { 3 },
month = { Oct },
year = { 2016 },
issn = { 0975-8887 },
pages = {
1-8
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume152/number3/26296-2016911825/
},
doi = { 10.5120/ijca2016911825 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:57:08.965588+05:30
%A Jimin Yu
%A Xiaogang Gong
%T Robust Model Predictive Control with One Free Control Move for NCSs with Data Missing
%J International Journal of Computer Applications
%@ 0975-8887
%V 152
%N 3
%P 1-8
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Current hot issue of networked control systems(NCSs) remains how to optimize the signal transmission in imperfect links, especially, data missing(between the controller and actuator) is a potential source of poor performance of the control system. Here, stochastic variables satisfying markov jump process are used to describe the fading channel. Considering the uncertainty factor of plant, a practical compensation technique is utilized to minimize the effects caused by data dropout. Attention is paid to designing a useful control law to drive the closed-loop system stable and preserve a guaranteed infinite-horizon performance function, where the infinite-horizon control moves are parameterized as a free control move. Furthermore, the corresponding problems about recursive feasibility and stochastic stability are established by a set of linear matrix inequalities. Simulation results are shown to verify the performance of the proposed approach.
References
- Jianjun Bai, Hongye Su, Jinfeng Gao, and Tao Sun. Modeling and stabilization of a wireless network control system with packet loss and time delay. Journal of the Franklin Institute, 349:2420–2430, 2012.
- Peng C and Han Q. On designing a novel self-triggered sampling scheme for networked control systems with data losses and communication delays. Industrial Electronics, IEEE Transactions on, page 1, 2015.
- G Como and F Fagnani. Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays. Robustness of Large-Scale Stochastic Matrices to Localized Perturbations, Network Science and Engineering, 2:53 – 64, 2015.
- Baocang Ding and Xubin Ping. Output feedback predictive control with one free control move for nonlinear systems represented by a takagiugeno model-fuzzy systems. IEEE Transactions on, pages 1063–6706, 2014.
- M Evans, M A.Cannon, and B Kouvaritakis. Robust mpc tower damping for variable speed wind turbines.control systems technology. IEEE Transactions on, pages 290 – 296, 2014.
- Cuzzola F.A, Geromel J.C, and Morari M. An improved approach for constrained robust model predictive control. Automatica, pages 1183–1189, 2002.
- J Fleming. Robust tube mpc for linear systems with multiplicative uncertainty kouvaritakis. IEEE Transactions on, pages 1087 – 1092, 2015.
- Franze G and Tedesco F. Networked control systems: a polynomial receding horizon approach. IEEE Transactions on Control of Network Systems, 1(4):318–327, 2014.
- Franze G, F Tedesco, and Famularo D. Model predictive control for constrained networked systems subject to data losses. Automatica, pages 272–278, 2015.
- Pin G and Parisini T. Networked predictive control of uncertain constrained nonlinear systems: recursive feasibility and input-to-state stability analysis. IEEE Transactions on Automatic Control, pages 72–87, 2011.
- Nguyen Tuan Hung, N.Bm, Ibrahim R, and Irfan M. Design of multi model predictive control for nonlinear process plant. Intelligent and Advanced Systems (ICIAS), 2014 5th International Conference on, pages 1–6, 2014.
- M Killian, B Mayer, A Schirrer, and M Kozek. Cooperative fuzzy model predictive control.fuzzy systems. IEEE Transactions on, 66:1, 2014.
- Jiwei Li, Dewei Li, and Yugeng Xi. On design of mpc with soft constraint based on multistep control set for stochastic multiplicative system. Control Conference (CCC), pages 4102–4125, 2012.
- D Muoz de la Pea and P.D Christofides. Lyapunov-based model predictive control of nonlinear systems subject to data losses. IEEE Transactions on Automatic Control, 53:2076– 2089, 2008.
- Kothare M.V, Balakrishnan V, and Morari M. Robust constrained model predictive control using linear matrix inequalities. Automatic, pages 1361–1379, 1996.
- R. Rakkiyappana. Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays. Control Applications, 2005. CCA 2005. Proceedings of 2005 IEEE Conference on, pages 260 – 265, 2014.
- R. Rakkiyappana. Stochastic sampled-data control for synchronization of complex dynamical networks with control packet loss and additive time-varying delays. Neural Networks, 66:455–470, 2015.
- P.K Roy, G.K Mann, and B.C Hawlader. Fuzzy rule-adaptive model predictive control for a multivariable heating system. Neural Networks, 66:455–470, 2015.
- Jeong S.C and Park P. Constrained mpc algorithm for uncertain time-varying systems with state-delay. IEEE Trans. Autom. Control, pages 257–263, 2005.
- Nurul Adilla Mohd Subha and Guo-Ping Liu. External consensus in multi-agent systems with large consecutive data loss under unreliable networks. Neural Networks, 10(9):IET Control Theory and Applications, 2016.
- Haiyi Sun, QingLing Zhang, and Ning Li. Robust passive control for stochastic networked control systems. Control and Decision Conference, 2009. CCDC ’09. Chinese, pages 482 – 487, 2009.
- A Teel, A.R. Recurrence principle for stochastic difference inclusions. Automatic Control, IEEE Transactions on, 60:420 – 435, 2014.
- Santos T.L.M and Gonzalez A.H. Stable mpc with reduced representation for linear systems with multiple input delays. American Control Conference (ACC), pages 238–243, 2015.
- A.L Warren, Marlin, and Thomas E. Improved output constraint-handling for mpc with disturbance uncertainty. American Control Conference. Proceedings of the 2003, pages 4573 – 4578, 2003.
- Hao Xu and S Jagannathan. Finite horizon stochastic optimal control of uncertain linear networked control system. Adaptive Dynamic Programming And Reinforcement Learning (ADPRL), 2013 IEEE Symposium on, pages 16–19, 2013.
- Hao Xu and S Jagannathan. Stochastic optimal controller design for uncertain nonlinear networked control system via neuro dynamic programming. Neural Networks and Learning Systems, IEEE Transactions on, 24:471 – 484, 2013.
- Langwen Zhang, Jingcheng Wang, Yang Ge, and Bohui Wang. Robust distributed model predictive control for uncertain networked control systems. Control Theory Applications, pages 1843–1851, 2014.
- Lixian Zhang, Huijun Gao, and O Kaynak. Network-induced constraints in networked control systemsla survey. Industrial Informatics, IEEE Transactions on, pages 403 – 416, 2012.
- Y. Zhao, H. Gao, and T. Chen. Fuzzy constrained predictive control of non-linear systems with packet dropouts. IET Control Theory Appl, pages 1665–1677, 2010.
- Yuanyuan Zou, James Lam, Yugang Niu, and Dewei Li. Constrained predictive control synthesis for quantized systems with markovian data loss. Automatica, 0:217–225, 2015.
Index Terms
Computer Science
Information Sciences
Keywords