Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12623
Title: Robust model predictive control under redundant channel transmission with applications in networked DC motor systems
Authors: Song, Y
Wang, Z
Ding, D
Wei, G
Keywords: Model predictive control;Networked control systems;Static output feedback;Redundant channels;DC motor control;Equality constraints
Issue Date: 2016
Publisher: John Wiley & Sons, Ltd.
Citation: International Journal of Robust and Nonlinear Control, (2016)
Abstract: In networked systems, intermittent failures in data transmission are usually inevitable due to the limited bandwidth of the communication channel, and an effective countermeasure is to add redundance so as to improve the reliability of the communication service. This paper is concerned with the model predictive control (MPC) problem by using static output feedback for a class of polytopic uncertain systems with redundant channels under both input and output constraints. By utilizing the min-max control approach combined with stochastic analysis, sufficient conditions are established to guarantee the feasibility of the designed MPC scheme that ensures the robust stability of the closed-loop system. In terms of the solution to an auxiliary optimization problem, an easy-to-implement MPC algorithm is proposed to obtain the desired sub-optimal control sequence as well as the upper bound of the quadratic cost function. Finally, to illustrate its effectiveness, the proposed design method is applied to control a networked direct current motor system.
URI: http://onlinelibrary.wiley.com/doi/10.1002/rnc.3542/abstract;jsessionid=F7C1ACC2BF22598718B60DE0B59DC05E.f04t04?systemMessage=Wiley+Online+Library+will+be+unavailable+on+Saturday+14th+May+11%3A00-14%3A00+BST+%2F+06%3A00-09%3A00+EDT+%2F+18%3A00-21%3A00+SGT+for+essential+maintenance.Apologies+for+the+inconvenience.
http://bura.brunel.ac.uk/handle/2438/12623
DOI: http://dx.doi.org/10.1002/rnc.3542
ISSN: 1049-8923
1099-1239
Appears in Collections:Dept of Computer Science Research Papers

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