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Distributed stochastic MPC for systems with parameter uncertainty and disturbances
KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
2018 (English)In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 28, no 6, p. 2424-2441Article in journal (Refereed) Published
Abstract [en]

A distributed stochastic model predictive control algorithm is proposed for multiple linear subsystems with both parameter uncertainty and stochastic disturbances, which are coupled via probabilistic constraints. To handle the probabilistic constraints, the system dynamics is first decomposed into a nominal part and an uncertain part. The uncertain part is further divided into 2 parts: the first one is constrained to lie in probabilistic tubes that are calculated offline through the use of the probabilistic information on disturbances, whereas the second one is constrained to lie in polytopic tubes whose volumes are optimized online and whose facets' orientations are determined offline. By permitting a single subsystem to optimize at each time step, the probabilistic constraints are then reduced into a set of linear deterministic constraints, and the online optimization problem is transformed into a convex optimization problem that can be performed efficiently. Furthermore, compared to a centralized control scheme, the distributed stochastic model predictive control algorithm only requires message transmissions when a subsystem is optimized, thereby offering greater flexibility in communication. By designing a tailored invariant terminal set for each subsystem, the proposed algorithm can achieve recursive feasibility, which, in turn, ensures closed-loop stability of the entire system. A numerical example is given to illustrate the efficacy of the algorithm. Copyright 

Place, publisher, year, edition, pages
John Wiley and Sons Ltd , 2018. Vol. 28, no 6, p. 2424-2441
Keywords [en]
distributed control, model predictive control (MPC), probabilistic constraints, stochastic systems, Closed loop control systems, Constrained optimization, Convex optimization, Distributed parameter control systems, Model predictive control, Optimization, Predictive control systems, Stochastic control systems, Closed loop stability, Convex optimization problems, Message transmissions, Parameter uncertainty, Probabilistic information, Stochastic disturbances, Stochastic models
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-227366DOI: 10.1002/rnc.4024ISI: 000427013400030Scopus ID: 2-s2.0-85041090534OAI: oai:DiVA.org:kth-227366DiVA, id: diva2:1212856
Note

Export Date: 9 May 2018; Article; CODEN: IJRCE; Correspondence Address: Xia, Y.; School of Automation, Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of TechnologyChina; email: xia_yuanqing@bit.edu.cn; Funding details: 61720106010, NSFC, National Natural Science Foundation of China; Funding details: 61621063, NSFC, National Natural Science Foundation of China; Funding details: 4161001, Natural Science Foundation of Beijing Municipality; Funding details: 61603041, NSFC, National Natural Science Foundation of China; Funding text: National Natural Science Foundation of China, Grant/Award Number: 61603041; Beijing Natural Science Foundation, Grant/Award Number: 4161001; National Natural Science Foundation Projects of International Cooperation and Exchanges, Grant/Award Number: 61720106010; Foundation for Innovative Research Groups of the National Natural Science Foundation of China, Grant/Award Number: 61621063; Funding text: This work was supported by the National Natural Science Foundation of China under grant 61603041, the Beijing Natural Science Foundation under grant 4161001, the National Natural Science Foundation Projects of International Cooperation and Exchanges under grant 61720106010, and by the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under grant 61621063. QC 20180604

Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-06-04Bibliographically approved

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Gao, Yulong

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