Distributed Model Predictive Control of Leader-Follower Systems Using an Interior Point Method with Efficient Computations

Abstract : Standard model predictive control strategies implythe online computation of control inputs at each samplinginstance, which traditionally limits this type of control scheme tosystems with slow dynamics. This paper focuses on distributedmodel predictive control for large-scale systems comprised ofinteracting linear subsystems, where the online computationsrequired for the control input can be distributed amongstthem. A model predictive controller based on a distributedinterior point method is derived, for which every subsystemin the network can compute stabilizing control inputs usingdistributed computations. We introduce local terminal sets andcost functions, which together satisfy distributed invarianceconditions for the whole system, that guarantees stability of theclosed-loop interconnected system. We show that the synthesisof both terminal sets and terminal cost functions can be donein a distributed framework.
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Contributor : Josiane Dartron <>
Submitted on : Wednesday, May 29, 2013 - 8:09:27 AM
Last modification on : Friday, December 20, 2019 - 11:48:05 AM

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  • HAL Id : hal-00827199, version 1

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I. Necoara, D. N. Clipici, Sorin Olaru. Distributed Model Predictive Control of Leader-Follower Systems Using an Interior Point Method with Efficient Computations. 2013 IEEE American Control Conference ACC, Jun 2013, Washington, United States. pp.1-8. ⟨hal-00827199⟩

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