Moment based model predictive control for systems with additive uncertainty

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Abstract

In this paper, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We show that the moment based formulation yields predictive control problems which are computationally simpler to solve compared to the existing robust MPC formulations, while providing statistical robustness properties. We apply the proposed MPC technique to a simple simulation example to demonstrate its effectiveness.

Original languageEnglish
Title of host publication2017 American Control Conference, ACC 2017, 24-26 May 2017, Seattle, Washington
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages3072-3077
Number of pages6
ISBN (Electronic)978-1-5090-5992-8
ISBN (Print)978-1-5090-4583-9
DOIs
Publication statusPublished - 29 Jun 2017
Event2017 American Control Conference (ACC 2017) - Sheraton Seattle Hotel, Seattle, United States
Duration: 24 May 201726 May 2017
http://acc2017.a2c2.org/

Conference

Conference2017 American Control Conference (ACC 2017)
Abbreviated titleACC 2017
CountryUnited States
CitySeattle
Period24/05/1726/05/17
Internet address

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