Convex Chance Constrained Model Predictive Control

Ashkan Jasour, Constantino Lagoa

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    7 Scopus citations

    Abstract

    We consider the Chance Constrained Model Predictive Control problem for polynomial systems subject to disturbances. In this problem, we aim at finding optimal control input for given disturbed dynamical system to minimize a given cost function subject to probabilistic constraints, over a finite horizon. The control laws provided have a predefined (low) risk of not reaching the desired target set. Building on the theory of measures and moments, a sequence of finite semidefinite programmings are provided, whose solution is shown to converge to the optimal solution of the original problem. Numerical examples are presented to illustrate the computational performance of the proposed approach.

    Original languageEnglish (US)
    Title of host publication2016 IEEE 55th Conference on Decision and Control, CDC 2016
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages6204-6209
    Number of pages6
    ISBN (Electronic)9781509018376
    DOIs
    StatePublished - Dec 27 2016
    Event55th IEEE Conference on Decision and Control, CDC 2016 - Las Vegas, United States
    Duration: Dec 12 2016Dec 14 2016

    Publication series

    Name2016 IEEE 55th Conference on Decision and Control, CDC 2016

    Other

    Other55th IEEE Conference on Decision and Control, CDC 2016
    CountryUnited States
    CityLas Vegas
    Period12/12/1612/14/16

    All Science Journal Classification (ASJC) codes

    • Artificial Intelligence
    • Decision Sciences (miscellaneous)
    • Control and Optimization

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  • Cite this

    Jasour, A., & Lagoa, C. (2016). Convex Chance Constrained Model Predictive Control. In 2016 IEEE 55th Conference on Decision and Control, CDC 2016 (pp. 6204-6209). [7799223] (2016 IEEE 55th Conference on Decision and Control, CDC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC.2016.7799223