Input-to-state stabilizing MPC for neutrally stable linear systems subject to input constraints

Jung Su Kim, Tae Woong Yoon, Ali Jadbabaie, Claudio De Persis

    Research output: Contribution to journalConference articlepeer-review

    9 Citations (Scopus)

    Abstract

    MPC(Mode) Predictive Control) is representative of control methods which are able to handle physical constraints. Closed-loop stability can therefore be ensured only locally in the presence of constraints of this type. However, if the system is neutrally stable, and if the constraints are imposed only on the input, global asymptotic stability can be obtained. A globally stabilizing finite-horizon MPC has lately been suggested for the neutrally stable systems using a non-quadratic terminal cost which consists of cubic as well as quadratic functions of the state. In this paper, an input-to-state-stabilizing MPC is proposed for the discrete-time input-constrained neutrally stable system using a non-quadratic terminal cost which is similar to that used in the global stabilizing MPC, provided that the external disturbance is sufficiently small. The proposed MPC algorithm is also coded using an SQP (Sequential Quadratic Programming) algorithm, and simulation results are given to show the effectiveness of the method.

    Original languageEnglish
    Article numberFrB13.4
    Pages (from-to)5041-5046
    Number of pages6
    JournalProceedings of the IEEE Conference on Decision and Control
    Volume5
    Publication statusPublished - 2004
    Event2004 43rd IEEE Conference on Decision and Control (CDC) - Nassau, Bahamas
    Duration: 2004 Dec 142004 Dec 17

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Modelling and Simulation
    • Control and Optimization

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