For dynamical systems with uncertainty, robust controllers can be designed by assuming that the uncertainty is bounded. The less we know about the uncertainty in the system, the more conservative the bound must be, which in turn may lead to reduced control performance. If measurements of the uncertain term are available, this data may be used to reduce the uncertainty in order to make bounds as tight as possible. In this paper, we consider a linear system with a sector-bounded uncertainty. We develop a model predictive control algorithm to control the system, and use a weighted Bayesian linear regression model to learn the least conservative sector condition using measurements collected in closed-loop. The resulting robust model predictive control algorithm therefore reduces the conservativeness of the controller, and provides probabilistic guarantees of asymptotic stability and constraint satisfaction. The efficacy of the proposed method is shown in simulation. ; publishedVersion


    Access

    Download


    Export, share and cite



    Title :

    Learning-based Robust Model Predictive Control for Sector-bounded Lur'e Systems



    Publication date :

    2021-01-01


    Remarks:

    cristin:1961720
    46-52 ; 54 ; IFAC-PapersOnLine ; 20



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629





    Improved robust stability criteria for time‐delay lur'e system

    Duan, Wenyong / Fu, Xiaorong / Liu, Zhengfan et al. | British Library Online Contents | 2017


    Absolute Instability Conditions for Lur'e Systems

    Inoue, M. / Suzuki, M. / Kashima, K. et al. | British Library Online Contents | 2013


    On Absolute Stability of Lur'e Control Systems with Multiple Non-Linearities

    Wu, M. / He, Y. / Liu, G.-P. et al. | British Library Online Contents | 2005


    Absolute stabilization of Lur'e systems via dynamic output feedback

    Zhang, Fan / Trentelman, Harry L. / Feng, Gang et al. | British Library Online Contents | 2018