A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 2 Issue 4
Oct.  2015

IEEE/CAA Journal of Automatica Sinica

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Article Contents
Yan Song, Haifeng Lou and Shuai Liu, "Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 374-381, 2015.
Citation: Yan Song, Haifeng Lou and Shuai Liu, "Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System," IEEE/CAA J. of Autom. Sinica, vol. 2, no. 4, pp. 374-381, 2015.

Distributed Model Predictive Control with Actuator Saturation for Markovian Jump Linear System

Funds:

This work was supported in part by National Natural Science Foundation of China (61403254, 61374039, 61203143), Shanghai Pujiang Program (13PJ1406300), Shanghai Natural Science Foundation of China (13ZR1428500), Innovation Program of Shanghai Municipal Education Commission (14YZ083), and Hujiang Foundation of China (C14002, B1402/D1402).

  • This paper is concerned with the distributed model predictive control (MPC) problem for a class of discrete-time Markovian jump linear systems (MJLSs) subject to actuator saturation and polytopic uncertainty in system matrices. The global system is decomposed into several subsystems which coordinate with each other. A set of distributed controllers is designed by solving a min-max optimization problem in terms of the solutions of linear matrix inequalities (LMIs). An iterative algorithm is developed to achieve the online computation. Finally, a simulation example is employed to show the effectiveness of the proposed algorithm.

     

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