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New Robust Model Predictive Control for Uncertain Systems with Input Constraints Using Relaxation Matrices

Author

Listed:
  • S. M. Lee

    (KT Co. Ltd.)

  • S. C. Won

    (Pohang University of Science and Technology)

  • J. H. Park

    (Yeungnam University)

Abstract

In this paper, we propose a new robust model predictive control (MPC) method for time-varying uncertain systems with input constraints. We formulate the problem as a minimization of the worst-case finite-horizon cost function subject to a new sufficient condition for cost monotonicity. The proposed MPC technique uses relaxation matrices to derive a less conservative terminal inequality condition. The relaxation matrices improve feasibility and system performance. The optimization problem is solved by semidefinite programming involving linear matrix inequalities (LMIs). A numerical example shows the effectiveness of the proposed method.

Suggested Citation

  • S. M. Lee & S. C. Won & J. H. Park, 2008. "New Robust Model Predictive Control for Uncertain Systems with Input Constraints Using Relaxation Matrices," Journal of Optimization Theory and Applications, Springer, vol. 138(2), pages 221-234, August.
    • Handle: RePEc:spr:joptap:v:138:y:2008:i:2:d:10.1007_s10957-008-9375-5
    DOI: 10.1007/s10957-008-9375-5
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    Cited by:

    1. Soroush Sadeghnejad & Farshad Khadivar & Mojtaba Esfandiari & Golchehr Amirkhani & Hamed Moradi & Farzam Farahmand & Gholamreza Vossoughi, 2023. "Using an Improved Output Feedback MPC Approach for Developing a Haptic Virtual Training System," Journal of Optimization Theory and Applications, Springer, vol. 198(2), pages 745-766, August.

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