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Model predictive control design for polytopic uncertain systems by synthesising multi-step prediction scenarios

Author

Listed:
  • Jianbo Lu
  • Yugeng Xi
  • Dewei Li
  • Yuli Xu
  • Zhongxue Gan

Abstract

A common objective of model predictive control (MPC) design is the large initial feasible region, low online computational burden as well as satisfactory control performance of the resulting algorithm. It is well known that interpolation-based MPC can achieve a favourable trade-off among these different aspects. However, the existing results are usually based on fixed prediction scenarios, which inevitably limits the performance of the obtained algorithms. So by replacing the fixed prediction scenarios with the time-varying multi-step prediction scenarios, this paper provides a new insight into improvement of the existing MPC designs. The adopted control law is a combination of predetermined multi-step feedback control laws, based on which two MPC algorithms with guaranteed recursive feasibility and asymptotic stability are presented. The efficacy of the proposed algorithms is illustrated by a numerical example.

Suggested Citation

  • Jianbo Lu & Yugeng Xi & Dewei Li & Yuli Xu & Zhongxue Gan, 2018. "Model predictive control design for polytopic uncertain systems by synthesising multi-step prediction scenarios," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(2), pages 344-357, January.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:2:p:344-357
    DOI: 10.1080/00207721.2017.1402214
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