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Robustly stable model predictive control based on parallel support vector machines with linear kernel

  • Bao Zhe-jing  (包哲静)Email author
  • Zhong Wei-min  (钟伟民)
  • Pi Dao-ying  (皮道映)
  • Sun You-xian  (孙优贤)
Article

Abstract

Robustly stable multi-step-ahead model predictive control (MPC) based on parallel support vector machines (SVMs) with linear kernel was proposed. First, an analytical solution of optimal control laws of parallel SVMs based MPC was derived, and then the necessary and sufficient stability condition for MPC closed loop was given according to SVM model, and finally a method of judging the discrepancy between SVM model and the actual plant was presented, and consequently the constraint sets, which can guarantee that the stability condition is still robust for model/plant mismatch within some given bounds, were obtained by applying small-gain theorem. Simulation experiments show the proposed stability condition and robust constraint sets can provide a convenient way of adjusting controller parameters to ensure a closed-loop with larger stable margin.

Key words

parallel support vector machines model predictive control stability robustness 

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Copyright information

© Central South University Press, Sole distributor outside Mainland China: Springer 2007

Authors and Affiliations

  • Bao Zhe-jing  (包哲静)
    • 1
    Email author
  • Zhong Wei-min  (钟伟民)
    • 2
  • Pi Dao-ying  (皮道映)
    • 1
  • Sun You-xian  (孙优贤)
    • 1
  1. 1.State Key Laboratory of Industrial Control TechnologyZhejiang UniversityHangzhouChina
  2. 2.State Key Laboratory of Chemical EngineeringEast China University of Science and TechnologyShanghaiChina

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