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

被引：0
|
作者：
包哲静 [1 ]
钟伟民 [2 ]
皮道映 [1 ]
孙优贤 [1 ]
机构：
[1] State Key Laboratory of Industrial Control Technology, Zhejiang University
[2] State Key Laboratory of Chemical Engineering, East China University of Science and Technology
来源：
Journal of Central South University of Technology | 2007年 / 05期
基金：
中国国家自然科学基金;
关键词：
parallel support vector machines; model predictive control; stability; robustness;
D O I：
暂无
中图分类号：
O231 [控制论（控制论的数学理论）];
学科分类号：
摘要：
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.
引用
收藏
页码：701 / 707
页数：7
相关论文
共 50 条
  • [1] Robustly stable model predictive control based on parallel support vector machines with linear kernel
    Bao Zhe-jing
    Zhong Wei-min
    Pi Dao-ying
    Sun You-xian
    JOURNAL OF CENTRAL SOUTH UNIVERSITY OF TECHNOLOGY, 2007, 14 (05): : 701 - 707
  • [2] Robustly stable model predictive control based on parallel support vector machines with linear kernel
    Zhe-jing Bao
    Wei-min Zhong
    Dao-ying Pi
    You-xian Sun
    Journal of Central South University of Technology, 2007, 14 : 701 - 707
  • [3] Model Predictive Control in Milling based on Support Vector Machines
    Ay, Muzaffer
    Stemmler, Sebastian
    Schwenzer, Max
    Abel, Dirk
    Bergs, Thomas
    IFAC PAPERSONLINE, 2019, 52 (13): : 1797 - 1802
  • [4] Fault tolerance in the framework of support vector machines based model predictive control
    Saludes Rodil, Sergio
    Fuente, M. J.
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2010, 23 (07) : 1127 - 1139
  • [5] An FPGA implementation of linear kernel support vector machines
    Pina-Ramirez, Omar
    Valdes-Cristerna, Raquel
    Yanez-Suarez, Oscar
    RECONFIG 2006: PROCEEDINGS OF THE 2006 IEEE INTERNATIONAL CONFERENCE ON RECONFIGURABLE COMPUTING AND FPGA'S, 2006, : 314 - +
  • [6] Nonlinear model predictive control based on support vector machine with multi-kernel
    Bao Zhejing
    Pi Daoying
    Sun Youxian
    CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2007, 15 (05) : 691 - 697
  • [7] Support vector machines-based generalized predictive control
    Iplikci, S.
    INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, 2006, 16 (17) : 843 - 862
  • [8] A nonlinear model predictive control based on Least Squares support vector machines NARX model
    Shi, Yun-Tao
    Sun, De-Hui
    Wang, Qing
    Nian, Si-Cheng
    Xiang, Li-Zhi
    PROCEEDINGS OF 2007 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, 2007, : 721 - +
  • [9] Parallel Clifford Support Vector Machines Using the Gaussian Kernel
    Gehová López-González
    Nancy Arana-Daniel
    Eduardo Bayro-Corrochano
    Advances in Applied Clifford Algebras, 2017, 27 : 647 - 660
  • [10] Parallel Clifford Support Vector Machines Using the Gaussian Kernel
    Lopez-Gonzalez, Gehova
    Arana-Daniel, Nancy
    Bayro-Corrochano, Eduardo
    ADVANCES IN APPLIED CLIFFORD ALGEBRAS, 2017, 27 (01) : 647 - 660
12345