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基于在线最小二乘支持向量机的广义预测控制

李丽娟 苏宏业 褚健

李丽娟, 苏宏业, 褚健. 基于在线最小二乘支持向量机的广义预测控制. 自动化学报, 2007, 33(11): 1182-1188. doi: 10.1360/aas-007-1182
引用本文: 李丽娟, 苏宏业, 褚健. 基于在线最小二乘支持向量机的广义预测控制. 自动化学报, 2007, 33(11): 1182-1188. doi: 10.1360/aas-007-1182
LI Li-Juan, SU Hong-Ye, CHU Jian. Generalized Predictive Control with Online Least Squares Support Vector Machines. ACTA AUTOMATICA SINICA, 2007, 33(11): 1182-1188. doi: 10.1360/aas-007-1182
Citation: LI Li-Juan, SU Hong-Ye, CHU Jian. Generalized Predictive Control with Online Least Squares Support Vector Machines. ACTA AUTOMATICA SINICA, 2007, 33(11): 1182-1188. doi: 10.1360/aas-007-1182

基于在线最小二乘支持向量机的广义预测控制

doi: 10.1360/aas-007-1182
详细信息
    通讯作者:

    李丽娟

Generalized Predictive Control with Online Least Squares Support Vector Machines

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    Corresponding author: LI Li-Juan
  • 摘要: This paper proposes a practical generalized predictive control (GPC) algorithm based on online least squares support vector machines (LS-SVM) which can deal with nonlinear systems effectively. At each sampling period the algorithm recursively modifies the model by adding a new data pair and deleting the least important one out of the consideration on realtime property. The data pair deleted is determined by the absolute value of lagrange multiplier from last sampling period. The paper gives the recursive algorithm of model parameters when adding a new data pair and deleting an existent one, respectively, and thus the inversion of a large matrix is avoided and the memory can be controlled by the algorithm entirely. The nonlinear LS-SVM model is applied in GPC algorithm at each sampling period. The experiments of generalized predictive control on pH neutralizing process show the effectiveness and practicality of the proposed algorithm.
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出版历程
  • 收稿日期:  2006-11-30
  • 修回日期:  2007-03-21
  • 刊出日期:  2007-11-20

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