Nonlinear predictive control based on a global model identified off-line

被引：0
|
作者：
Peng, H [1 ]
Ozaki, T [1 ]
Toyoda, Y [1 ]
Haggan-Ozaki, V [1 ]
机构：
[1] Cent S Univ, Coll Informat Engn, Changsha 410083, Peoples R China
来源：
PROCEEDINGS OF THE 2002 AMERICAN CONTROL CONFERENCE, VOLS 1-6 | 2002年 / 1-6卷
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A model predictive control (MPC) strategy for the non-stationary nonlinear systems with operating point-dependent dynamics is presented. The MPC proposed does not require on-line parameters estimation, because its internal model is an off-line identified global (RBF-ARX) model, which is a generalized ARX model with Gaussian radial basis function networks-based functional coefficients. The RBF-ARX model parameters are estimated using a quickly-convergent structured nonlinear parameter optimization method (SNPOM). The quadratic programming routines may be used to solve the MPC problem with constraints. Simulation study on a chemical process shows satisfactory modeling and control performance.
引用
收藏
页码：4197 / 4202
页数：6
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