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

页码：701 / 707

页数：7

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