New Robust Model Predictive Control for Uncertain Systems with Input Constraints Using Relaxation Matrices

被引：0

作者：

S. M. Lee

S. C. Won

J. H. Park

机构：

[1] KT Co. Ltd.,Platform Verification Division, BcN Business Unit

[2] Pohang University of Science and Technology,Department of Electronic and Electrical Engineering

[3] Yeungnam University,Department of Electrical Engineering

来源：

Journal of Optimization Theory and Applications | 2008年 / 138卷

关键词：

Model predictive control; Time-varying uncertain systems; Input constraints; LMIs;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

In this paper, we propose a new robust model predictive control (MPC) method for time-varying uncertain systems with input constraints. We formulate the problem as a minimization of the worst-case finite-horizon cost function subject to a new sufficient condition for cost monotonicity. The proposed MPC technique uses relaxation matrices to derive a less conservative terminal inequality condition. The relaxation matrices improve feasibility and system performance. The optimization problem is solved by semidefinite programming involving linear matrix inequalities (LMIs). A numerical example shows the effectiveness of the proposed method.

引用

页码：221 / 234

页数：13

共 21 条

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[10] Li S.(undefined)undefined undefined undefined undefined-undefined

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