.png){kind=icon}
返回
A robust model predictive control algorithm for incrementally conic uncertain/nonlinear systems
.png){kind=icon}
PRE
AI
DOI：10.1002/rnc.1613.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a robustly stabilizing model predictive control algorithm for systems with incrementally conic uncertain/nonlinear terms and bounded disturbances. The resulting control input consists of feedforward and feedback components. The feedforward control generates a nominal trajectory from online solution of a finite-horizon constrained optimal control problem for a nominal system model. The feedback control policy is designed off-line by utilizing a model of the uncertainty/nonlinearity and establishes invariant 'state tubes' around the nominal system trajectories. The entire controller is shown to be robustly stabilizing with a region of attraction composed of the initial states for which the finite-horizon constrained optimal control problem is feasible for the nominal system. Synthesis of the feedback control policy involves solution of linear matrix inequalities. An illustrative numerical example is provided to demonstrate the control design and the resulting closed-loop system performance. Copyright (C) 2010 John Wiley & Sons, Ltd.
Keyword:
model predictive control
robust control
Lyapunov theory
nonlinear systems
uncertain systems
linear matrix inequalities
receding horizon control
incrementally conic uncertainty
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.2
论文数:
7.0K
被引数:
1.4W
机构
引用论文
Robust model predictive control of constrained linear systems with bounded disturbances有界扰动约束线性系统的鲁棒模型预测控制.png)
AUTOMATICA
OAAI
AUTOMATICAIF5.9
OAAI分享收藏分享收藏分享收藏
查看更多内容