返回
Robust explicit MPC based on approximate multiparametric convex programming
DOI:10.1109/TAC.2006.878755.png)
摘要
En 中文
Many robust model predictive control (MPC) schemes require the online solution of a computationally demanding convex program. For deterministic MPC schemes, multiparametric programming was successfully applied to move offline most of the computation. In this paper, we adopt a general approximate multiparametric algorithm recently suggested for convex problems and propose to apply it to a classical robust MPC scheme. This approach enables one to implement a robust MPC controller in real time for systems with polytopic uncertainty, ensuring robust constraint satisfaction and robust convergence to a given bounded set.
Keyword:
model predictive control (MPC)
multiparametric programming
robust control
uncertain systems
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:
7
论文数:
1.3W
被引数:
6.7W
机构
暂无机构信息
引用论文
Feedback min-max model predictive control using a single linear program: robust stability and the explicit solution使用单个线性程序的反馈最小-最大模型预测控制: 鲁棒稳定性和显式解决方案
Piecewise affinity of min-max MPC with bounded additive uncertainties and a quadratic criterion
AUTOMATICA
IF5.9

