Fast Nonlinear Model Predictive Control via Set Membership Approximation: An Overview
Fast Nonlinear Model Predictive Control via Set Membership Approximation: An Overview
复制标题
通过集合隶属近似的快速非线性模型预测控制：概述
DOI：
10.1007/978-3-642-01094-1_36
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
M. Milanese
中科院分区：
文献类型：
--
作者：
M. Canale;L. Fagiano;M. Milanese
描述了集员(SM)函数逼近技术的使用，以便离线计算逼近给定的非线性模型预测控制(NMPC)律的控制律。NMPC的在线评估时间比NMPC滚动域策略所要求的优化时间更快，从而使NMPC也可以应用于动态“快”的过程。此外，SM方法允许在保证最坏情况精度的情况下获得近似控制律，该近似控制律可以适当地调整以获得与精确NMPC控制器任意接近的闭环系统稳定性和性能特性。特别地，本文回顾了三种不同的SM方法，即“最优”、“最近点”和“局部”逼近的性质，并通过数值算例比较了它们的性能。
The use of Set Membership (SM) function approximation techniques is described, in order to compute off-line a control lawwhich approximates a given Nonlinear Model Predictive Control (NMPC) law. The on-line evaluation time ofis faster than the optimization required by the NMPC receding horizon strategy, thus allowing application of NMPC also on processes with “fast” dynamics. Moreover, SM methodology allows to derive approximated control laws with guaranteed worst-case accuracy, which can be suitably tuned to achieve closed loop stability and performance properties that are arbitrarily close to those of the exact NMPC controller. In particular, the properties of three different SM techniques are reviewed here, namely the “optimal”, “nearest point” and the “local” approximations, and their performances are compared on a numerical example.