Nonlinear model predictive control from data: a set membership approach

被引：47
|
作者：
Canale, M. [1 ]
Fagiano, L. [1 ,2 ]
Signorile, M. C. [1 ]
机构：
[1] Politecn Torino, Dipartimento Automat & Informat, I-10129 Turin, Italy
[2] Univ Calif Santa Barbara, Dept Mech Engn, Santa Barbara, CA 93106 USA
来源：
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL | 2014年 / 24卷 / 01期
关键词：
predictive control; robust stability; nonlinear control; STABILITY; SYSTEMS;
D O I：
10.1002/rnc.2878
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new approach to design a Nonlinear Model Predictive Control law that employs an approximate model, derived directly from data, is introduced. The main advantage of using such models lies in the possibility to obtain a finite computable bound on the worst-case model error. Such a bound can be exploited to analyze the robust convergence of the system trajectories to a neighborhood of the origin. The effectiveness of the proposed approach, named Set Membership Predictive Control, is shown in a vehicle lateral stability control problem, through numerical simulations of harsh maneuvers. Copyright (c) 2012 John Wiley & Sons, Ltd.
引用
收藏
页码：123 / 139
页数：17
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