Nonlinear model predictive control from data: a set membership approach

被引：48

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

页码：123 / 139

页数：17

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