Explicit Model Predictive Control via Nonlinear Piecewise Approximations

被引：2
|
作者：
Van-Vuong Trinh [1 ,2 ,3 ]
Alamir, Mazen [1 ]
Bonnay, Patrick [2 ,3 ]
Bonne, Francois [2 ,3 ]
机构：
[1] CNRS, GIPSA Lab, Control Syst Dept, 11 Rue Math, F-38402 St Martin Dheres, France
[2] Univ Grenoble Alpes, INAC SBT, F-38000 Grenoble, France
[3] CEA Grenoble, INAC SBT, F-38000 Grenoble, France
来源：
IFAC PAPERSONLINE | 2016年 / 49卷 / 18期
关键词：
System Identification; Nonlinear Approximation; Explicit Model Predictive Control; CONTROL LAWS; SYSTEMS; FRAMEWORK;
D O I：
10.1016/j.ifacol.2016.10.173
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a novel identification methology proposed to capture general multivariate nonlinear relationships, with focus on the bounded-error approximation of model predictive control for constrained (non)linear systems. The formulation of the identification problem takes, at each iteration, the form of a constrained linear (or quadratic) optimization problem that is mathematically feasible as well as numerically tractable. The efficiency of the proposed method for the derivation of low-complexity explicit model predictive controllers is demonstrated via the constrained control of a tlwrmodyicamic power plant. (C) 2016, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.
引用
收藏
页码：259 / 264
页数：6
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