Data-Driven Computation of Robust Invariant Sets and Gain-Scheduled Controllers for Linear Parameter-Varying Systems

被引：1

作者：

Mejari, Manas ^{[1
]}

Gupta, Ankit ^{[2
]}

Piga, Dario ^{[1
]}

机构：

[1] IDSIA Dalle Molle Inst Artificial Intelligence, CH-6962 Lugano, Switzerland

[2] Zenseact AB, Vehicle Mot Planning, S-41756 Gothenburg, Sweden

来源：

IEEE CONTROL SYSTEMS LETTERS | 2023年 / 7卷

关键词：

Constrained control; data driven control; linear parameter-varying systems; robust control;

D O I：

10.1109/LCSYS.2023.3329291

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a direct data-driven approach to synthesize robust control invariant (RCI) sets and their associated gain-scheduled feedback control laws for linear parameter-varying (LPV) systems subjected to bounded disturbances. A data-set consisting of a single state-input-scheduling trajectory is gathered from the system, which is directly utilized to compute polytopic RCI set and controllers by solving a semi-definite program. The proposed method does not require an intermediate LPV model identification step. Through a numerical example, we show that the proposed approach can generate RCI sets with a relatively small number of data samples when the data satisfies certain excitation conditions.

引用

页码：3355 / 3360

页数：6

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