Online Output-Feedback Optimal Control of Linear Systems Based on Data-Driven Adaptive Learning

被引：0

作者：

Zhao, Jun ^{[1
]}

Na, Jing ^{[1
]}

Gao, Guanbin ^{[1
]}

Han, Shichang ^{[1
]}

Chen, Qiang ^{[2
]}

Wang, Shubo ^{[3
]}

机构：

[1] Kunming Univ Sci & Technol, Fac Mech & Elect Engn, Kunming 650500, Yunnan, Peoples R China

[2] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310023, Peoples R China

[3] Qingdao Univ, Sch Automat, Qingdao 266071, Peoples R China

来源：

IFAC PAPERSONLINE | 2020年 / 53卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Adaptive optimal control; output-feedback control; adaptive control; data-driven learning; Kronecker's product;

D O I：

10.1016/j.ifacol.2020.12.2201

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a new approach to solve the output-feedback optimal control for linear systems. A modified algebraic Riccati equation (MARE) is constructed by investigating the corresponding relationship with the state-feedback optimal control. To solve the derived MARE, an online data-driven adaptive learning is designed, where the vectorization operation and Kronecker's product are applied to reformulate the output Lyapunov function. Consequently, only the measurable system input and output are used to derive the solution of the MARE. In this case, the output-feedback optimal control solution can be obtained in an online manner without resorting to the unknown system states. Simulation results are provided to demonstrate the efficacy of the suggested method. Copyright (C) 2020 The Authors.

引用

页码：1596 / 1601

页数：6

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