Online Policy Learning-Based Output-Feedback Optimal Control of Continuous-Time Systems

被引：36

作者：

Zhao, Jun ^{[1
]}

Lv, Yongfeng ^{[2
]}

Zeng, Qingliang ^{[1
,3
]}

Wan, Lirong ^{[1
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Mech & Elect Engn, Qingdao 266590, Peoples R China

[2] Taiyuan Univ Technol, Coll Elect & Power Engn, Taiyuan 030024, Peoples R China

[3] Shandong Normal Univ, Dept Informat Sci & Engn, Jinan 250358, Peoples R China

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS | 2024年 / 71卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Optimal control; Mathematical models; Heuristic algorithms; Atmospheric modeling; Riccati equations; Observers; Convergence; Output-feedback control; optimal control; policy learning; continuous-time systems; LINEAR-SYSTEMS;

D O I：

10.1109/TCSII.2022.3211832

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Although state-feedback optimal control of the continuous-time (CT) systems has been extensively studied, resolving optimal control online via output-feedback is still challenging, especially only input-output information can be used. In this brief, we develop an innovative technique to online design the output-feedback optimal control (OFOC) of the CT systems. Firstly, to synthesis the OFOC, an output-feedback algebraic Riccati equation (OARE) is constructed, which can be solved using input-output information. Then, an online policy learning (PL) algorithm is developed to compute the solution of the OARE, where only the input-output information is required and the conventional offline learning procedure is avoided. Simulations based on an aircraft model are provided to test the developed control method and online learning algorithm.

引用

页码：652 / 656

页数：5

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