Online Output-Feedback Optimal Control of Linear Systems Based on Data-Driven Adaptive Learning⁎This work was supported by National Natural Science Foundation of China (NSFC) under Grants 61922037 and 61873115. (Corresponding author: Jing Na). Issue 2 (2020)

• Record Type: Journal Article
• Authors: Zhao, Jun; Na, Jing; Gao, Guanbin; Han, Shichang; Chen, Qiang; Wang, Shubo
• Is Part Of: IFAC-PapersOnLine. Volume 53:Issue 2(2020)
• Page Start: 1596
• Page End: 1601
• Publication Date: 2020
• Subjects: Adaptive optimal control -- output-feedback control -- adaptive control -- data-driven learning -- Kronecker's product; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2020.12.2201
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: 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.