期刊
IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY
卷 23, 期 1, 页码 264-273出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCST.2014.2322778
关键词
Integral reinforcement learning (IRL); linear continuous-time (CT) systems; optimal control; output feedback
资金
- National Science Foundation [ECCS-1128050, IIS-1208623]
- Office of Naval Research [N00014-13-1-0562]
- European Office of Aerospace Research and Development, Air Force Office of Scientific Research [13-3055]
- National Natural Science Foundation of China [61120106011]
- China Education Ministry Project 111 [B08015]
- Div Of Electrical, Commun & Cyber Sys
- Directorate For Engineering [1128050, 1405173] Funding Source: National Science Foundation
- Div Of Information & Intelligent Systems
- Direct For Computer & Info Scie & Enginr [1208623] Funding Source: National Science Foundation
Reinforcement learning (RL) techniques have been successfully used to find optimal state-feedback controllers for continuous-time (CT) systems. However, in most real-world control applications, it is not practical to measure the system states and it is desirable to design output-feedback controllers. This paper develops an online learning algorithm based on the integral RL (IRL) technique to find a suboptimal output-feedback controller for partially unknown CT linear systems. The proposed IRL-based algorithm solves an IRL Bellman equation in each iteration online in real time to evaluate an output-feedback policy and updates the output-feedback gain using the information given by the evaluated policy. The knowledge of the system drift dynamics is not required by the proposed method. An adaptive observer is used to provide the knowledge of the full states for the IRL Bellman equation during learning. However, the observer is not needed after the learning process is finished. The convergence of the proposed algorithm to a suboptimal output-feedback solution and the performance of the proposed method are verified through simulation on two real-world applications, namely, the X-Y table and the F-16 aircraft.
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