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|Title:||Adaptive optimal output tracking of continuous-time systems via output-feedback-based reinforcement learning||Authors:||Chen, Ci
Lewis, Frank L.
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Source:||Chen, C., Xie, L., Xie, K., Lewis, F. L. & Xie. Shengli (2022). Adaptive optimal output tracking of continuous-time systems via output-feedback-based reinforcement learning. Automatica, 146, 110581-. https://dx.doi.org/10.1016/j.automatica.2022.110581||Journal:||Automatica||Abstract:||Reinforcement learning provides a powerful tool for designing a satisfactory controller through interactions with the environment. Although off-policy learning algorithms were recently designed for tracking problems, most of these results either are full-state feedback or have bounded control errors, which may not be flexible or desirable for engineering problems in the real world. To address these problems, we propose an output-feedback-based reinforcement learning approach that allows us to find the optimal control solution using input–output data and ensure the asymptotic tracking control of continuous-time systems. More specifically, we first propose a dynamical controller revised from the standard output regulation theory and use it to formulate an optimal output tracking problem. Then, a state observer is used to re-express the system state. Consequently, we address the rank issue of the parameterization matrix and analyze the state re-expression error that are crucial for transforming the off-policy learning into an output-feedback form. A comprehensive simulation study is given to demonstrate the effectiveness of the proposed approach.||URI:||https://hdl.handle.net/10356/163293||ISSN:||0005-1098||DOI:||10.1016/j.automatica.2022.110581||Schools:||School of Electrical and Electronic Engineering||Rights:||© 2022 Elsevier Ltd. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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