Reinforcement Learning-Based Linear Quadratic Regulation of Continuous-Time Systems Using Dynamic Output Feedback

In this paper, we propose a model-free solution to the linear quadratic regulation (LQR) problem of continuous-time systems based on reinforcement learning using dynamic output feedback. The design objective is to learn the optimal control parameters by using only the measurable input-output data, w...

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Veröffentlicht in:	IEEE transactions on cybernetics Jg. 50; H. 11; S. 4670 - 4679
Hauptverfasser:	Rizvi, Syed Ali Asad, Lin, Zongli
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.11.2020 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Adaptive dynamic programming (ADP) Algorithms Continuous time systems Cost function Dynamic programming Feedback control Iterative methods Learning Linear quadratic regulator linear quadratic regulator (LQR) Mathematical model Mathematical models Optimal control Optimization Output feedback Parameter estimation Parameterization reinforcement learning (RL) Riccati equation Stability analysis State feedback
ISSN:	2168-2267, 2168-2275, 2168-2275
Online-Zugang:	Volltext
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