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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Vydané v:IEEE transactions on cybernetics Ročník 50; číslo 11; s. 4670 - 4679
Hlavní autori: Rizvi, Syed Ali Asad, Lin, Zongli
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.11.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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