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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Abstract	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, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI). Unlike the existing output feedback methods for continuous-time systems, the need to apply discrete approximation is obviated. In contrast with the static output feedback controllers, the proposed method can also handle systems that are state feedback stabilizable but not static output feedback stabilizable. An advantage of this scheme is that it stands immune to the exploration bias issue. Moreover, it does not require a discounted cost function and, thus, ensures the closed-loop stability and the optimality of the solution. Compared with earlier output feedback results, the proposed VI method does not require an initially stabilizing policy. We show that the estimates of the control parameters converge to those obtained by solving the LQR algebraic Riccati equation. A comprehensive simulation study is carried out to verify the proposed algorithms.
AbstractList	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, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI). Unlike the existing output feedback methods for continuous-time systems, the need to apply discrete approximation is obviated. In contrast with the static output feedback controllers, the proposed method can also handle systems that are state feedback stabilizable but not static output feedback stabilizable. An advantage of this scheme is that it stands immune to the exploration bias issue. Moreover, it does not require a discounted cost function and, thus, ensures the closed-loop stability and the optimality of the solution. Compared with earlier output feedback results, the proposed VI method does not require an initially stabilizing policy. We show that the estimates of the control parameters converge to those obtained by solving the LQR algebraic Riccati equation. A comprehensive simulation study is carried out to verify the proposed algorithms. 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, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI). Unlike the existing output feedback methods for continuous-time systems, the need to apply discrete approximation is obviated. In contrast with the static output feedback controllers, the proposed method can also handle systems that are state feedback stabilizable but not static output feedback stabilizable. An advantage of this scheme is that it stands immune to the exploration bias issue. Moreover, it does not require a discounted cost function and, thus, ensures the closed-loop stability and the optimality of the solution. Compared with earlier output feedback results, the proposed VI method does not require an initially stabilizing policy. We show that the estimates of the control parameters converge to those obtained by solving the LQR algebraic Riccati equation. A comprehensive simulation study is carried out to verify the proposed algorithms.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, without requiring model information. A state parametrization scheme is presented which reconstructs the system state based on the filtered input and output signals. Based on this parametrization, two new output feedback adaptive dynamic programming Bellman equations are derived for the LQR problem based on policy iteration and value iteration (VI). Unlike the existing output feedback methods for continuous-time systems, the need to apply discrete approximation is obviated. In contrast with the static output feedback controllers, the proposed method can also handle systems that are state feedback stabilizable but not static output feedback stabilizable. An advantage of this scheme is that it stands immune to the exploration bias issue. Moreover, it does not require a discounted cost function and, thus, ensures the closed-loop stability and the optimality of the solution. Compared with earlier output feedback results, the proposed VI method does not require an initially stabilizing policy. We show that the estimates of the control parameters converge to those obtained by solving the LQR algebraic Riccati equation. A comprehensive simulation study is carried out to verify the proposed algorithms.
Author	Rizvi, Syed Ali Asad Lin, Zongli
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Cites_doi	10.1080/00207179.2013.863432 10.1007/s11768-011-0166-4 10.1109/ACC.2016.7526571 10.1109/ACC.1994.735224 10.1016/j.automatica.2018.05.027 10.1109/TCYB.2014.2313915 10.1109/MCAS.2009.933854 10.1016/j.automatica.2008.08.017 10.1109/TCST.2014.2322778 10.1109/TSMCB.2010.2043839 10.1109/MCS.2012.2214134 10.1016/j.automatica.2012.06.096 10.1109/TAC.1968.1098829 10.1109/TAC.2016.2548662 10.1109/TCYB.2014.2384016 10.1016/j.automatica.2016.05.003 10.1109/TCYB.2015.2477810 10.1109/TNN.2009.2027233 10.1109/TAC.2011.2122570 10.1109/CDC.2017.8263836 10.1109/TAC.2016.2616644
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References	ref13 ref24 ref23 ref15 ref14 landelius (ref8) 1997 ref20 ref11 ref22 ref10 ref21 lewis (ref9) 1995 ref2 lewis (ref12) 2012; 32 ref1 ref17 ref16 ref19 ref18 ref7 ref4 ref6 ref5 gao (ref3) 2014
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SubjectTerms	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
Title	Reinforcement Learning-Based Linear Quadratic Regulation of Continuous-Time Systems Using Dynamic Output Feedback
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