A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation

Bo Pang; Zhong Ping Jiang

doi:10.1109/CDC42340.2020.9304046

A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation

Bo Pang, Zhong Ping Jiang

Electrical and Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper presents a data-driven algorithm to solve the problem of infinite-horizon linear quadratic regulation (LQR), for a class of discrete-time linear time-invariant systems subjected to state and control constraints. The problem is divided into a constrained finite-horizon LQR subproblem and an unconstrained infinite-horizon LQR subproblem, which can be solved directly from collected input/state data, separately. Under certain conditions, the combination of the solutions of the subproblems converges to the optimal solution of the original problem. The effectiveness of the proposed approach is validated by a numerical example.

Original language	English (US)
Title of host publication	2020 59th IEEE Conference on Decision and Control, CDC 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6010-6015
Number of pages	6
ISBN (Electronic)	9781728174471
DOIs	https://doi.org/10.1109/CDC42340.2020.9304046
State	Published - Dec 14 2020
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual, Jeju Island, Korea, Republic of Duration: Dec 14 2020 → Dec 18 2020

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2020-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	59th IEEE Conference on Decision and Control, CDC 2020
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	12/14/20 → 12/18/20

ASJC Scopus subject areas

Control and Systems Engineering
Modeling and Simulation
Control and Optimization

Access to Document

10.1109/CDC42340.2020.9304046

Cite this

Pang, B., & Jiang, Z. P. (2020). A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation. In 2020 59th IEEE Conference on Decision and Control, CDC 2020 (pp. 6010-6015). Article 9304046 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC42340.2020.9304046

A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation. / Pang, Bo; Jiang, Zhong Ping.
2020 59th IEEE Conference on Decision and Control, CDC 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 6010-6015 9304046 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2020-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Pang, B & Jiang, ZP 2020, A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation. in 2020 59th IEEE Conference on Decision and Control, CDC 2020., 9304046, Proceedings of the IEEE Conference on Decision and Control, vol. 2020-December, Institute of Electrical and Electronics Engineers Inc., pp. 6010-6015, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 12/14/20. https://doi.org/10.1109/CDC42340.2020.9304046

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AB - This paper presents a data-driven algorithm to solve the problem of infinite-horizon linear quadratic regulation (LQR), for a class of discrete-time linear time-invariant systems subjected to state and control constraints. The problem is divided into a constrained finite-horizon LQR subproblem and an unconstrained infinite-horizon LQR subproblem, which can be solved directly from collected input/state data, separately. Under certain conditions, the combination of the solutions of the subproblems converges to the optimal solution of the original problem. The effectiveness of the proposed approach is validated by a numerical example.

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A Data-driven Approach for Constrained Infinite-Horizon Linear Quadratic Regulation

Abstract

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ASJC Scopus subject areas

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