Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter

Weinan Gao; Zhong Ping Jiang

doi:10.1109/ACC.2016.7525294

Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter

Weinan Gao, Zhong Ping Jiang

Electrical and Computer Engineering

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

Abstract

This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information. The convergence of the proposed approach is theoretically ensured and the tradeoff between optimality and sampling period is rigorously studied as well. Finally, we show the performance of the proposed algorithm under bounded model uncertainties.

Original language	English (US)
Title of host publication	2016 American Control Conference, ACC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2512-2517
Number of pages	6
ISBN (Electronic)	9781467386821
DOIs	https://doi.org/10.1109/ACC.2016.7525294
State	Published - Jul 28 2016
Event	2016 American Control Conference, ACC 2016 - Boston, United States Duration: Jul 6 2016 → Jul 8 2016

Publication series

Name	Proceedings of the American Control Conference
Volume	2016-July
ISSN (Print)	0743-1619

Other

Other	2016 American Control Conference, ACC 2016
Country/Territory	United States
City	Boston
Period	7/6/16 → 7/8/16

ASJC Scopus subject areas

Electrical and Electronic Engineering

Access to Document

10.1109/ACC.2016.7525294

Cite this

Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter. / Gao, Weinan; Jiang, Zhong Ping.
2016 American Control Conference, ACC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2512-2517 7525294 (Proceedings of the American Control Conference; Vol. 2016-July).

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

Gao, W & Jiang, ZP 2016, Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter. in 2016 American Control Conference, ACC 2016., 7525294, Proceedings of the American Control Conference, vol. 2016-July, Institute of Electrical and Electronics Engineers Inc., pp. 2512-2517, 2016 American Control Conference, ACC 2016, Boston, United States, 7/6/16. https://doi.org/10.1109/ACC.2016.7525294

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abstract = "This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information. The convergence of the proposed approach is theoretically ensured and the tradeoff between optimality and sampling period is rigorously studied as well. Finally, we show the performance of the proposed algorithm under bounded model uncertainties.",

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AB - This paper studies a data-driven adaptive optimal control problem of a Quanser's 2-degree-of-freedom (DOF) helicopter via output-feedback. A novel sampled-data-based approximate/adaptive dynamic programming (ADP) approach is developed. We start from a stabilizing controller computed using the bound of model uncertainties. Then the optimal control gain is iteratively learned by input/output information. The convergence of the proposed approach is theoretically ensured and the tradeoff between optimality and sampling period is rigorously studied as well. Finally, we show the performance of the proposed algorithm under bounded model uncertainties.

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Data-driven adaptive optimal output-feedback control of a 2-DOF helicopter

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