Data-Driven Immersion and Invariance Adaptive Attitude Control for Rigid Bodies With Double-Level State Constraints

Shao, X; Hu, Q; Shi, Y; Yi, B

Data-Driven Immersion and Invariance Adaptive Attitude Control for Rigid Bodies With Double-Level State Constraints

Shao, X Hu, Q Shi, Y Yi, B

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Transactions on Control Systems Technology, 2022, 30, (2), pp. 779-794
Issue Date:: 2022-03-01

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Field	Value	Language
dc.contributor.author	Shao, X
dc.contributor.author	Hu, Q
dc.contributor.author	Shi, Y
dc.contributor.author	Yi, B https://orcid.org/0000-0002-0517-9519
dc.date.accessioned	2023-03-10T04:08:27Z
dc.date.available	2023-03-10T04:08:27Z
dc.date.issued	2022-03-01
dc.identifier.citation	IEEE Transactions on Control Systems Technology, 2022, 30, (2), pp. 779-794
dc.identifier.issn	1063-6536
dc.identifier.issn	1558-0865
dc.identifier.uri	http://hdl.handle.net/10453/166958
dc.description.abstract	This article investigates the attitude control problem of a rigid body subject to attitude and angular rate constraints (double-level state constraints), and inertia uncertainties. A data-driven immersion and invariance (II) adaptive control scheme is proposed to tackle this technically challenging problem. As a stepping stone, a novel dynamically scaled II adaptive controller is designed to bypass the realizability condition that may not hold in the Lyapunov sense when considering angular rate constraints. Lyapunov stability analysis shows that this controller can enable the attitude errors and angular rates to converge asymptotically to zero for most initial conditions in the accessible space, while strictly obeying double-level state constraints with the help of two judiciously constructed potential functions. After that, to further relax the dependence of parameter convergence on the persistent excitation (PE) condition, the II adaptive law is extended to a data-driven counterpart through adding a learning term that is acquired by adopting the regressor filtering in conjunction with the dynamic regressor extension and mixing (DREM) procedure. The extended adaptive controller can not only preserve all the results obtained by the earlier proposed II adaptive controller, but also ensure asymptotic parameter convergence under a finite excitation condition much weaker than PE. In addition, benefiting from the DREM method and some special designs, the parameter convergence rates across all the parameter vector components can be flexibly tuned in an explicit way, and moreover, they are independent of the excitation level. Finally, simulation results are given to show the effectiveness of the proposed method.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Transactions on Control Systems Technology
dc.relation.isbasedon	10.1109/TCST.2021.3076439
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	Data-Driven Immersion and Invariance Adaptive Attitude Control for Rigid Bodies With Double-Level State Constraints
dc.type	Journal Article
utslib.citation.volume	30
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-03-10T04:08:26Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	30
utslib.citation.issue	2

Abstract:

This article investigates the attitude control problem of a rigid body subject to attitude and angular rate constraints (double-level state constraints), and inertia uncertainties. A data-driven immersion and invariance (II) adaptive control scheme is proposed to tackle this technically challenging problem. As a stepping stone, a novel dynamically scaled II adaptive controller is designed to bypass the realizability condition that may not hold in the Lyapunov sense when considering angular rate constraints. Lyapunov stability analysis shows that this controller can enable the attitude errors and angular rates to converge asymptotically to zero for most initial conditions in the accessible space, while strictly obeying double-level state constraints with the help of two judiciously constructed potential functions. After that, to further relax the dependence of parameter convergence on the persistent excitation (PE) condition, the II adaptive law is extended to a data-driven counterpart through adding a learning term that is acquired by adopting the regressor filtering in conjunction with the dynamic regressor extension and mixing (DREM) procedure. The extended adaptive controller can not only preserve all the results obtained by the earlier proposed II adaptive controller, but also ensure asymptotic parameter convergence under a finite excitation condition much weaker than PE. In addition, benefiting from the DREM method and some special designs, the parameter convergence rates across all the parameter vector components can be flexibly tuned in an explicit way, and moreover, they are independent of the excitation level. Finally, simulation results are given to show the effectiveness of the proposed method.

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http://hdl.handle.net/10453/166958