Robust Adaptive Safety-Critical Control for Unknown Systems With Finite-Time Elementwise Parameter Estimation

Wang, S; Lyu, B; Wen, S; Shi, K; Zhu, S; Huang, T

Robust Adaptive Safety-Critical Control for Unknown Systems With Finite-Time Elementwise Parameter Estimation

Wang, S Lyu, B Wen, S

Shi, K Zhu, S Huang, T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53, (3), pp. 1607-1617
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wang, S
dc.contributor.author	Lyu, B
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Shi, K
dc.contributor.author	Zhu, S
dc.contributor.author	Huang, T
dc.date.accessioned	2023-02-28T03:56:49Z
dc.date.available	2023-02-28T03:56:49Z
dc.date.issued	2022-01-01
dc.identifier.citation	IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 53, (3), pp. 1607-1617
dc.identifier.issn	2168-2216
dc.identifier.issn	2168-2232
dc.identifier.uri	http://hdl.handle.net/10453/166543
dc.description.abstract	Safety is always one of the most critical principles for a control system. This article investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefiting from the dynamic regressor extension and mixing (DREM), an extended elementwise parameter identification law is utilized to dismiss the uncertainty. It is shown that the proposed control scheme can always ensure safety in the identification process with injected excitation noise. Besides, the elementwise identification process using DREM can minimize the theoretical conservatism of the safe adaptation law compared to other existing adaptive CBF (aCBF) algorithms. The stability of the proposed safe control scheme is proven, where the safety is guaranteed by constructing appropriate forward invariant aCBF. Furthermore, the robustness of our algorithms under bounded disturbances is analyzed. Finally, the proposed framework is tested on two simulation-based examples, including the adaptive cruise control problem where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances, and the potential crash risk is avoided by our safe control scheme. These examples illustrate the effectiveness of our algorithm.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Systems, Man, and Cybernetics: Systems
dc.relation.isbasedon	10.1109/TSMC.2022.3203176
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Robust Adaptive Safety-Critical Control for Unknown Systems With Finite-Time Elementwise Parameter Estimation
dc.type	Journal Article
utslib.citation.volume	53
utslib.for	08 Information and Computing Sciences
utslib.for	09 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/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-28T03:56:48Z
pubs.issue	3
pubs.publication-status	Published
pubs.volume	53
utslib.citation.issue	3

Abstract:

Safety is always one of the most critical principles for a control system. This article investigates a safety-critical control scheme for unknown structured systems by using the control barrier function (CBF) method. Benefiting from the dynamic regressor extension and mixing (DREM), an extended elementwise parameter identification law is utilized to dismiss the uncertainty. It is shown that the proposed control scheme can always ensure safety in the identification process with injected excitation noise. Besides, the elementwise identification process using DREM can minimize the theoretical conservatism of the safe adaptation law compared to other existing adaptive CBF (aCBF) algorithms. The stability of the proposed safe control scheme is proven, where the safety is guaranteed by constructing appropriate forward invariant aCBF. Furthermore, the robustness of our algorithms under bounded disturbances is analyzed. Finally, the proposed framework is tested on two simulation-based examples, including the adaptive cruise control problem where the slope resistance of the following vehicle is robustly estimated in finite time against small disturbances, and the potential crash risk is avoided by our safe control scheme. These examples illustrate the effectiveness of our algorithm.

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http://hdl.handle.net/10453/166543