Least Squares Based Adaptive Control Allocation

Simon Hafner; Barzin Hosseini; Florian Holzapfel

doi:10.1109/CCTA54093.2023.10253298

Least Squares Based Adaptive Control Allocation

Simon Hafner, Barzin Hosseini, Florian Holzapfel

Chair of Flight System Dynamics

Technical University of Munich

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

1 Scopus citations

Abstract

This study presents a Recursive Least Squares (RLS) based approach for adaptive control allocation. The algorithm is premised on the least squares based modification for adaptive control and guarantees convergence under the condition of linear independent data. We modified the adaptation law for control allocation to adapt the control effectiveness vector. The adaptive control allocation is presented with an (extended) Incremental Nonlinear Dynamic Inversion (INDI) controller. We compared two different RLS parameterizations with regularization for handling the nullspace of the input matrix of an overactuated system. The adaptive control allocation is successfully tested in simulation with a nonlinear hexacopter model and under realistic noise levels.

Original language	English
Title of host publication	2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	651-656
Number of pages	6
ISBN (Electronic)	9798350335446
DOIs	https://doi.org/10.1109/CCTA54093.2023.10253298
State	Published - 2023
Event	2023 IEEE Conference on Control Technology and Applications, CCTA 2023 - Bridgetown, Barbados Duration: 16 Aug 2023 → 18 Aug 2023

Publication series

Name	2023 IEEE Conference on Control Technology and Applications, CCTA 2023

Conference

Conference	2023 IEEE Conference on Control Technology and Applications, CCTA 2023
Country/Territory	Barbados
City	Bridgetown
Period	16/08/23 → 18/08/23

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10.1109/CCTA54093.2023.10253298

Cite this

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title = "Least Squares Based Adaptive Control Allocation",

abstract = "This study presents a Recursive Least Squares (RLS) based approach for adaptive control allocation. The algorithm is premised on the least squares based modification for adaptive control and guarantees convergence under the condition of linear independent data. We modified the adaptation law for control allocation to adapt the control effectiveness vector. The adaptive control allocation is presented with an (extended) Incremental Nonlinear Dynamic Inversion (INDI) controller. We compared two different RLS parameterizations with regularization for handling the nullspace of the input matrix of an overactuated system. The adaptive control allocation is successfully tested in simulation with a nonlinear hexacopter model and under realistic noise levels.",

author = "Simon Hafner and Barzin Hosseini and Florian Holzapfel",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Conference on Control Technology and Applications, CCTA 2023 ; Conference date: 16-08-2023 Through 18-08-2023",

year = "2023",

doi = "10.1109/CCTA54093.2023.10253298",

language = "English",

series = "2023 IEEE Conference on Control Technology and Applications, CCTA 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2023 IEEE Conference on Control Technology and Applications, CCTA 2023",

}

Hafner, S, Hosseini, B & Holzapfel, F 2023, Least Squares Based Adaptive Control Allocation. in 2023 IEEE Conference on Control Technology and Applications, CCTA 2023. 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Institute of Electrical and Electronics Engineers Inc., pp. 651-656, 2023 IEEE Conference on Control Technology and Applications, CCTA 2023, Bridgetown, Barbados, 16/08/23. https://doi.org/10.1109/CCTA54093.2023.10253298

Least Squares Based Adaptive Control Allocation. / Hafner, Simon; Hosseini, Barzin; Holzapfel, Florian.
2023 IEEE Conference on Control Technology and Applications, CCTA 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 651-656 (2023 IEEE Conference on Control Technology and Applications, CCTA 2023).

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

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AU - Hosseini, Barzin

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PY - 2023

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N2 - This study presents a Recursive Least Squares (RLS) based approach for adaptive control allocation. The algorithm is premised on the least squares based modification for adaptive control and guarantees convergence under the condition of linear independent data. We modified the adaptation law for control allocation to adapt the control effectiveness vector. The adaptive control allocation is presented with an (extended) Incremental Nonlinear Dynamic Inversion (INDI) controller. We compared two different RLS parameterizations with regularization for handling the nullspace of the input matrix of an overactuated system. The adaptive control allocation is successfully tested in simulation with a nonlinear hexacopter model and under realistic noise levels.

AB - This study presents a Recursive Least Squares (RLS) based approach for adaptive control allocation. The algorithm is premised on the least squares based modification for adaptive control and guarantees convergence under the condition of linear independent data. We modified the adaptation law for control allocation to adapt the control effectiveness vector. The adaptive control allocation is presented with an (extended) Incremental Nonlinear Dynamic Inversion (INDI) controller. We compared two different RLS parameterizations with regularization for handling the nullspace of the input matrix of an overactuated system. The adaptive control allocation is successfully tested in simulation with a nonlinear hexacopter model and under realistic noise levels.

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U2 - 10.1109/CCTA54093.2023.10253298

DO - 10.1109/CCTA54093.2023.10253298

M3 - Conference contribution

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T3 - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023

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BT - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2023 IEEE Conference on Control Technology and Applications, CCTA 2023

Y2 - 16 August 2023 through 18 August 2023

ER -

Least Squares Based Adaptive Control Allocation

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