A Hammerstein-Weiner Modification of Adaptive Autopilot for Parameter Drift Mitigation with Experimental Results

A crucial challenge in the safe operation of adaptive controllers is the problem of parameter drift, where an underlying optimization problem, if ill-conditioned, may lead to parameter drift. This paper presents a Wiener adaptive autopilot for multicopters to mitigate instabilities caused by adaptive parameter drift and presents simulation and experimental results to validate the modified autopilot. The modified adaptive controller is obtained by including a static nonlinearity in the adaptive loop, updated by the retrospective cost adaptive control algorithm. It is shown in simulation and physical test experiments that the adaptive autopilot with proposed modifications can continually improve the fixed-gain autopilot as well as prevent the drift of the adaptive parameters, thus improving the robustness of the adaptive autopilot.

Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2024-09-05
DOI
https://doi.org/10.13016/m220b5-mele
Primary Topic
Mycorrhizal Fungi and Plant Interactions
Type
article
Field-Weighted Citation Impact
0.00

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article

A Hammerstein-Weiner Modification of Adaptive Autopilot for Parameter Drift Mitigation with Experimental Results

Maryland Shared Open Access Repository (USMAI Consortium)
Mycorrhizal Fungi and Plant Interactions
article

A Hammerstein-Weiner Modification of Adaptive Autopilot for Parameter Drift Mitigation with Experimental Results

article en

Abstract

A crucial challenge in the safe operation of adaptive controllers is the problem of parameter drift, where an underlying optimization problem, if ill-conditioned, may lead to parameter drift. This paper presents a Wiener adaptive autopilot for multicopters to mitigate instabilities caused by adaptive parameter drift and presents simulation and experimental results to validate the modified autopilot. The modified adaptive controller is obtained by including a static nonlinearity in the adaptive loop, updated by the retrospective cost adaptive control algorithm. It is shown in simulation and physical test experiments that the adaptive autopilot with proposed modifications can continually improve the fixed-gain autopilot as well as prevent the drift of the adaptive parameters, thus improving the robustness of the adaptive autopilot.

Maryland Shared Open Access Repository (USMAI Consortium)
Office of Naval Research
Climate action
Openalex Percentile: Top 53%
Mycorrhizal Fungi and Plant Interactions
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