Kalman Filter Based Adaptive Control Allocation

Simon Hafner, Andrea Dan Ryals, Hangxu Li, Lorenzo Pollini, Florian Holzapfel

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In recent years, various approaches for adaptive control allocation have been published with the goal to enable fault tolerance and improve flight performance of overactuated vehicles. Most of the presented methods are based on Model Reference Adaptive Control (MRAC) techniques and/or recursive least squares. In this study, we present a novel strategy combining concurrent learning MRAC and Kalman filtering to adapt the control effectiveness vector. The proposed strategy incorporates short term updates of the direct update as well as stationary correction through the Kalman filter. The adaptive control allocation has been evaluated using a hexacopter high fidelity simulation model. The hexacopter is controlled by an Incremental Nonlinear Dynamic Inversion (INDI) velocity controller and by a backstepping controller. In the simulation, we demonstrated the advantages of the adaptive control allocation for partial loss of effectiveness of one rotor and in nominal conditions. The adaptive control allocation avoided the crash of the hexacopter and improved tracking in forward flight compensating for effectiveness changes.

Original languageEnglish
Title of host publication2024 32nd Mediterranean Conference on Control and Automation, MED 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages113-118
Number of pages6
ISBN (Electronic)9798350395440
DOIs
StatePublished - 2024
Event32nd Mediterranean Conference on Control and Automation, MED 2024 - Chania, Crete, Greece
Duration: 11 Jun 202414 Jun 2024

Publication series

Name2024 32nd Mediterranean Conference on Control and Automation, MED 2024

Conference

Conference32nd Mediterranean Conference on Control and Automation, MED 2024
Country/TerritoryGreece
CityChania, Crete
Period11/06/2414/06/24

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