Gaussian process adaptive incremental backstepping flight control

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dc.contributor.author Ignatyev, Dmitry I.
dc.contributor.author Shin, Hyo-Sang
dc.contributor.author Tsourdos, Antonios
dc.date.accessioned 2022-02-09T16:47:11Z
dc.date.available 2022-02-09T16:47:11Z
dc.date.issued 2021-12-29
dc.identifier.citation Ignatyev D, Shin H-S, Tsourdos A. (2021) Gaussian process adaptive incremental backstepping flight control. In: AIAA SciTech 2022 Forum, 3-7 January 2022, San Diego, CA, USA and Virtual Event en_UK
dc.identifier.uri https://doi.org/10.2514/6.2022-2032
dc.identifier.uri https://dspace.lib.cranfield.ac.uk/handle/1826/17558
dc.description.abstract The presence of uncertainties caused by unforeseen malfunctions in the actuation system or changes in aircraft behaviour could lead to aircraft loss of control during flight. The paper proposes almost model-independent control law combining recent developments in nonlinear control theory, data-driven methods, and sensor technologies by considering Gaussian Processes Adaptive augmentation for Incremental Backstepping control (IBKS) algorithm. IBKS uses angular accelerations and current control deflections to reduce the dependency on the aircraft model. However, it requires knowledge of control effectiveness. Conducted research shows that if the input-affine property of the IBKS is violated, e.g., in severe conditions with a combination of multiple failures, the IBKS can lose stability. Meanwhile, the GP-based estimator provides fast identification and the resultant GP-adaptive IBKS algorithm demonstrates improved stability and tracking performance. The performance of the algorithm is validated using a large transport aircraft flight dynamics model. en_UK
dc.language.iso en en_UK
dc.publisher AIAA en_UK
dc.rights Attribution-NonCommercial 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/ *
dc.title Gaussian process adaptive incremental backstepping flight control en_UK
dc.type Conference paper en_UK


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