Incremental nonlinear dynamic inversion with sparse online Gaussian processes adaptation for partially unknown systems

Simple item page
dc.contributor.authorIgnatyev, Dmitry
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2022-08-03T14:48:04Z
dc.date.available2022-08-03T14:48:04Z
dc.date.issued2022-08-01
dc.description.abstractSensor-based Incremental control is a recently developed family of techniques with a reduced dependency on a plant model. This approach uses measurements or estimates of current state derivatives and actuator states to linearize the dynamics with respect to the previous time moment. However, in such a formulation, the control system is sensitive to the quality of measurements or estimations. The presence of uncertainties caused by unforeseen malfunctions in measurement and/or actuation systems could provoke drastic performance degradation. The paper proposes a sensor-based Incremental Nonlinear Dynamic Inversion (INDI) control algorithm augmented with Budgeted Sparse Online Gaussian Processes Adaptation for the compensation of unknown system behaviour. INDI performs quite efficiently under design conditions. Meanwhile, GP-based direct adaptation provides not only long-term dependency learning but also noise signal filtering. The efficiency of the proposed approach is demonstrated with a longitudinal motion of a missile.en_UK
dc.identifier.citationIgnatyev D, Tsourdos A. (2022) Incremental nonlinear dynamic inversion with sparse online Gaussian processes adaptation for partially unknown systems. In: 2022 30th Mediterranean Conference on Control and Automation (MED), 28 June - 1 July 2022, Vouliagmeni, Greece, pp. 233-238en_UK
dc.identifier.eisbn978-1-6654-0673-4
dc.identifier.isbn978-1-6654-0674-1
dc.identifier.issn2325-369X
dc.identifier.urihttps://doi.org/10.1109/MED54222.2022.9837175
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18278
dc.language.isoenen_UK
dc.publisherIEEEen_UK
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectGaussian processesen_UK
dc.subjectData-drivenen_UK
dc.subjectSensor baseden_UK
dc.subjectNonlinear control systemsen_UK
dc.subjectuncertaintyen_UK
dc.titleIncremental nonlinear dynamic inversion with sparse online Gaussian processes adaptation for partially unknown systemsen_UK
dc.typeConference paperen_UK

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Incremental_nonlinear_dynamic_inversion-2022.pdf
Size:
316 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.63 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Staff publications (SATM)