On-line learning and updating unmanned tracked vehicle dynamics

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dc.contributor.authorStrawa, Natalia
dc.contributor.authorIgnatyev, Dmitry I.
dc.contributor.authorZolotas, Argyrios C.
dc.contributor.authorTsourdos, Antonios
dc.date.accessioned2021-01-27T17:57:28Z
dc.date.available2021-01-27T17:57:28Z
dc.date.issued2021-01-15
dc.description.abstractIncreasing levels of autonomy impose more pronounced performance requirements for unmanned ground vehicles (UGV). Presence of model uncertainties significantly reduces a ground vehicle performance when the vehicle is traversing an unknown terrain or the vehicle inertial parameters vary due to a mission schedule or external disturbances. A comprehensive mathematical model of a skid steering tracked vehicle is presented in this paper and used to design a control law. Analysis of the controller under model uncertainties in inertial parameters and in the vehicle-terrain interaction revealed undesirable behavior, such as controller divergence and offset from the desired trajectory. A compound identification scheme utilizing an exponential forgetting recursive least square, generalized Newton–Raphson (NR), and Unscented Kalman Filter methods is proposed to estimate the model parameters, such as the vehicle mass and inertia, as well as parameters of the vehicle-terrain interaction, such as slip, resistance coefficients, cohesion, and shear deformation modulus on-line. The proposed identification scheme facilitates adaptive capability for the control system, improves tracking performance and contributes to an adaptive path and trajectory planning framework, which is essential for future autonomous ground vehicle missionsen_UK
dc.identifier.citationStrawa N, Ignatyev DI, Zolotas AC, Tsourdos A. (2021) On-line learning and updating unmanned tracked vehicle dynamics. Electronics, Volume 10, Issue 2, 2021, Article number 187en_UK
dc.identifier.issn2079-9292
dc.identifier.urihttps://doi.org/10.3390/electronics10020187
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/16271
dc.language.isoenen_UK
dc.publisherMDPIen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectUnscented Kalman Filteren_UK
dc.subjectgeneralized Newton–Raphsonen_UK
dc.subjectrecursive least square with exponential forgettingen_UK
dc.subjectvehicle-terrain interactionen_UK
dc.subjectunmanned tracked vehicleen_UK
dc.titleOn-line learning and updating unmanned tracked vehicle dynamicsen_UK
dc.typeArticleen_UK

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