On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics
Increasing 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 miss...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/10/2/187 |
| _version_ | 1797411107192700928 |
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| author | Natalia Strawa Dmitry I. Ignatyev Argyrios C. Zolotas Antonios Tsourdos |
| author_facet | Natalia Strawa Dmitry I. Ignatyev Argyrios C. Zolotas Antonios Tsourdos |
| author_sort | Natalia Strawa |
| collection | DOAJ |
| description | Increasing 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 missions. |
| first_indexed | 2024-03-09T04:40:13Z |
| format | Article |
| id | doaj.art-93a9594ea20b402089b3248fa2465d23 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T04:40:13Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-93a9594ea20b402089b3248fa2465d232023-12-03T13:21:59ZengMDPI AGElectronics2079-92922021-01-0110218710.3390/electronics10020187On-Line Learning and Updating Unmanned Tracked Vehicle DynamicsNatalia Strawa0Dmitry I. Ignatyev1Argyrios C. Zolotas2Antonios Tsourdos3Centre for Autonomous and Cyber-Physical Systems, SATM, Cranfield University, Cranfield MK43 0AL, UKCentre for Autonomous and Cyber-Physical Systems, SATM, Cranfield University, Cranfield MK43 0AL, UKCentre for Autonomous and Cyber-Physical Systems, SATM, Cranfield University, Cranfield MK43 0AL, UKCentre for Autonomous and Cyber-Physical Systems, SATM, Cranfield University, Cranfield MK43 0AL, UKIncreasing 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 missions.https://www.mdpi.com/2079-9292/10/2/187unmanned tracked vehicleinertial parametersvehicle-terrain interactionidentificationrecursive least square with exponential forgettinggeneralized Newton–Raphson |
| spellingShingle | Natalia Strawa Dmitry I. Ignatyev Argyrios C. Zolotas Antonios Tsourdos On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics Electronics unmanned tracked vehicle inertial parameters vehicle-terrain interaction identification recursive least square with exponential forgetting generalized Newton–Raphson |
| title | On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics |
| title_full | On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics |
| title_fullStr | On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics |
| title_full_unstemmed | On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics |
| title_short | On-Line Learning and Updating Unmanned Tracked Vehicle Dynamics |
| title_sort | on line learning and updating unmanned tracked vehicle dynamics |
| topic | unmanned tracked vehicle inertial parameters vehicle-terrain interaction identification recursive least square with exponential forgetting generalized Newton–Raphson |
| url | https://www.mdpi.com/2079-9292/10/2/187 |
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