Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes
This paper presents a robust position/attitude tracking control method for a fully-actuated hexarotor unmanned aerial vehicle (UAV) based on Gaussian processes. Multirotor UAVs suffer from modelling errors due to their structure complexity and aerodynamical disturbances whose perfect mathematical fo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-06-01
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| Series: | SICE Journal of Control, Measurement, and System Integration |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/18824889.2022.2125242 |
| _version_ | 1797661036437831680 |
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| author | Tatsuya Ibuki Hiroto Yoshioka Mitsuji Sampei |
| author_facet | Tatsuya Ibuki Hiroto Yoshioka Mitsuji Sampei |
| author_sort | Tatsuya Ibuki |
| collection | DOAJ |
| description | This paper presents a robust position/attitude tracking control method for a fully-actuated hexarotor unmanned aerial vehicle (UAV) based on Gaussian processes. Multirotor UAVs suffer from modelling errors due to their structure complexity and aerodynamical disturbances whose perfect mathematical formulation is intractable. To handle this issue, this paper incorporates a data-based learning technique with model-based control. The hexarotor UAV dynamical model, considering modelling errors and aerodynamic disturbances as unknown dynamics, is first derived. Gaussian process regression is next introduced as a learning method for the unknown dynamics, which provides probabilistic distributions of the predicted values. The predicted means are regarded as deterministic information and cancelled out by feedforward control inputs. The predicted variances are considered as the bounds of the model uncertainties with high probability, and a robust control method to ensure ultimate boundedness of the tracking control error is proposed for the uncertain system. The effectiveness of the proposed method is demonstrated via experiments with a self-developed hexarotor UAV testbed. |
| first_indexed | 2024-03-11T18:39:33Z |
| format | Article |
| id | doaj.art-1eaae09b69d24aaaa51e25894bf5314c |
| institution | Directory Open Access Journal |
| issn | 1884-9970 |
| language | English |
| last_indexed | 2024-03-11T18:39:33Z |
| publishDate | 2022-06-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | SICE Journal of Control, Measurement, and System Integration |
| spelling | doaj.art-1eaae09b69d24aaaa51e25894bf5314c2023-10-12T13:43:52ZengTaylor & Francis GroupSICE Journal of Control, Measurement, and System Integration1884-99702022-06-0115220121010.1080/18824889.2022.21252422125242Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processesTatsuya Ibuki0Hiroto Yoshioka1Mitsuji Sampei2Meiji UniversityeSOL Co., Ltd.Tokyo Institute of TechnologyThis paper presents a robust position/attitude tracking control method for a fully-actuated hexarotor unmanned aerial vehicle (UAV) based on Gaussian processes. Multirotor UAVs suffer from modelling errors due to their structure complexity and aerodynamical disturbances whose perfect mathematical formulation is intractable. To handle this issue, this paper incorporates a data-based learning technique with model-based control. The hexarotor UAV dynamical model, considering modelling errors and aerodynamic disturbances as unknown dynamics, is first derived. Gaussian process regression is next introduced as a learning method for the unknown dynamics, which provides probabilistic distributions of the predicted values. The predicted means are regarded as deterministic information and cancelled out by feedforward control inputs. The predicted variances are considered as the bounds of the model uncertainties with high probability, and a robust control method to ensure ultimate boundedness of the tracking control error is proposed for the uncertain system. The effectiveness of the proposed method is demonstrated via experiments with a self-developed hexarotor UAV testbed.http://dx.doi.org/10.1080/18824889.2022.2125242gaussian processmultirotor uavlearning-based controlrobust controlnonlinear control |
| spellingShingle | Tatsuya Ibuki Hiroto Yoshioka Mitsuji Sampei Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes SICE Journal of Control, Measurement, and System Integration gaussian process multirotor uav learning-based control robust control nonlinear control |
| title | Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes |
| title_full | Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes |
| title_fullStr | Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes |
| title_full_unstemmed | Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes |
| title_short | Robust pose tracking control for a fully-actuated hexarotor UAV based on Gaussian processes |
| title_sort | robust pose tracking control for a fully actuated hexarotor uav based on gaussian processes |
| topic | gaussian process multirotor uav learning-based control robust control nonlinear control |
| url | http://dx.doi.org/10.1080/18824889.2022.2125242 |
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