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Neuronal Slidding Mode Output Control Application in a Attitude for Quadrotor
The primary goal of this research report is to investigate the challenging topic of flight dynamics control-regulation over a quadrotor UAV's orientation (attitude) in the presence of wind disturbances and unmodeled dynamics. To do this, we first create a viable output feedback controller-regul...
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| creator | Rodriguez-Mata, A.E. Aurelien Cabarbaye, R. Baray-Arana, R. De Los Rios, P. Acosta-Cano Rodriguez-Rangel, H. |
| description | The primary goal of this research report is to investigate the challenging topic of flight dynamics control-regulation over a quadrotor UAV's orientation (attitude) in the presence of wind disturbances and unmodeled dynamics. To do this, we first create a viable output feedback controller-regulator that incorporates Filters High Gain Observers (FHGO) of ultimate generation, feedback and Radial Basis Function Neural Network (RBF) compensation of estimated disturbances. Furthermore, the usage of reliable sliding mode controllers. Then, despite wind dynamics, noise in sensor outputs, velocity estimates, and common modeling mistakes, a quadrotor UAV is maintained at a given orientation utilizing such a controller. Finally, the effectiveness of the suggested controller is investigated in a simulation framework using a realistic physical model of a quadrotor UAV. |
| doi_str_mv | 10.1109/CCE53527.2021.9633073 |
| format | conference_proceeding |
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Finally, the effectiveness of the suggested controller is investigated in a simulation framework using a realistic physical model of a quadrotor UAV.</description><subject>Attitude control</subject><subject>Computational modeling</subject><subject>Nueronal Networks</subject><subject>Observers</subject><subject>Quadrotor</subject><subject>Reliability</subject><subject>Robust control</subject><subject>Sliddig Mode</subject><subject>State feedback</subject><subject>Uncertainty</subject><issn>2642-3766</issn><isbn>9781665400299</isbn><isbn>1665400293</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2021</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkFFLwzAUhaMgOOZ-gQj5A503uW2S-1jK1MHmEPV5pE0jkdqULH3w3ztwL-e8fBz4DmMPAtZCAD02zabCSuq1BCnWpBBB4xVbkTZCqaoEkETXbCFVKQvUSt2y1en0DQAo6Bxqwfav_ZziaAf-PgTnwvjF99H1_DDnac68iWNOceD1NA2hsznEkYeRW17nHPJ8Bn1M_G22LsUc0x278XY49atLL9nn0-ajeSl2h-dtU--KIAFz4X1nOiyBOlKg7Nmgba1pUVRWeyTpQZXWOA2kS41GGWsUqdIZ4QnIES7Z_f9u6Pv-OKXwY9Pv8XIA_gGO9U6a</recordid><startdate>20211110</startdate><enddate>20211110</enddate><creator>Rodriguez-Mata, A.E.</creator><creator>Aurelien Cabarbaye, R.</creator><creator>Baray-Arana, R.</creator><creator>De Los Rios, P. 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To do this, we first create a viable output feedback controller-regulator that incorporates Filters High Gain Observers (FHGO) of ultimate generation, feedback and Radial Basis Function Neural Network (RBF) compensation of estimated disturbances. Furthermore, the usage of reliable sliding mode controllers. Then, despite wind dynamics, noise in sensor outputs, velocity estimates, and common modeling mistakes, a quadrotor UAV is maintained at a given orientation utilizing such a controller. Finally, the effectiveness of the suggested controller is investigated in a simulation framework using a realistic physical model of a quadrotor UAV.</abstract><pub>IEEE</pub><doi>10.1109/CCE53527.2021.9633073</doi><tpages>7</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2642-3766 |
| ispartof | 2021 18th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE), 2021, p.1-7 |
| issn | 2642-3766 |
| language | eng |
| recordid | cdi_ieee_primary_9633073 |
| source | IEEE Xplore All Conference Series |
| subjects | Attitude control Computational modeling Nueronal Networks Observers Quadrotor Reliability Robust control Sliddig Mode State feedback Uncertainty |
| title | Neuronal Slidding Mode Output Control Application in a Attitude for Quadrotor |
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