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Type | Conference or Workshop Paper |
Scope | Discipline-based scholarship |
Published in Proceedings | Yes |
Title | Onboard State Dependent LQR for Agile Quadrotors |
Organization Unit | |
Authors |
|
Presentation Type | paper |
Item Subtype | Original Work |
Refereed | Yes |
Status | Published in final form |
Language |
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Page Range | 1 - 8 |
Event Title | IEEE International Conference on Robotics and Automation (ICRA), 2018. |
Event Type | conference |
Event Location | Brisbane |
Event Start Date | May 21 - 2018 |
Event End Date | May 25 - 2018 |
Place of Publication | IEEE International Conference on Robotics and Automation (ICRA), 2018. |
Publisher | IEEE |
Abstract Text | State-of-the-art approaches in quadrotor control split the problem into multiple cascaded subproblems, exploiting the different time scales of the rotational and translational dynamics. They calculate a desired acceleration as input for a cascaded attitude controller but omit the attitude dynamics. These approaches use limits on the desired acceleration to maintain feasibility and robustness through the control cascade. We propose an implementation of an LQR controller, which: (I) is linearized depending on the quadrotor’s state; (II) unifies the control of rotational and translational states; (III) handles time-varying system dynamics and control parameters. Our implementation is efficient enough to compute the full linearization and solution of the LQR at a minimum of 10Hz on the vehicle using a common ARM processor. We show four successful experiments: (I) controlling at hover state with large disturbances; (II) tracking along a trajectory; (III) tracking along an infeasible trajectory; (IV) tracking along a trajectory with disturbances. All the experiments were done using only onboard visual inertial state estimation and LQR computation. To the best of our knowledge, this is the first implementation and evaluation of a state-dependent LQR capable of onboard computation while providing this amount of versatility and performance. Video of the experiments: https://youtu.be/8OVsJNgNfa0 Narrated video presentation: https://youtu.be/c7gHF-NJjPo |
Zusammenfassung | State-of-the-art approaches in quadrotor control split the problem into multiple cascaded subproblems, exploiting the different time scales of the rotational and translational dynamics. They calculate a desired acceleration as input for a cascaded attitude controller but omit the attitude dynamics. These approaches use limits on the desired acceleration to maintain feasibility and robustness through the control cascade. We propose an implementation of an LQR controller, which: (I) is linearized depending on the quadrotor’s state; (II) unifies the control of rotational and translational states; (III) handles time-varying system dynamics and control parameters. Our implementation is efficient enough to compute the full linearization and solution of the LQR at a minimum of 10Hz on the vehicle using a common ARM processor. We show four successful experiments: (I) controlling at hover state with large disturbances; (II) tracking along a trajectory; (III) tracking along an infeasible trajectory; (IV) tracking along a trajectory with disturbances. All the experiments were done using only onboard visual inertial state estimation and LQR computation. To the best of our knowledge, this is the first implementation and evaluation of a state-dependent LQR capable of onboard computation while providing this amount of versatility and performance. Video of the experiments: https://youtu.be/8OVsJNgNfa0 Narrated video presentation: https://youtu.be/c7gHF-NJjPo |
Official URL | http://rpg.ifi.uzh.ch/docs/ICRA18_Foehn.pdf |
Other Identification Number | merlin-id:16268 |
PDF File | Download from ZORA |
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