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Onboard State Dependent LQR for Agile Quadrotors


Föhn, Philipp; Scaramuzza, Davide (2018). Onboard State Dependent LQR for Agile Quadrotors. In: IEEE International Conference on Robotics and Automation (ICRA), 2018., Brisbane, 21 May 2018 - 25 May 2018, 1-8.

Abstract

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

Abstract

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

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:25 May 2018
Deposited On:22 Mar 2018 11:58
Last Modified:13 Apr 2018 11:43
Publisher:IEEE
OA Status:Green
Official URL:http://rpg.ifi.uzh.ch/docs/ICRA18_Foehn.pdf
Other Identification Number:merlin-id:16268

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