Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances

Antonelli, Gianluca; Arrichiello, Filippo; Chiaverini, Stefano; Giordano, Paolo Robuffo

doi:10.1109/aim.2013.6584280

Cited by 63 publications

(65 citation statements)

References 17 publications

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“…Since temporal tracking requires the system to follow the trajectory with exact computed timing, it is often less robust against disturbances and model uncertainties. Therefore, we chose spatial tracking, similarly to the approach in [17]. In each control loop, we search on the remaining trajectory for the reference state that minimizes the spatial distance to the actual vehicle position.…”

Section: E Trajectory Tracking Schemementioning

confidence: 99%

Onboard State Dependent LQR for Agile Quadrotors

Foehn

Scaramuzza

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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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 timevarying 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.

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Section: E Trajectory Tracking Schemementioning

confidence: 99%

Onboard State Dependent LQR for Agile Quadrotors

Foehn

Scaramuzza

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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“…They proved that the adaptive controller is able to stabilise the quad-rotor and compensate for any changes in the centre of gravity, while the PD controller and feedback linearisation controller are not able to cope with dynamic changes in the centre of gravity. The same technique was implemented as Antonelli et al (2013) considered some external disturbance in quad-rotor design such as the possibility of wrong estimation of the centre of mass.…”

Section: Related Workmentioning

confidence: 99%

Controlling an unmanned quad-rotor aerial vehicle with model parameter uncertainty and actuator failure

Vial

Dong

Alshbatat

2016

IJISTA

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It is challenging to stabilise an unmanned quad-rotor aerial vehicle when a dynamic change in its model parameters or failure of its actuator occurs. In this paper, a quad-rotor unmanned aerial vehicle (UAV) is controlled based on model reference adaptive control (MRAC) and a linear quadratic regulator (LQR). The kinematics and dynamics of the quad-rotor are calculated, and Lyapunov's direct stability method is used to design the MRAC. In order to evaluate the performance of the adaptive control algorithms in the presence of thrust loss that may occur due to component failure or physical damage, a real quad-rotor is built from scratch using commercial components. Both controllers are designed, implemented and tested using AVR microcontrollers. Comparison is made between the controllers under normal and faulty situations and the effectiveness of the proposed control strategy is verified. Simulation and experimental results show that both controllers have satisfactory performance under normal conditions and even in the presence of the partial loss of thrust that may occur due to the loss of control effectiveness in one of the rotors or the damage of one propeller, superior system performance is observed using the proposed MRAC controller.

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“…Research effort on AM studies aerial vehicles equipped with one or more robotic arms, allowing to describe those systems as Aerial Robots (ARs). This research line has been particularly fostered by European projects like ARCAS, AeroArms, and AeroWorks 1 , leading to results in mechanical design of new aerial platforms [1], [2], control analysis [3], [4] visual perception for AM [5], [6], and planning [7], [8]. The literature also encompasses the use of VTOLs to transport loads and interact by means of cables, see [9]- [11] and references therein.…”

Section: Introductionmentioning

confidence: 99%

Towards a Flying Assistant Paradigm: the OTHex

Staub

Bicego

Sablé

et al. 2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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This paper presents the OTHex platform for aerial manipulation developed at LAAS-CNRS. The OTHex is probably the first multi-directional thrust platform designed to act as Flying Assistant which can aid human operators and/or Ground Manipulators to move long bars for assembly and maintenance tasks. The work emphasis is on task-driven custom design and experimental validations. The proposed control framework is built around a low-level geometric controller, and includes an external wrench estimator, an admittance filter, and a trajectory generator. This tool gives the system the necessary compliance to resist external force disturbances arising from contact with the surrounding environment or to parameter uncertainties in the load. A set of experiments validates the real-world applicability and robustness of the overall system.

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Adaptive trajectory tracking for quadrotor MAVs in presence of parameter uncertainties and external disturbances

Cited by 63 publications

References 17 publications

Onboard State Dependent LQR for Agile Quadrotors

Onboard State Dependent LQR for Agile Quadrotors

Controlling an unmanned quad-rotor aerial vehicle with model parameter uncertainty and actuator failure

Towards a Flying Assistant Paradigm: the OTHex

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