10 research outputs found
A flow disturbance estimation and rejection strategy for multirotors with round-trip trajectories
This paper presents a round-trip strategy of multirotors subject to unknown
flow disturbances. During the outbound flight, the vehicle immediately utilizes
the wind disturbance estimations in feedback control, as an attempt to reduce
the tracking error. During this phase, the disturbance estimations with respect
to the position are also recorded for future use. For the return flight, the
disturbances previously collected are then routed through a feedforward
controller. The major assumption here is that the disturbances may vary over
space, but not over time during the same mission. We demonstrate the
effectiveness of this feedforward strategy via experiments with two different
types of wind flows; a simple jet flow and a more complex flow. To use as a
baseline case, a cascaded PD controller with an additional feedback loop for
disturbance estimation was employed for outbound flights. To display our
contributions regarding the additional feedforward approach, an additional
feedforward correction term obtained via prerecorded data was integrated for
the return flight. Compared to the baseline controller, the feedforward
controller was observed to produce 43% less RMSE position error at a vehicle
ground velocity of 1 m/s with 6 m/s of environmental wind velocity. This
feedforward approach also produced 14% less RMSE position error for the complex
flows as well
Touch the Wind: Simultaneous Airflow, Drag and Interaction Sensing on a Multirotor
Disturbance estimation for Micro Aerial Vehicles (MAVs) is crucial for
robustness and safety. In this paper, we use novel, bio-inspired airflow
sensors to measure the airflow acting on a MAV, and we fuse this information in
an Unscented Kalman Filter (UKF) to simultaneously estimate the
three-dimensional wind vector, the drag force, and other interaction forces
(e.g. due to collisions, interaction with a human) acting on the robot. To this
end, we present and compare a fully model-based and a deep learning-based
strategy. The model-based approach considers the MAV and airflow sensor
dynamics and its interaction with the wind, while the deep learning-based
strategy uses a Long Short-Term Memory (LSTM) neural network to obtain an
estimate of the relative airflow, which is then fused in the proposed filter.
We validate our methods in hardware experiments, showing that we can accurately
estimate relative airflow of up to 4 m/s, and we can differentiate drag and
interaction force.Comment: The first two authors contributed equall
Using A Quadrotor As Wind Sensor: Time-Varying Parameter Estimation Algorithms
International audienceThe objective of this paper is to develop an algorithm for the estimation of time-varying wind parameters by taking into account a detailed quadrotor model. The design objectives include the time convergence optimization, robustness to measurement noises, and a guaranteed convergence of the estimates to the true values under mild applicability conditions. It is supposed that the estimation algorithm can use IMU (accelerometers, gyroscopes) sensors augmented with an earth reference tracking system and rotor rotational velocity sensors. To this end, three time-varying parameter estimation algorithms are introduced, compared and finally merged to estimate the varying wind velocity in on-board quadrotor systems. Final numerical experiments , using a nonlinear quadrotor simulator, are used to validate the proposed approaches
Development and evaluation of a dynamically scaled testbed aircraft for a visual inertial odometry dataset
In this thesis we describe the design, manufacturing, and testing of a dynamically scaled aircraft, which is a scaled model of a general aviation vehicle that dynamically behaves in a similar manner as the full-scale aircraft. This scaled model (Cirrus SR22T) is to serve as a testbed for both Distributed Electric Propulsion (DEP) aircraft research and for Visual Inertial Odometry (VIO) research. The aircraft is used as a baseline to compare with the DEP aircraft, to draw conclusion regarding the effect of changing to a DEP configuration, and to provide a way to measure the effect that a DEP configuration would have on a full-scale aircraft. The aircraft is also used to collect data from various onboard sensors to provide a data set for the VIO research community to use
Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs
Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances