Model-aided state estimation for quadrotor micro air vehicles amidst wind disturbances

© 2014 IEEE. This paper extends the recently developed Model-Aided Visual-Inertial Fusion (MA-VIF) technique for quadrotor Micro Air Vehicles (MAV) to deal with wind disturbances. The wind effects are explicitly modelled in the quadrotor dynamic equations excluding the unobservable wind velocity component. This is achieved by a nonlinear observability of the dynamic system with wind effects. We show that using the developed model, the vehicle pose and two components of the wind velocity vector can be simultaneously estimated with a monocular camera and an inertial measurement unit. We also show that the MA-VIF is reasonably tolerant to wind disturbances, even without explicit modelling of wind effects and explain the reasons for this behaviour. Experimental results using a Vicon motion capture system are presented to demonstrate the effectiveness of the proposed method and validate our claims

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

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

Modellbasierte Quadrokopter-Navigation mit Lasterstützung

Quadrokopter werden häufig in Gebäuden oder in Gebäudenähe eingesetzt, wo die Verfügbarkeit von GPS nicht gewährleistet ist. Um dennoch zuverlässig Position und Lage des Fluggerätes bestimmen zu können, werden in dieser Arbeit Methoden zur modellgestützten Navigation entwickelt und erfolgreich auf Quadrokopter angewendet. Die Nutzung des Bewegungsmodells reduziert das Fehlerwachstum wesentlich. Weitere Verbesserungen werden durch geeignete Integration von Lasermessungen erzielt