4 research outputs found
UAV Control in Close Proximities - Ceiling Effect on Battery Lifetime
With the recent developments in the unmanned aerial vehicles (UAV), it is
expected them to interact and collaborate with their surrounding objects, other
robots and people in order to wisely plan and execute particular tasks.
Although these interaction operations are inherently challenging as compared to
free-flight missions, they might bring diverse advantages. One of them is their
basic aerodynamic interaction during the flight in close proximities which can
result in a reduction of the controller effort. In this study, by collecting
real-time data, we have observed that the current drawn by the battery can be
decreased while flying very close to the surroundings with the help of the
ceiling effect. For the first time, this phenomenon is analyzed in terms of
battery lifetime degradation by using a simple full equivalent cycle counting
method. Results show that cycling related effect on battery degradation can be
reduced by a 15.77% if the UAV can utilize ceiling effect.Comment: ICoIAS 201
Nonlinear Model Predictive Control for the Stabilization of a Wheeled Unmanned Aerial Vehicle on a Pipe
This letter addresses the task of stabilizing a wheeled unmanned aerial vehicle on a pipe, which is an emerging applica- tion in oil and gas facilities for nondestructive measurements. After the derivation of the dynamic model of the system, a discrete-time nonlinear model predictive controller is designed over a finite horizon. The analysis of the asymptotic stability of the designed controller is carried out. Numerical tests show the performance and the robustness of the proposed solution
Model Predictive Control for Micro Aerial Vehicles: A Survey
This paper presents a review of the design and application of model
predictive control strategies for Micro Aerial Vehicles and specifically
multirotor configurations such as quadrotors. The diverse set of works in the
domain is organized based on the control law being optimized over linear or
nonlinear dynamics, the integration of state and input constraints, possible
fault-tolerant design, if reinforcement learning methods have been utilized and
if the controller refers to free-flight or other tasks such as physical
interaction or load transportation. A selected set of comparison results are
also presented and serve to provide insight for the selection between linear
and nonlinear schemes, the tuning of the prediction horizon, the importance of
disturbance observer-based offset-free tracking and the intrinsic robustness of
such methods to parameter uncertainty. Furthermore, an overview of recent
research trends on the combined application of modern deep reinforcement
learning techniques and model predictive control for multirotor vehicles is
presented. Finally, this review concludes with explicit discussion regarding
selected open-source software packages that deliver off-the-shelf model
predictive control functionality applicable to a wide variety of Micro Aerial
Vehicle configurations