Gust disturbance alleviation with Incremental Nonlinear Dynamic Inversion

Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near obstacles by their ability to withstand wind gusts. Currently widespread position control methods such as Proportional Integral Derivative control do not perform well under the influence of gusts. Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control technique that can control nonlinear systems subject to disturbances. This method was developed for the attitude control of MAVs, but in this paper we generalize this method to the outer loop control of MAVs under gust loads. Significant improvements over a traditional Proportional Integral Derivative (PID) controller are demonstrated in an experiment where the drone flies in and out of a fan's wake. The control method does not rely on frequent position updates, so it is ready to be applied outside with standard GPS modules

Taming mismatches in inter-agent distances for the formation-motion control of second-order agents

This paper presents the analysis on the influence of distance mismatches on the standard gradient-based rigid formation control for second-order agents. It is shown that, similar to the first-order case as recently discussed in the literature, these mismatches introduce two undesired group behaviors: a distorted final shape and a steady-state motion of the group formation. We show that such undesired behaviors can be eliminated by combining the standard formation control law with distributed estimators. Finally, we show how the mismatches can be effectively employed as design parameters in order to control a combined translational and rotational motion of the formation.Comment: 14 pages, conditionally accepted in Automatic Control, IEEE Transactions o

A Sampled-Data Form of Incremental Nonlinear Dynamic Inversion for Spacecraft Attitude Control

This paper presents a sampled–data form of the recently reformulated incremental nonlinear dynamic inversion (INDI) applied for robust spacecraft attitude control. INDI is a combined model– and sensor–based approach mostly applied for attitude control that only requires an accurate control effectiveness model and measurements of the state and some of its derivatives. This results in a reduced dependency on exact knowledge of system dynamics which is known as a major disadvantage of model–based nonlinear dynamic inversion controllers. However, most of the INDI derivations proposed in the literature assume a very high sampling rate of the system and its controller while also not explicitly considering the available sampling time of the digital control computer. Neglecting the sampling time and its effect in the controller derivations can lead to stability and performance issues of the resulting closed–loop nonlinear system. Therefore, our objective is to bridge this gap between continuous–time, highly sampled INDI formulations and their discrete, lowly sampled counterparts in the context of spacecraft attitude control where low sampling rates are common. Our sampled–data reformulation allows explicit consideration of the sampling time via an approximate sampled–data model in normal form widely known in the literature. The resulting sampled–data INDI control is still robust up to a certain sampling time since it remains only sensitive to parametric uncertainties. Simulation experiments for this particular problem demonstrate the bridge considered between INDI formulations which allows for low sampling control rates

Gust Disturbance Alleviation with Incremental Nonlinear Dynamic Inversion

Micro Aerial Vehicles (MAVs) are limited in their operation outdoors near obstacles by their ability to withstand wind gusts. Currently widespread position control methods such as Proportional Integral Derivative control do not perform well under the influence of gusts. Incremental Nonlinear Dynamic Inversion (INDI) is a sensor-based control technique that can control nonlinear systems subject to disturbances. This method was developed for the attitude control of MAVs, but in this paper we generalize this method to the outer loop control of MAVs under gust loads. Significant improvements over a traditional Proportional Integral Derivative (PID) controller are demonstrated in an experiment where the drone flies in and out of a fan's wake. The control method does not rely on frequent position updates, so it is ready to be applied outside with standard GPS modules.Control & Simulatio