Ewoud Smeur's Posts (2)

Sort by

Wind Gusts
Drones have the potential to perform many useful tasks, such as search and rescue, package delivery and aerial imaging. But in order to perform these tasks in an outdoor environment, the vehicles need to be able to control their position under the influence of wind gusts. This is especially true when flying close to obstacles, as a position error due to a wind gust might result in a collision.

Incremental control
At the MAVLab of Delft University of Technology, we have taken the concept of Incremental Nonlinear Dynamic Inversion (INDI), and applied it to the linear accelerations of MAVs. The idea is that both disturbances as well as control forces are measured by the accelerometer. This means that a desired acceleration can be achieved by incrementing the previous control input based on the difference between desired and measured acceleration.

INDI can be compared to the integrator part of a PID controller, but where an integrator is blindly adding input, INDI takes the actuator effectiveness, actuator dynamics and filtering into account. This way, it can exactly determine the size of the input increment that should be applied, and it knows when the output should be expected. This allows the controller to react very fast to even the strongest of disturbances, such as a 10 m/s wind gust in the video above. (Note that the top speed of the Bebop is 13 m/s, according to Parrot!)

Wind tunnel experiment
In the experiment shown in the video, the drone is flying in and out of the wind tunnel flow, which is blowing at 10 m/s. We show a comparison of position control with INDI and PID, both with the same inner loop control. The position of the vehicle is sent to the drone at 4 Hz using an Optitrack infrared tracking system. When flying in and out of the wind tunnel, PID control leads to large overshoot, where it takes a long time for the integrator to compensate the change in wind. For INDI control, observe that the maximum deviation is much smaller, and that the vehicle returns to the correct position in a much shorter time.

The figure below shows the top view of the experiment, where the wind tunnel is blowing in the negative XN direction, and is located at −1.425 < YN < 1.425.


The use of INDI does not require any fancy sensors, except for the accelerometer that is standard on most drones. If the position loop is bypassed, it is even possible for a pilot to command accelerations; letting go of the stick will make the drone resist any acceleration, such that it keeps its velocity.

The INDI code is included in the Paparazzi open source autopilot, but should be easy to incorporate in other autopilots as well.

More information can be found in the paper, which can be downloaded from Elsevier (free for the first 50 days) or Researchgate.

Read more…

Cyclone Hybrid Vehicle (Tailsitter)

Together, the Dronelab at ENAC in Toulouse and the MAVLab at TU Delft have developed the Cyclone. It is a hybrid drone of the tailsitter type, with just 4 actuators. In the video, we show autonomous flight, ranging from hover to forward flight. Below, we will highlight some of the interesting aspects, but more details can be found in the paper.

The vehicle is designed with efficiency and simplicity in mind. More than anything else, the Cyclone is a fixed wing aircraft capable of hovering. It is equipped with two propellers and two flaps. To spare the beautiful plane in the image below, we made a cheap version to test with. The Cyclone that is starring in the video has a 3D printed fuselage and a foam wing laminated with Kevlar. With the battery we are currently flying with it weighs 1.1 kg, flies most efficient at 16 m/s, and tops out at about 30 m/s (level flight).

To control the Cyclone, we use a method called Incremental Nonlinear Dynamic Inversion (INDI). The benefit of this method is that it is able to compensate for unmodeled effects or disturbances, by considering the angular acceleration. After all, according to Newton, all moments acting on the drone together result in an angular acceleration. This way, it is possible to compensate these moments and disturbances without modeling them.


The way we see it, a hybrid, like any rotorcraft needs to be able to keep its position regardless of wind. This means that the drone needs to be able to fly at any constant airspeed (0-30 m/s) without ascending. Therefore, were for a transition to forward flight or back, climbing a bit will make things much easier, we are not doing this. However, that means that we need to deal with high angle of attack flight, which generates a large pitch-down moment. A moment so large, that saturation of the flaps is commonplace (watch the flaps in the video during the back-transition).


We used the newly designed 'Chimera' autopilot, which is aimed at ease of use and runs the Paparazzi open source autopilot software. Among the many features are an STMF7 processor, an SD card slot, a mount for an XBEE and a differential pressure sensor.


Read more…