Disturbance Estimation and Rejection for High-Precision Multirotor Position Control

Abstract

Many multirotor Unmanned Aerial Systems applications have a critical need for precise position control in environments with strong dynamic external disturbances such as wind gusts or ground and wall effects. Moreover, to maximize flight time, small multirotor platforms have to operate within strict constraints on payload and thus computational performance. In this paper, we present the design and experimental comparison of Model Predictive and PID multirotor position controllers augmented with a disturbance estimator to reject strong wind gusts up to 12 m/s and ground effect. For disturbance estimation, we compare Extended and Unscented Kalman filtering. In extensive in-and outdoor flight tests, we evaluate the suitability of the developed control and estimation algorithms to run on a computationally constrained platform. This allows to draw a conclusion on whether potential performance improvements justify the increased computational complexity of MPC for multirotor position control and UKF for disturbance estimation.

Internal Wind Turbine Blade Inspections Using UAVs: Analysis and Design Issues

“…While the control method used in [107] has reduced the altitude error produced by the ground effect, the height threshold for switching controllers was chosen arbitrarily based on known Z R range. In [108], the ground effect is treated as an always present unknown disturbance estimated by a Kalman filter and compensated for by a Model Predictive Control (MPC). The advantage of having a robust control strategy such as [108] over adaptive control approach used in [107] is that the Z R ratio does not need to be known beforehand, as external disturbances such as aerodynamic disturbances are treated as a noise affecting the system at all times.…”

“…In order to ensure that the UAV does not collide with the ceiling, it needs to be compensated for before the UAV can operate inside a WTB. Due to similarities between ground and ceiling effects, controllers proposed in [107,108] could be adapted for the ceiling effect.…”

“…Authors in [109] have proposed a method for detecting when the UAV experiences the wall effect, allowing the UAV to keep its distance. Controllers used by [107,108] may also be adapted for the relevant angles.…”

“…Although there are several works exploring the aerodynamic disturbances affecting an UAV operating in a constrained space, there are two additional challenges present in the WTB. Firstly, the surfaces in the WTB are curved and complex, while works in [102,103,[105][106][107][108][109] have only considered flat surfaces. To the best of our knowledge, there are insufficient academic works dealing with the aerodynamic disturbances for quadcopters operating near uneven surfaces.…”

“…The map generated by SLAM could be used as prediction horizon and constraints, providing optimal solution for an UAV inside the WTB based on the information that the cameras and sensors acquire. The MPC can update the path when new information is available [108,[136][137][138], while also taking the aerodynamic disturbances or the UAV's dynamics into consideration. Finally, with large enough map of the environment, MPC could generate the quickest or least power consuming collision-free path for the UAV to take while performing the interior WTB inspection.…”