Direct Acceleration Feedback Control of Quadrotor Aerial Vehicles

Hamandi, Mahmoud; Tognon, Marco; Franchi, Antonio

doi:10.1109/icra40945.2020.9196557

Cited by 10 publications

(10 citation statements)

References 21 publications

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“…This is a drawback as such measurements are not available with the majority of the multirotor UAVs hardware currently deployed. The recent work of [20] utilized acceleration error scheme instead of the common feedback linearization based approaches. This approach requires a clean, lag-free acceleration measurement which was obtained by designing a novel regression based filter that removes accelerometer noise caused by propeller rotations (the accelerometer measurement comes from the IMU).…”

Section: B Related Workmentioning

confidence: 99%

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

et al. 2022

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The demand for accurate and fast trajectory tracking for multirotor Unmanned Aerial Vehicles (UAVs) have grown recently due to advances in UAV avionics technology and application domains. In many applications, the multirotor UAV is required to accurately perform aggressive maneuvers in challenging scenarios like the presence of external wind disturbances or in-flight payload changes. In this paper, we propose a systematic controller tuning approach based on identification results obtained by a recently developed Deep Neural Networks with the Modified Relay Feedback Test (DNN-MRFT) algorithm. We formulate a linear equivalent representation suitable for DNN-MRFT using feedback linearization. This representation enables the analytical investigation of different controller structures and tuning settings, and captures the non-linearity trends of the system. With this approach, the trade-off between performance and robustness in design was made possible which is convenient for the design of controllers of UAVs operating in uncertain environments. We demonstrate that our approach is adaptive and robust through a set of experiments, where accurate trajectory tracking is maintained despite significant changes to the UAV aerodynamic characteristics and the application of wind disturbance. Due to the model-based system design, it was possible to obtain low discrepancy between simulation and experimental results which is beneficial for potential use of the proposed approach for real-time model-based planning and fault detection tasks. We obtained RMSE of 3.59 cm when tracking aggressive trajectories in the presence of strong wind, which is on par with state-of-the-art.INDEX TERMS Unmanned aerial vehicles, system identification, adaptive and robust control, trajectory tracking, PID control, machine learning.

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Section: B Related Workmentioning

confidence: 99%

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

et al. 2022

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“…The usage of such linear filters lags the acceleration measurement which can degrade control performance. Thus [14] proposed an adaptive nonlinear filtering technique based on a manually tuned empirical signal and noise models that does not introduce lag to the acceleration signal. Adaptability of such nonlinear manually tuned filtration techniques to different UAV setups can be limited due to the variety of noise sources.…”

Section: A Relevant Workmentioning

confidence: 99%

“…Due to the novelty of including the acceleration as a state, we can not compare with other dynamic based filters from literature. Authors in [14] did however propose a regression based notch filter for removing the noise form accelerometer measurements in UAVs. The performance of the filter was not analyzed quantitatively, but the authors claimed that the filter removed most of the noises, without introducing a lag or attenuation in the signal.…”

Section: B Figure-eight Maneuvermentioning

confidence: 99%

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Dynamic Based Estimator for UAVs with Real-time Identification Using DNN and the Modified Relay Feedback Test

Wahbah,

Chehadeh,

Zweiri

2021

Preprint

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Control performance of Unmanned Aerial Vehicles (UAVs) is directly affected by their ability to estimate their states accurately. With the increasing popularity of autonomous UAV solutions in real world applications, it is imperative to develop robust adaptive estimators that can ameliorate sensor noises in low-cost UAVs. Utilizing the knowledge of UAV dynamics in estimation can provide significant advantages, but remains challenging due to the complex and expensive pre-flight experiments required to obtain UAV dynamic parameters. In this paper, we propose two decoupled dynamic model based Extended Kalman Filters for UAVs, that provide high rate estimates for position, and velocity of rotational and translational states, as well as filtered inertial acceleration. The dynamic model parameters are estimated online using the Deep Neural Network and Modified Relay Feedback Test (DNN-MRFT) framework, without requiring any prior knowledge of the UAV physical parameters. The designed filters with realtime identified process model parameters are tested experimentally and showed two advantages. Firstly, smooth and lag-free estimates of the UAV rotational speed and inertial acceleration are obtained, and used to improve the closed loop system performance, reducing the controller action by over 6%. Secondly, the proposed approach enabled the UAV to track aggressive trajectories with low rate position measurements, a task usually infeasible under those conditions. The experimental data shows that we achieved estimation performance matching other methods that requires full knowledge of the UAV parameters.

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“…We assume that the robot is controlled by a position controller able to track any C 2 trajectory with negligible error in the domain of interest, independently from external disturbances. With the recent robust controllers (as the one in [6], [52] for both unidirectional-and multidirectional-thrust vehicles) and disturbance observers for aerial vehicles, one can obtain very precise tracking, even in the presence of external disturbances. Nevertheless, in practice, precise tracking is not needed for stability, but only to achieve perfect performance.…”

Section: Modelingmentioning

confidence: 99%

Physical Human-Robot Interaction with a Tethered Aerial Vehicle: Application to a Force-based Human Guiding Problem

Tognon¹,

Alami²,

Siciliano³

2020

Preprint

Self Cite

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Today, physical Human-Robot Interaction (pHRI) is a very popular topic in the field of ground manipulation. At the same time, Aerial Physical Interaction (APhI) is also developing very fast. Nevertheless, pHRI with aerial vehicles has not been addressed so far. In this work, we present the study of one of the first systems in which a human is physically connected to an aerial vehicle by a cable. We want the robot to be able to pull the human toward a desired position (or along a path) only using forces as an indirect communication-channel. We propose an admittancebased approach that makes pHRI safe. A controller, inspired by the literature on flexible manipulators, computes the desired interaction forces that properly guide the human. The stability of the system is formally proved with a Lyapunov-based argument. The system is also shown to be passive, and thus robust to non-idealities like additional human forces, time-varying inputs, and other external disturbances. We also design a maneuver regulation policy to simplify the path following problem. The global method has been experimentally validated on a group of four subjects, showing a reliable and safe pHRI.

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Direct Acceleration Feedback Control of Quadrotor Aerial Vehicles

Cited by 10 publications

References 21 publications

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

Systematic Online Tuning of Multirotor UAVs for Accurate Trajectory Tracking Under Wind Disturbances and In-Flight Dynamics Changes

Dynamic Based Estimator for UAVs with Real-time Identification Using DNN and the Modified Relay Feedback Test

Physical Human-Robot Interaction with a Tethered Aerial Vehicle: Application to a Force-based Human Guiding Problem

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