Nonlinear MPC with Motor Failure Identification and Recovery for Safe and Aggressive Multicopter Flight

Tzoumanikas, Dimos; Yan, Qingyue; Leutenegger, Stefan

doi:10.1109/icra40945.2020.9196690

Cited by 13 publications

(17 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…. f 6 with A ∈ R 4×6 the allocation matrix related to the MAV geometry as described in [26]. B F e and B M e are given by:…”

Section: A Hybrid Modellingmentioning

confidence: 99%

Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing

Tzoumanikas¹,

Graule²,

Yan³

et al. 2020

Robotics: Science and Systems XVI

Self Cite

View full text Add to dashboard Cite

Aerial manipulation aims at combining the maneuverability of aerial vehicles with the manipulation capabilities of robotic arms. This, however, comes at the cost of the additional control complexity due to the coupling of the dynamics of the two systems. In this paper we present a Nonlinear Model Predictive Control (NMPC) specifically designed for Micro Aerial Vehicles (MAVs) equipped with a robotic arm. We formulate a hybrid control model for the combined MAV-arm system which incorporates interaction forces acting on the end effector. We explain the practical implementation of our algorithm and show extensive experimental results of our custom built system performing multiple 'aerial-writing' tasks on a whiteboard, revealing accuracy in the order of millimetres.

show abstract

“…. f 6 with A ∈ R 4×6 the allocation matrix related to the MAV geometry as described in [26]. B F e and B M e are given by:…”

Section: A Hybrid Modellingmentioning

confidence: 99%

Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing

Tzoumanikas¹,

Graule²,

Yan³

et al. 2020

Robotics: Science and Systems XVI

Self Cite

View full text Add to dashboard Cite

show abstract

“…Thrust force coefficients k f i and drag moment coefficients k m i of each rotor i are also part of x G i . These last-mentioned inclusions to the state vector reduce the amount of a priori assumptions needed during the estimation, compared to Wuest et al [21] and Tzoumanikas et al [18].…”

Section: A State Vectormentioning

confidence: 99%

Filter-Based Online System-Parameter Estimation for Multicopter UAVs

Böhm

Scheiber

Weiß

2021

Robotics: Science and Systems XVII

View full text Add to dashboard Cite

Accurate system modeling and identification gain importance as tasks executed by autonomously acting unmanned aerial vehicles (UAVs) get more complex and demanding.This paper presents a Bayesian filter approach to online and continuously identify the system parameters, sensor suite calibration states, and vehicle navigation states in a holistic framework. Previous work only tackles subsets of the overall state vector during dedicated phases (e.g., motionless, online during flight, post-processing). These works often introduce the artificial so-called body frame forcing assumptions on system states, such as the inertia matrix's principal axes orientation. Our approach estimates the entire state vector in the (usually not precisely known) center of mass, eliminating several assumptions caused by the artificially introduced body frame in other work. Since our approach also estimates geometric states such as the rotor and sensor placements, no hand-made measures to the unknown center of mass are required -the system is fully self-calibrating. A detailed discussion on the system's observability reveals additionally required (different) measurements for a theoretical and a real N -arm multicopter. We show that easy and precise hand-measurable quantities in real applications can provide the required information. Statistically relevant simulations in Gazebo/RotorS providing ground truth for all states yet having realistic physics validate all our findings.

show abstract

“…When such knowledge is not available, fault identification or adaptive control techniques need to be leveraged. In [7], an Extended Kalman Filter (EKF)-based fault identification is used to decide if there is one or multiple rotor failures, and a control allocation is updated based on the failure using a nonlinear Model Predictive Control (MPC). In [8], a selfreconfiguration technique which allows the system to decide on its configuration based on the actuator failure and its desired trajectory is introduced.…”

Section: Related Workmentioning

confidence: 99%

A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions

Yel¹,

Bezzo²

2021

Preprint

View full text Add to dashboard Cite

Due to changes in model dynamics or unexpected disturbances, an autonomous robotic system may experience unforeseen challenges during real-world operations which may affect its safety and intended behavior: in particular actuator and system failures and external disturbances are among the most common causes of degraded mode of operation. To deal with this problem, in this work, we present a meta-learningbased approach to improve the trajectory tracking performance of an unmanned aerial vehicle (UAV) under actuator faults and disturbances which have not been previously experienced. Our approach leverages meta-learning to adapt the system's model at runtime to make accurate predictions about the system's future state. A runtime monitoring and validation technique is proposed to decide when the system needs to adapt its model by considering a data pruning procedure for efficient learning. Finally the desired trajectory is adapted based on future predictions by borrowing robust control logic to make the system track the original and desired path without needing to access the system's controller. The proposed framework is applied and validated in both simulations and experiments on a faulty UAV navigation case study demonstrating a drastic increase in tracking performance.

show abstract

Nonlinear MPC with Motor Failure Identification and Recovery for Safe and Aggressive Multicopter Flight

Cited by 13 publications

References 23 publications

Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing

Aerial Manipulation Using Hybrid Force and Position NMPC Applied to Aerial Writing

Filter-Based Online System-Parameter Estimation for Multicopter UAVs

A Meta-Learning-based Trajectory Tracking Framework for UAVs under Degraded Conditions

Contact Info

Product

Resources

About