Fault-Tolerant Model Predictive Control for Multirotor UAVs

Diget, Emil Lykke; Hasan, Agus; Manoonpong, Poramate

doi:10.23919/acc53348.2022.9867240

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…This approach utilizes sensor data measurements to construct models without a prior knowledge of the system’s governing equations. Machine learning algorithms, such as neural networks or support vector machines, learn patterns and relationships from the data to generate predictive models [15] , [16] , [17] , [18] , [19] , [20] , [21] , [22] . While this method is versatile and applicable to a wide range of systems, it often produces black-box models, making it challenging to interpret the underlying dynamics.…”

Section: Methods Detailsmentioning

confidence: 99%

WyNDA: A method to discover mathematical models of dynamical systems from data

Hasan

2024

MethodsX

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Section: Methods Detailsmentioning

confidence: 99%

WyNDA: A method to discover mathematical models of dynamical systems from data

Hasan

2024

MethodsX

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“…The topic of AD in UAVs has been covered by quite a few works [34] [35] [36]. One of the first categories of approaches to this diagnostic problem utilizes model-based fault diagnosis with sophisticated [37] methods to evaluate model residuals and conclude on the fault's occurrence. In [25], the method is based on a nonlinear observer [38], which is an extension of linear observer [39] design techniques using transformations related to linear observability matrices.…”

Section: Related Workmentioning

confidence: 99%

A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

et al. 2023

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Unmanned Aerial Vehicles and the increasing variety of their applications are raising in popularity. The growing number of UAVs, emphasizes the significance of drones' reliability and robustness. Thus, there is a need for an efficient self-observing sensing mechanism to detect real-time anomalies in drone behavior. Previous works suggested prediction models from control theory, yet, they are complex by nature and hard to implement, while Deep Learning solutions are of great utility. In this paper, we propose a real-time framework to detect anomalies in drones by analyzing the sound emitted from them. For this purpose, we construct a hybrid Deep Learning based Transformer and a Convolutional Neural Network inspired by the well-known VGG architecture. Our approach is examined over a dataset that is collected from a single microphone set located on a micro drone in real-time. Our approach achieves an F1score of 88.4% in detecting anomalies and outperforms the VGG-16 architecture. Moreover, the framework presented in this paper reduces the number of parameters of the well-known VGG-16 from 138M, into a shrunk version with 3.6M parameters only. Additionally, our real-time approach, results in a smaller number of parameters in the neural network, and yet yields high accuracy in anomaly detection in drones with an average inference time of 0.2 seconds per second. Moreover, with an earphone that weighs less than 100 grams on top of the UAV, our method is shown to be beneficial, even in extreme conditions such as a microsize dataset that is composed of three hours of flight recordings. The presented self-observing method can be implemented by simply adding a microphone to drones and transmitting the captured audio for analysis to the remote control or performing it onboard the drone using a dedicated microcontroller.

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“…In contrast, MPC can handle a variety of uncertainties while considering system constraints. However, it is computationally complex, especially for nonlinear systems, and may not perform well in real time under all conditions (Diget et al, 2022; Schwenzer et al, 2021). The FL controller can address point‐stabilization problems, but it cannot ensure robustness during operation (Akhtar et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Leveraging digital twin for autonomous docking of a container truck with stabilization control

Widyotriatmo,

Kuncara,

Amri

et al. 2024

Journal of Field Robotics

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This paper describes the design, development, and implementation of a high‐precision autonomous docking control system for a container truck based on a digital twin approach. The digital twin is used to simulate the dynamic behavior of the physical truck and to design the controller by providing a virtual platform to test, validate, and optimize control strategies and algorithms before their deployment in the actual system. To this end, a cascade of a nonlinear observer and an unscented Kalman filter is used to estimate the state variables of the physical truck for point‐stabilization and orientation controls during the autonomous docking process. The docking motion involves two stabilization problems: point stabilization for smooth motion from the initial configuration to the docking slot, and orientation control to deliver the container truck to the final docking position with a margin of error of 5 cm for position and 0.0087 rad for orientation. The stability of both controllers is investigated, and simulations and experiments are conducted to demonstrate the accuracy of the proposed method in a container terminal environment.

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Fault-Tolerant Model Predictive Control for Multirotor UAVs

Cited by 12 publications

References 14 publications

WyNDA: A method to discover mathematical models of dynamical systems from data

WyNDA: A method to discover mathematical models of dynamical systems from data

A Stethoscope for Drones: Transformers-Based Methods for UAVs Acoustic Anomaly Detection

Leveraging digital twin for autonomous docking of a container truck with stabilization control

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