Fault Tolerance Analysis of L1 Adaptive Control System for Unmanned Aerial Vehicles

Krishnamoorthy, K.

doi:10.33915/etd.6015

Cited by 4 publications

(2 citation statements)

References 32 publications

(41 reference statements)

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“…There are linear time-invariant models, parameter-varying models [6], and even non-linear models to this approach. Even in the past few years, Kalman Filter [1], adaptive control [7], sliding mode control [6,8], and PMI filter-based solutions [9] are being studied with little to no implementation on real flights. In 2016, Caliskan et.…”

Section: A Simulations and Real-flight Tests For Fdimentioning

confidence: 99%

“…To simulate a failure, additional 4 rotors are added to the QDrone and tested under controlled environments. While most of the works involve simulations using Matlab and Simulink [8], another good demonstration from Narasimhan, Abishek, et al [3] employs this on Intels Shooting Star UAV platform and performs simulations in indoor and outdoor environments using only the onboard sensors. The control allocation is performed using pseudoinverse-based model inversion and the model is identified using least squares and updated using recursive least squares as the fault occurs.…”

Section: A Simulations and Real-flight Tests For Fdimentioning

confidence: 99%

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Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Ashe¹,

Goli²,

Kandath³

et al. 2023

Preprint

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<p>Reconfigurable aerial platforms such as multicopter unmanned aerial vehicles (UAVs) allow the design of fail-safe systems because of inherent redundancy in actuators and sensors to maintain stability with a reduction in flight performance. The methods based on univariate and multivariate time series analysis of just the attitude signals can pave the way for model-free systems that can be generalized across a class of UAVs like multicopters. In this paper, we present a critical analysis of real-flight attitude time-series signals and investigate them for data-driven motor fault and failure detection and isolation (FDI), specifically for multicopters configurations like quadcopters and hexacopters. We analyze flight data for different scenarios of outdoor flights, healthy and faulty, hovering and cruising, loss of efficiency, and single-rotor failure of every motor. We tested it for small to medium-sized multicopters. The failure detection and classification are performed without relying on analytical system modeling or the knowledge of the controller.</p> <p>Thus, we perform three major assessments: vector auto-regression (VAR) using residual variance, time-frequency analysis, and dimensionality analysis of the recorded variables to support the classification framework. To the author's best knowledge, this is the first attempt at providing an algorithmic foundation that works on existing hardware and flight controllers using streaming flight attitude data rather than simulations. This allows us to implement these online methods in real-time on multicopters, which drastically increases levels of safety and scalability of unmanned flights in drone applications.</p>

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Section: A Simulations and Real-flight Tests For Fdimentioning

confidence: 99%

Section: A Simulations and Real-flight Tests For Fdimentioning

confidence: 99%

Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Ashe¹,

Goli²,

Kandath³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Ashe

Goli

Kandath

et al. 2023

2023 International Conference on Unmanned Aircraft Systems (ICUAS)

View full text Add to dashboard Cite

Reconfigurable aerial platforms such as multicopter unmanned aerial vehicles (UAVs) allow the design of fail-safe systems because of inherent redundancy in actuators and sensors to maintain stability with a reduction in flight performance. The methods based on univariate and multivariate time series analysis of just the attitude signals can pave the way for modelfree systems that can be generalized across a class of UAVs like multicopters. In this paper, we present a critical analysis of real-flight attitude time-series signals and investigate them for data-driven motor fault and failure detection and isolation (FDI), specifically for multicopters configurations like quadcopters and hexacopters. We analyze flight data for different scenarios of outdoor flights, healthy and faulty, hovering and cruising, loss of efficiency, and single-rotor failure of every motor. We tested it for small to medium-sized multicopters. The failure detection and classification are performed without relying on analytical system modeling or the knowledge of the controller.Thus, we perform three major assessments: vector autoregression (VAR) using residual variance, time-frequency analysis, and dimensionality analysis of the recorded variables to support the classification framework. To the author's best knowledge, this is the first attempt at providing an algorithmic foundation that works on existing hardware and flight controllers using streaming flight attitude data rather than simulations. This allows us to implement these online methods in real-time on multicopters, which drastically increases levels of safety and scalability of unmanned flights in drone applications.

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Conceptual Development of a Simulation Environment for Missiles Under Damaged States

Floyd

Perhinschi

Gross

et al. 2023

AIAA SCITECH 2023 Forum

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Fault Tolerance Analysis of L1 Adaptive Control System for Unmanned Aerial Vehicles

Cited by 4 publications

References 32 publications

Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Multivariate Data Analysis for Motor Failure Detection and Isolation in A Multicopter UAV Using Real-Flight Attitude Signals

Conceptual Development of a Simulation Environment for Missiles Under Damaged States

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