Aerodynamic Parameter Identification and Uncertainty Quantification for Small Unmanned Aircraft

Hale, Lawrence E.; Patil, Mayuresh; Roy, Christopher J.

doi:10.2514/6.2015-1538

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…Such approaches include prediction error methods (PEM) [6], [7], [8], maximum likelihood (ML) methods [9], [10], least square (LS) methods [11], frequency response identification methods [12], [13], [14], and neural network-based methods [15], [16], [17]. Several studies in the literature applied these techniques to UAV operation with accurate identification results [18], [19], [20], [21], [22], [23]. Nonetheless, these methods require extensive data generation and accurate selection of optimizer initial conditions, which demand human experience and cause sus-ceptibility to data biases and overfitting.…”

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confidence: 99%

“…Nonetheless, these methods require extensive data generation and accurate selection of optimizer initial conditions, which demand human experience and cause sus-ceptibility to data biases and overfitting. Furthermore, most of these methods are computationally expensive and not suitable for real-time applications; they are instead applied offline to process an abundance of previously collected flight or operational data [18], [19], [20], [21], [22].…”

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confidence: 99%

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Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

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Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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“…Notice that, the observation function Ψ i (̂i,̂i) is defined as (6), in this way it follows then that .…”

Section: Smomentioning

confidence: 99%

“…Indeed, this parametric variation directly affects the control laws, which could negatively impact the vehicle performance [4]. To deal with this complex scenario, researches have proposed different approaches, for example, (i) algorithms to estimate the variation of the vehicle mass [5], or (ii) algorithms for the identification of aerodynamic parameters [6], which are further used in the control loop. The Gradient Algorithm (GA) has been a frequently used parameter estimator due to its effectiveness and easiness of implementation.…”

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Parameter estimation and control of an unmanned aircraft‐based transportation system for variable‐mass payloads

Alatorre

Espinoza

Sanchez

et al. 2021

Asian Journal of Control

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This paper deals with the development of an adaptive robust controller for an Unmanned Aircraft System with variable mass. The goal is to improve trajectory tracking and energy performance while the aircraft mass is gradually reducing due to payload release. To improve the trajectory tracking performance, we propose an algorithm that merges the concepts of least squares method and sliding mode observer for the estimation of the vehicle mass. The nonlinear observer estimates the linear velocities and accelerations, which are further used to obtain the vehicle mass. A robust adaptive pole placement controller based on the Attractive Ellipsoid Method uses this estimation to update the controllers gains due to the mass variation. To validate the effectiveness of the proposed algorithm for performing aerial transportation and deployment of payloads, we present a numerical example comparing the performance of the proposed method with respect to using Proportional Derivative controllers as well as Robust Controllers. KEYWORDSload transportation, model in the loop, nonlinear observer, robust adaptive control, UAS, variable mass 1 INTRODUCTION Diverse research works have explored original solutions for the Unmanned Aircraft System (UAS)-based payload transportation problem for constant and variable mass payloads. As a few examples, the authors in Kui et al. [1]developed an autonomous vehicle to transport a payload by using a tensor embedded in the vehicle, while the authors in Geng and Langelaan [2] designed strategies for load transportation that involves a team of cooperative vehicles. Previously, the authors in Haus et al. [3] presented the MORUS project where they conducted the

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“…When evaluating the turbulence effects on fixed-wing airplanes, the Dryden spectral model, which assumes a "frozen-field", is normally used (Hale et al 2017). However, the Dryden model is not suitable for a lowspeed rotorcraft as the mean wind speed becomes dominant (Dahl and Faulkner 1978).…”

Section: Computational Fluid Dynamics (Cfd) Simulationsmentioning

confidence: 99%

System identification of wind effects on multirotor aircraft

Wei

Lin

Kong

2021

Int J Intell Robot Appl

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Multirotor airplanes are widely used in many outdoor applications, e.g., agriculture, transportation, and public safety, where winds might be strong and prevalent. However, the effects of wind on multirotor aircraft are still not fully understood yet. The objective of this paper is to investigate and model wind effects on a real hovering octocopter. The wind is directly measured and considered as one of the inputs to the bare-airframe model. Then a series of models, each corresponding to a different wind condition, are identified from real flight data through a system identification approach. The time-domain validation results show that an average of 15% error reduction can be achieved by considering wind effects, captured by a wind correction term. The identified models will play an important role for the future development of model-based controllers for outdoor multirotor aircraft.

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Aerodynamic Parameter Identification and Uncertainty Quantification for Small Unmanned Aircraft

Cited by 7 publications

References 8 publications

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Parameter estimation and control of an unmanned aircraft‐based transportation system for variable‐mass payloads

System identification of wind effects on multirotor aircraft

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