Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

Saderla, Subrahmanyam; Rajaram, Dhayalan; Ghosh, Arnab

doi:10.1017/aer.2018.36

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Due to the scale and size of small UAVs, sensors onboard are prone to the interference of various subsystems, like propulsion units, which eventually leads to measurement noise. Output error method based on maximum likelihood estimator is proved suitable, even in the presence of measurement noise, for estimating linear and nonlinear aerodynamic parameters of UAVs from flight tests data pertaining to various flight regimes [17,18]. However, the OEM only estimates system parameters deterministically if system dynamics are appropriately modeled.…”

Section: Introductionmentioning

confidence: 99%

Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator

2023

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Aerodynamic characterisation from flight testing is an integral subroutine for evaluating a new flight vehicle’s aerodynamic performance, stability and controllability. The estimation of aerodynamic parameters from flight test data has extensively been explored, in the past, using estimation methods such as the equation error method, output error method and filter error method. However, in the current era, non-gradient-based estimation techniques are gaining attention from researchers due to their inherent data-driven optimisation capability to find the global best solution. In this paper, a novel non-gradient-based estimation method is proposed for the aerodynamic characterisation of unmanned aerial vehicles from flight data, which relies on the maximum likelihood method augmented with particle swarm optimisation. Flight data sets of a wing-alone unmanned aerial vehicle are used to demonstrate the capabilities of the proposed method in estimating aerodynamic derivatives. Estimates from the proposed method are corroborated with the wind tunnel test and output error method results. It has been observed that simulated flight vehicle responses using estimated parameters are in good agreement with measured data in most of the manoeuvers considered. Confidence in the estimates of linear and nonlinear aerodynamic parameters is well established with the lower limit of Cramer-Rao bounds, which are minimal. The proposed method also demonstrates good predictability of the quasi-steady stall aerodynamic model by estimating stall characteristic parameters such as aerofoil static stall characteristics parameter, hysteresis time constant and breakpoint. The overall performance of the proposed estimation method is on par with the output error method and is validated with the proof-of-match exercise.

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Section: Introductionmentioning

confidence: 99%

Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator

2023

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“…Two major techniques that have been widely used and well developed are the time-domain identification and the frequency-domain identification (10) . The Maximum Likelihood (ML) method is by far the most commonly used time-domain technique for estimating parameters from dynamic flight data (14,(16)(17)(18) . Previously, Saderla et al (12,17) efficiently applied the ML together with Gauss Newton (GN) method to estimate longitudinal and lateral-directional parameter for Unmanned Aerial Vehicle (UAV).…”

Section: Introductionmentioning

confidence: 99%

Enhancement of light aircraft 6 DOF simulation using flight test data in longitudinal motion

et al. 2021

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This paper proposes a procedure to improve the accuracy of the light aircraft 6 DOF simulation model by implementing model tuning and aerodynamic database correction using flight test data. In this study, the full-scale flight testing of a 2-seater aircraft has been performed in specific longitudinal manoeuver for model enhancement and simulation validation purposes. The baseline simulation model database is constructed using multi-fidelity analysis methods such as wind tunnel (W/T) test, computational fluid dynamic (CFD) and empirical calculation. The enhancement process starts with identifying longitudinal equations of motion for sensitivity analysis, where the effect of crucial parameters is analysed and then adjusted using the model tuning technique. Next, the classical Maximum Likelihood (ML) estimation method is applied to calculate aerodynamic derivatives from flight test data, these parameters are utilised to correct the initial aerodynamic table. A simulation validation process is introduced to evaluate the accuracy of the enhanced 6 DOF simulation model. The presented results demonstrate that the applied enhancement procedure has improved the simulation accuracy in longitudinal motion. The discrepancy between the simulation and flight test response showed significant improvement, which satisfies the regulation tolerance.

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Identification of Aerodynamic Derivatives of a Re-entry Module

Mark

Srinivasan

2020

Lecture Notes in Mechanical Engineering

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Lateral directional parameter estimation of a miniature unmanned aerial vehicle using maximum likelihood and Neural Gauss Newton methods

Cited by 4 publications

References 24 publications

Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator

Aerodynamic characterisation of delta wing unmanned aerial vehicle using non-gradient-based estimator

Enhancement of light aircraft 6 DOF simulation using flight test data in longitudinal motion

Identification of Aerodynamic Derivatives of a Re-entry Module

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