Data-Driven Method based Aerodynamic Parameter Estimation from Flight Data

Kumar, Ajit; Ghosh, Arnab

doi:10.2514/6.2018-0768

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…In Equation (20), the first derivative of J w.r.t. represents the gradient G and an approximation of the second derivate represents the Fisher information matrix F .…”

Section: Conventional Filter Error Methods (Fem)mentioning

confidence: 99%

“…The advancement in sensor technology has made the fabrication of micro-electro-sensor systems possible, which helps in logging the flight data acquired while performing system identification manoeuvres, even in small UAVs. Equation error methods (EEM), output error methods (OEM) [14][15][16], filter error methods (FEM) [17][18][19] and Artificial Intelligence-(AI) based methods [20][21][22][23][24] are primary aerodynamic parameter estimation methods. The least square cost function-based EEM has been touted as a promising alternative for a rapid parameter estimation technique because of its computational simplicity.…”

Section: Introductionmentioning

confidence: 99%

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Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Kumar

Saderla

et al. 2023

Aeronaut. j.

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From arsenal delivery to rescue missions, unmanned aerial vehicles (UAVs) are playing a crucial role in various fields, which brings the need for continuous evolution of system identification techniques to develop sophisticated mathematical models for effective flight control. In this paper, a novel parameter estimation technique based on filter error method (FEM) augmented with particle swarm optimisation (PSO) is developed and implemented to estimate the longitudinal and lateral-directional aerodynamic, stability and control derivatives of fixed-wing UAVs. The FEM used in the estimation technique is based on the steady-state extended Kalman filter, where the maximum likelihood cost function is minimised separately using a randomised solution search algorithm, PSO and the proposed method is termed FEM-PSO. A sufficient number of compatible flight data sets were generated using two cropped delta wing UAVs, namely CDFP and CDRW, which are used to analyse the applicability of the proposed estimation method. A comparison has been made between the parameter estimates obtained using the proposed method and the computationally intensive conventional FEM. It is observed that most of the FEM-PSO estimates are consistent with wind tunnel and conventional FEM estimates. It is also noticed that estimates of crucial aerodynamic derivatives ${C_{{L_\alpha }}},\;{C_{{m_\alpha }}},\;{C_{{Y_\beta }}},\;{C_{{l_\beta }}}$ and ${C_{{n_\beta }}}$ obtained using FEM-PSO are having relative offsets of 2.5%, 1.5%, 6.5%, 3.4% and 7.6% w.r.t. wind tunnel values for CDFP, and 1.4%, 1.9%, 0.1%, 9.6% and 7.5% w.r.t. wind tunnel values for CDRW. Despite having slightly higher Cramer-Rao Lower Bounds of estimated aerodynamic derivatives using the FEM-PSO method, the simulated responses have a relative error of less than 0.10% w.r.t. measured flight data. A proof-of-match exercise is also conducted to ascertain the efficacy of the estimates obtained using the proposed method. The degree of effectiveness of the FEM-PSO method is comparable with conventional FEM.

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“…In Equation (20), the first derivative of J w.r.t. represents the gradient G and an approximation of the second derivate represents the Fisher information matrix F .…”

Section: Conventional Filter Error Methods (Fem)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Kumar

Saderla

et al. 2023

Aeronaut. j.

View full text Add to dashboard Cite

show abstract

“…The fuzzy logic-based estimation method is also used by many researchers to characterise flight vehicle aerodynamics. Unlike the OEM and filter error method, the fuzzy logic-based estimation method does not require a mathematical model of system dynamics [20,21]. It is generally observed that the model-based estimation techniques suffer sensitivity issues with data and artificial intelligence estimation methods have limitations with the data training, and all these techniques require heavy computational capabilities for accurate estimation.…”

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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“…They deal with input and output set of data and do not need to solve the equation of motion. However, these methods have some challenges in designing and real-time implementation (Ghosh et al , 1998; Oliver Nelles, 2001; Singh and Ghosh, 2007; Peyada and Ghosh, 2009; Kumar et al , 2017; Kumar and Ghosh, 2018; Kumar and Ghosh, 2019a, 2019b, 2019c).…”

Section: Introductionmentioning

confidence: 99%

Regularization regression methods for aerodynamic parameter estimation from flight data

Kumar

Ghosh

2023

AEAT

Self Cite

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Purpose The purpose of this study is to estimate aerodynamic parameters using regularized regression-based methods. Design/methodology/approach Regularized regression methods used are LASSO, ridge and elastic net. Findings A viable option of aerodynamic parameter estimation from regularized regression-based methods is found. Practical implications Efficacy of the methods is examined on flight test data. Originality/value This study provides regularized regression-based methods for aerodynamic parameter estimation from the flight test data.

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Data-Driven Method based Aerodynamic Parameter Estimation from Flight Data

Cited by 11 publications

References 14 publications

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

Parametric model identification of delta wing UAVs using filter error method augmented with particle swarm optimisation

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

Regularization regression methods for aerodynamic parameter estimation from flight data

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