Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using Maximum Likelihood, Least Square and Neural Gauss Newton methods

Saderla, Subrahmanyam; Dhayalan, R.; Singh, Kirtiman; Kumar, N.; Ghosh, Arnab

doi:10.1017/aer.2019.70

Cited by 11 publications

(10 citation statements)

References 22 publications

(41 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Some of the longitudinal and lateral-directional parameters of both CDFP and CDRW UAVs have already been estimated using wind tunnel testing in Refs [40][41][42]. The parameters estimated using FEM and FEM-PSO are compared against the corresponding wind tunnel estimates for both CDFP and CDRW.…”

Section: Resultsmentioning

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.

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

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.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However in the case of UAVs, the availability of research related to the aerodynamic characterisation from flight data is minimal due to their classified applications. EEM is one of the simplest and computationally efficient flight test methods to estimate flight vehicle aerodynamic stability and control derivatives from flight test data [29]. However, the formulation of EEM restricts its application in estimating aerodynamic parameters from flight data pertaining to near stall and high angle-of-attack manoeuvers.…”

Section: Introductionmentioning

confidence: 99%

“…It is well observed that both methods require priory information about initial conditions for better convergence and confidence of solutions. On the contrary, the AI estimation method based on Neural Networks does not require priory information about initial conditions and can be used to characterise the aerodynamic behaviour of a UAV in linear and nonlinear flight regimes [29,31]. In general, the Neural Networks estimation method is based on OEM requires gradient computation to update the weights of networks, which makes it a high computational effort demanding method.…”

Section: Introductionmentioning

confidence: 99%

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

2022

Self Cite

View full text Add to dashboard Cite

In the era of Unmanned Aerial Systems (UAS), an onboard autopilot occupies a prominent place and is inevitable for many of their modern applications. The efficacy of autopilot heavily relies upon the accuracy of the sensors employed and the capability of the onboard flight controller. In general, aerodynamic behaviour and flight dynamic capabilities of Unmanned Aerial Vehicles (UAVs) govern the selection and the design of flight controllers. Precise modeling of linear aerodynamic characteristics from flight data can be achieved using many of the existing classical parameter estimation techniques such as Output Error Method (OEM), Equation Error Method (EEM), and Filter Error Method (FEM). However, all the classical methods may not be readily applicable for aerodynamic modeling in nonlinear flight envelopes. The current manuscript is an attempt to exploit the capabilities of the Artificial Intelligence (AI) technique, named Particle Swarm Optimisation (PSO), in combination with Least Squares (LS) cost function to perform linear as well as nonlinear aerodynamic parameter estimation. The aforementioned task is accomplished by considering flight data from manoeuvers pertaining to linear angles of attack, moderate and near stall flight envelopes of two different UAVs with cropped delta planform geometry. Parameters estimated using the proposed LS-PSO method are consistent with minimum standard deviation and are on a par with OEM estimates. The proposed LS-PSO method enhances the capabilities of LS-based EEM while estimating stall characteristic parameters, which was not possible with LS alone. The longitudinal and lateral-directional static parameters estimated from the full-scale wind tunnel testing of the two UAVs were also used to corroborate the results obtained from the flight data using the LS-PSO method.

show abstract

“…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%

“…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). Verma and Peyada (18) utilized the extreme learning machine based to extract the stability and control derivatives of the all composites HANSA-3 aircraft using the real flight test data.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Longitudinal and lateral aerodynamic characterisation of reflex wing Unmanned Aerial Vehicle from flight tests using Maximum Likelihood, Least Square and Neural Gauss Newton methods

Cited by 11 publications

References 22 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

System identification of cropped delta UAVs from flight test methods using particle Swarm-Optimisation-based estimation

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

Contact Info

Product

Resources

About