Online system identification of mini cropped delta UAVs using flight test methods

Saderla, Subrahmanyam; Kim, Yoonsoo; Ghosh, Arnab

doi:10.1016/j.ast.2018.07.008

Cited by 21 publications

(11 citation statements)

References 7 publications

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“…Apart from the waveforms, the amplitudes need special care to excite certain dynamic modes [11]. The input can be a frequency sweep [12], doublet and 3-2-1-1 waveforms [34][35][36], etc. It has been suggested in [11] that the maneuvers should not exceed ±5° angles of attack, ±20°/ angular rates and ±0.…”

Section: Methodsmentioning

confidence: 99%

System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

et al. 2020

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Section: Methodsmentioning

confidence: 99%

System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

et al. 2020

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“…Since most of these applications demand the UAV to be highly manoeuvrable, they are designed to have marginal stability or even instability [1], demanding a dedicated fly-by-wire onboard autopilot. The robustness and effectiveness of an onboard autopilot are significantly influenced by the UAV dynamics, which is indeed described by its aerodynamic model [3]. Besides the above purpose, the aerodynamic model also plays an integral part in developing a realistic flight simulator for the training of pilots.…”

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.

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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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“…System identification is the method of estimating aircraft model derivatives from experimental data. This is the process of determining a suitable mathematical model, usually containing differential equations, with unknown parameters which have to be determined indirectly from the measured data (10)(11)(12) . Nowadays, there is a considerable volume of literatures on the system identification method (13)(14)(15) .…”

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

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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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Online system identification of mini cropped delta UAVs using flight test methods

Cited by 21 publications

References 7 publications

System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

System Identification and Control for a Tail-Sitter Unmanned Aerial Vehicle in the Cruise Flight

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

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

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