Lateral aircraft parameter estimation using neuro-fuzzy and genetic algorithm based method

Roy, Abhishek; Peyada, N. K.

doi:10.1109/aero.2017.7943677

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Similarly, C Y , C l , and C n at μ th are derived by using the measured value of the sensor variables b a y ðμÞ the dynamic pressure q, the mass of the aircraft, and the inertial parameters as shown by equations ( 6)-( 8) 38,43,44…”

Section: Neural Artificial Bee Colony (Nabc) Fusion Algorithmmentioning

confidence: 99%

“…Similarly, CY, Cl, and Cn at μth are derived by using the measured value of the sensor variables truea^y(μ) the dynamic pressure q¯, the mass of the aircraft, and the inertial parameters as shown by equations (6)–(8) 38,43,44 whereHere, m is the aircraft mass, Feng the total thrust, ΔFeng is the differential thrust, σeng the tilt angle of the engine thrust line, [I(*)] repre...…”

Section: Neural Artificial Bee Colony (Nabc) Fusion Algorithmmentioning

confidence: 99%

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Estimation of aerodynamic parameters using neural artificial bee colony fusion algorithm for moderate angle of attack using real flight data

Kumar,

Sonkar,

George

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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Aircraft system identification aims to estimate the aerodynamic force and moment coefficients utilizing intelligent modeling and parametric identification methodologies. Classical methods like output, filter, and equation error methods apply extensively as parametric approaches. In contrast, machine learning approaches like Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference Systems (ANFIS), etc., are alternatives to model-based methods. This work presents a novel aerodynamic parameters estimation technique that fuses two biologically inspired optimization techniques, (i) the Artificial Bee Colony (ABC) optimization and (ii) ANN for an actual aircraft while incorporating system and measurement uncertainty. The fusion of ABC and ANN imparts the ability to address sensor noise challenges associated with system identification and parameter estimation. Comparison of the proposed method’s results with the benchmark techniques like Least Square, Filter Error, and Neural Gauss Methods using recorded flight data of the ATTAS (DLR German Aerospace Centre) and HANSA-3 (IIT Kanpur) aircrafts established its adequacy and efficacy. Furthermore, the capability of the proposed hybrid method to extract stability and control variables from the stable aircraft kinematics is shown even with insufficient information in its data history.

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Section: Neural Artificial Bee Colony (Nabc) Fusion Algorithmmentioning

confidence: 99%

Section: Neural Artificial Bee Colony (Nabc) Fusion Algorithmmentioning

confidence: 99%

Estimation of aerodynamic parameters using neural artificial bee colony fusion algorithm for moderate angle of attack using real flight data

Kumar,

Sonkar,

George

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part G:

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“…In contrast, the Unscented Kalman Filter (UKF) uses as a non-linear filter without linearising the nonlinear model (10) . Data-driven methods such as Neural Partial Differentiation (NPD) (11)(12)(13) , the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are also used for parameter estimation (14)(15)(16) . However, these black-box modelling techniques suffer from the drawback of requiring training with a large number of datasets and longer computational time.…”

Section: Introductionmentioning

confidence: 99%

Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

Mohamed

Joy²

2021

Aeronaut. j.

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This paper aims to accurately estimate the lateral directional aerodynamic parameters in real time irrespective of the variations in the process and measurement covariance matrices. The proposed algorithm for parameter estimation is based on the integration of adaptive techniques into a stochastic nonlinear filter. The proposed adaptive estimation algorithm is applied to flight test data, and the lateral directional derivatives are estimated in real time. The estimates are compared with those obtained from the Filter Error Method (FEM), an offline parameter estimation method accounting for process noise. The estimation results are observed to be very comparable, and the supremacy of the adaptive filter is illustrated by varying the covariance matrices of both process and measurement noises. The parameters estimated by the adaptive filter are found to converge to their actual values, whereas the estimates of the regular filter are observed to diverge from the actual values when changing the noise covariance matrices. The proposed adaptive algorithm can estimate the lateral directional aerodynamic derivatives more accurately without prior knowledge of either process or measurement noise covariance matrices. Hence, it is of great value in online implementations.

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“…Neuro-fuzzy is considered a gray-box modeling technique between neural networks and fuzzy logic based models. 7,8,[10][11][12][13][14] In this work, a novel technique ANFIS-Delta method has been used to estimate the aerodynamic parameters. ANFIS combines the ANN with a fuzzy set theory to map predictor variables to target outputs, which can efficiently approximate a function to the desired degree of accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…ANFIS combines the ANN with a fuzzy set theory to map predictor variables to target outputs, which can efficiently approximate a function to the desired degree of accuracy. 13 Also, like Delta method 15 it does not require priori postulation of the equation of motion and solution of the equation, which makes it an alternate choice in comparison to popular methods such as least squares (LS) and maximum likelihood estimation (MLE). Also, a fuzzy inference system equipped with learning ability becomes directly applicable to fuzzy controllers.…”

Section: Introductionmentioning

confidence: 99%

ANFIS-Delta method for aerodynamic parameter estimation using flight data

Kumar

Ghosh

2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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In this paper, aerodynamic parameters have been estimated using neuro-fuzzy-based novel approach (ANFIS-Delta). ANFIS-Delta is an extension of a feed-forward neural network based Delta method. This method augments the philosophies of an adaptive neuro-fuzzy inference system (ANFIS) in the Delta method. The current work studies the comparison of ANFIS-Delta estimated results with the existing methods using the flight data gathered on the Hansa-3 research aircraft at IIT Kanpur and also, demonstrates the efficacy of the algorithm on DLR HFB-320 aircraft data. Further, the robustness of the ANFIS-Delta is examined using simulated data with known measurement noise of various strength and estimated parameters are compared with the wind tunnel extracted aerodynamic parameters.

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Lateral aircraft parameter estimation using neuro-fuzzy and genetic algorithm based method

Cited by 6 publications

References 10 publications

Estimation of aerodynamic parameters using neural artificial bee colony fusion algorithm for moderate angle of attack using real flight data

Estimation of aerodynamic parameters using neural artificial bee colony fusion algorithm for moderate angle of attack using real flight data

Lateral directional aircraft aerodynamic parameter estimation using adaptive stochastic nonlinear filter

ANFIS-Delta method for aerodynamic parameter estimation using flight data

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