Aircraft parameter estimation using output-error methods

Góes, Luiz Carlos Sandoval; Hemerly, Elder Moreira; Maciel, Benedito Carlos de Oliveira; Neto, Wilson Rios; Mendonca, CelsoBraga; Hoff, J. C.

doi:10.1080/17415970600573544

Cited by 16 publications

(11 citation statements)

References 2 publications

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“…This paper uses the OE method for time-domain aircraft system identification. OE optimization for aircraft parameter estimation was first introduced by Main and Iliff [53,54] and remains a popular method [2,55]. Aircraft dynamics are a continuous-time system, but measurements are assumed to be taken at discrete time intervals for processing.…”

Section: Point Model System Identification: the Output-error Methodsmentioning

confidence: 99%

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

Stachiw

Crain

Ricciardi

2022

Preprint

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The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire flight envelope, the traditional models can be extended across the entire flight envelope by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire flight envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

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Section: Point Model System Identification: the Output-error Methodsmentioning

confidence: 99%

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

Stachiw

Crain

Ricciardi

2022

Preprint

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“…ese include the Equation Error Method (EEM) [5][6][7][8], the Output Error Method (OEM) [9][10][11], and the Filter Error Method (FEM) [12][13][14][15]. However, those methods still require a predefined/ initial aircraft model.…”

Section: Introductionmentioning

confidence: 99%

“…(Teacher Phase) For j � 1 to n p (2.2) Generate p T based on p T_success, j , p T_fail,j. (2.3) Update the population using(10) based on the x teacher from(14). Best, worst, average, standard deviation, and Friedman test of the RMSE at 50,000 function evaluations of 20 individual runs, with and without added noise…”

mentioning

confidence: 99%

Aircraft Control Parameter Estimation Using Self‐Adaptive Teaching‐Learning‐Based Optimization with an Acceptance Probability

Kanokmedhakul

Panagant

Bureerat

et al. 2021

Computational Intelligence and Neuroscience

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This work presents a metaheuristic (MH) termed, self-adaptive teaching-learning-based optimization, with an acceptance probability for aircraft parameter estimation. An inverse optimization problem is presented for aircraft longitudinal parameter estimation. The problem is posed to find longitudinal aerodynamic parameters by minimising errors between real flight data and those calculated from the dynamic equations. The HANSA-3 aircraft is used for numerical validation. Several established MHs along with the proposed algorithm are used to solve the proposed optimization problem, while their search performance is investigated compared to a conventional output error method (OEM). The results show that the proposed algorithm is the best performer in terms of search convergence and consistency. This work is said to be the baseline for purely applying MHs for aircraft parameter estimation.

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“…Several studies on modeling and simulation of flight control systems are included in the literature [18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] . In the study by Góes et.al 18 , a comparison of two output-error methods to inverse modeling of an aircraft are presented by using the experimental flight data. The paper given by Sembiring et.al 21 deals with the estimation of unmeasured parameters based on flight data recorded in quick access recorder (QAR) device.…”

Section: Introductionmentioning

confidence: 99%

ABC and DE Algorithms based Fuzzy Modeling of Flight Data for Speed and Fuel Computation

Bağış¹,

Konar²

2018

IJCIS

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It is crucial to evaluate the information obtained from the sensors in a fast and accurate manner in air vehicles exposed to many internal and external influences during their flights. The effectiveness and flexibility of the reasoning method comes to the forefront when the pilot or flight control system makes the necessary decisions in critical situations. The method used should be able to provide the nearest response to the decision that the pilot has to give, even when working with complex, unclear or incomplete data. Thus, it is not the correct approach to think that sensorial measurements and methods that describe them through a model are separate from each other. In this study, a fuzzy rule based model is designed using some important measurement data belonging to a flight control system for simultaneously computation of speed and fuel parameters. The fuzzy model parameters are determined by using artificial bee colony (ABC) and differential evolution (DE) algorithms in the modeling process in which actual flight data of Boeing B-767-200ER type aircraft are used. To demonstrate the efficiency of the modeling approach and the algorithms, fuzzy model structures having 3 inputs-and-2 outputs for different rule numbers are tested. The inputs of the fuzzy models are considered as altitude, weight, and engine pressure ratio. On the other hand, parameters of the flight speed and fuel amount are used for outputs of the models. The results achieved from the models are comparatively presented with each other and actual values.

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Aircraft parameter estimation using output-error methods

Cited by 16 publications

References 2 publications

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

A Physics-based Neural Network for Flight Dynamics Modelling and Simulation

Aircraft Control Parameter Estimation Using Self‐Adaptive Teaching‐Learning‐Based Optimization with an Acceptance Probability

ABC and DE Algorithms based Fuzzy Modeling of Flight Data for Speed and Fuel Computation

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