Retrospective and Recent Examples of Aircraft and Rotorcraft System Identification at DLR

Deiler, Christoph; Mönnich, W.; Seher-Weiß, Susanne; Wartmann, Johannes

doi:10.2514/1.c037262

Cited by 3 publications

(2 citation statements)

References 81 publications

(123 reference statements)

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“…Recent research [5] offers an excellent overview of the state of the art in this field. Tools for the time domain are comprehensively examined in [7], with additional contributions from the German Aerospace Center featured in [8]. Furthermore, system identification techniques for aircraft dynamics in the frequency domain have been provided in [9].…”

Section: Introduction 1motivationmentioning

confidence: 99%

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Peña-García,

Velázquez-Sánchez,

Gómez-Daza-Argumedo

et al. 2024

Aerospace

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This research introduces a physics-based identification technique utilizing genetic algorithms. The primary objective is to derive a parametric matrix, denoted as A, describing the time-invariant linear model governing the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests that the genetic algorithm provided with the adequate fitness function could be an appealing alternative for aircraft model identification, even when limited data are available. Furthermore, in some cases, linearization using a genetic algorithm can be more efficient than classical methods.

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Section: Introduction 1motivationmentioning

confidence: 99%

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Peña-García,

Velázquez-Sánchez,

Gómez-Daza-Argumedo

et al. 2024

Aerospace

View full text Add to dashboard Cite

show abstract

Section: Introduction 1motivationmentioning

confidence: 99%

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Peña-García,

Velázquez-Sánchez,

Gómez-Daza-Argumedo

et al. 2023

Preprint

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This work proposes a physics-based identification technique based on genetic algorithms. The main objective is to obtain a parametric matrix A that describes the time-invariant linear model of the longitudinal dynamics of an aircraft. This is achieved by proposing a fitness function based on the properties of the transition matrix and taking advantage of some of the capabilities of the genetic algorithm, mainly those of restricting the search ranges of the unknowns. In this case, such unknowns are related to the type of aircraft and flight conditions that are considered during the identification process. The proposed identification method is validated with a reliable nonlinear model that can be found in the literature, as well as with the calculation of the trim condition and linearization generally used in aircraft dynamics. In summary, this study suggests the genetic algorithm provided with the adequate fitness function, could be an appealing alternative for aircraft model identification, even when only a limited amount of data is available. Furthermore, in some cases, linearization using genetic algorithm can be more efficient than classical methods.

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Data-Driven Approach for Fixed-Frame Rotorcraft Mast-Moment Estimation

Döring,

Wartmann,

Seher-Weiß

2024

Journal of Guidance, Control, and Dynamics

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In contrast to the fixed frame, measuring rotor states in the rotating frame of a helicopter rotor is complex and costly and therefore usually unaffordable for small production rotorcraft. In this paper, a data-driven approach to linear observer tuning is proposed and illustrated for predicting the rotor mast bending moments using fixed-frame rigid-body measurements only. The method is demonstrated on a research helicopter, where flight test data is used to identify three possible design models, optimize their respective observer performances, and select the most promising model structures for final application. The resulting observers are assessed in the time and frequency domains using sweep and turbulence data. The results show that data-driven observer tuning allows the effective adaptation of the observer to the reference data. To extend the observer to a larger flight envelope, improved models and a larger database would be required.

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Retrospective and Recent Examples of Aircraft and Rotorcraft System Identification at DLR

Cited by 3 publications

References 81 publications

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Physics-Based Aircraft Dynamics Identification Using Genetic Algorithms

Data-Driven Approach for Fixed-Frame Rotorcraft Mast-Moment Estimation

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