Maximum likelihood stability and control derivative identification of a Cessna Citation II

Milne, G.W.; Soijer, Marco W.; Juliana, S.; Hermansyah, Hermansyah; Mulder, J.A.

doi:10.2514/6.2001-4013

Cited by 3 publications

(4 citation statements)

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“…Indeed it was found that, because of this and the particular dynamics of the system (discussed subsequently), relatively large inputs are required compared to typical inputs used for linear model identification with fixed-wing aircraft. 41,42,43 This, however, may conflict with the linearity requirement. Alternatively, a solution may be to minimise this problem by completely filtering out the flapping content prior to identification.…”

Section: Input Design Remarksmentioning

confidence: 99%

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Armanini

Karásek

Visser

et al. 2017

AIAA Atmospheric Flight Mechanics Conference

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Section: Input Design Remarksmentioning

confidence: 99%

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Armanini

Karásek

Visser

et al. 2017

AIAA Atmospheric Flight Mechanics Conference

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“…One-step techniques, such as the maximum likelihood method, estimate both the state variables and the aerodynamic parameters at the same time by an optimization process. This is done by a formulation of the process model that implicitly includes the aerodynamic coefficients, requiring an a priori knowledge of the structure of the aerodynamic model [21,22]. A study on kite aerodynamic identification with estimations of the generated lift and drag, and based on some a priori system modelization, has been presented recently [23].…”

mentioning

confidence: 99%

Flight-Path Reconstruction and Flight Test of Four-Line Power Kites

Borobia-Moreno

Sánchez‐Arriaga

Serino

et al. 2018

Journal of Guidance, Control, and Dynamics

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A flight-path reconstruction algorithm for tethered aircraft, which is based on an extended Kalman filter, is presented. The algorithm is fed by the measurements of a set of onboard and ground-based instruments and provides the optimal estimation of the system state-space trajectory, which includes typical aircraft variables such as position and velocity, as well as an estimation of the aerodynamic force and torque. Therefore, it can be applied to closed-loop control in airborne wind energy systems and it is a first step toward aerodynamic parameter identification of tethered aircraft using flight-test data. The performance of the algorithm is investigated by feeding it with real flight data obtained from a low-cost and highly portable experimental setup with a four-line kite. Several flight tests, which include pullup and lateral-directional steering maneuvers with two kites of different areas, are conducted. The coherence of the estimations provided by the filter, such as the kite state-space trajectory and aerodynamic forces and torques, is analyzed. For some standard variables, such as kite Euler angles and position, the results are also compared with a second independent onboard estimator.

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“…[19][20][21][22][23]28 One of the main reasons for this is that maximum likelihood estimates have some attractive statistical properties. They are consistent and efficient, which means that the parameter estimate converges to the true parameter set and that the variance reduces to the Cramér-Rao lower bound as the sample size increases.…”

Section: Genetic Maximum Likelihood Estimationmentioning

confidence: 99%

“…Maximum likelihood estimation is an example of a statistical time-domain identification method that, for instance, has been successfully applied to the identification of aircraft stability and control derivatives from flight test data [19][20][21] and of air-and spacecraft structural modes. 22,23 This study focuses on the application of a maximum likelihood parameter estimation algorithm to the problem of multi-channel pilot model identification.…”

mentioning

confidence: 99%

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Zaal

Pool

Chu

et al. 2009

Journal of Guidance, Control, and Dynamics

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This paper presents a new method for estimating the parameters of multi-channel pilot models that is based on maximum likelihood estimation. To cope with the inherent nonlinearity of this optimization problem, the gradient-based Gauss-Newton algorithm commonly used to optimize the likelihood function in terms of output error is complemented with a genetic algorithm. This significantly increases the probability of finding the global optimum of the optimization problem. The genetic maximum likelihood method is successfully applied to data from a recent human-inthe-loop experiment. Accurate estimates of the pilot model parameters and the remnant characteristics were obtained. Multiple simulations with increasing levels of pilot remnant were performed, using the set of parameters found from the experimental data, to investigate how the accuracy of the parameter estimate is affected by increasing remnant. It is shown that only for very high levels of pilot remnant the bias in the parameter estimates

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Maximum likelihood stability and control derivative identification of a Cessna Citation II

Cited by 3 publications

References 0 publications

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Flight Testing and Preliminary Analysis for Global System Identification of Ornithopter Dynamics Using On-board and Off-board Data

Flight-Path Reconstruction and Flight Test of Four-Line Power Kites

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

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