Genetic Algorithm Based Approach for Parameters Estimation of the Hess Operator Model

Zaychik, Kirill B.; Cardullo, Frank M.

doi:10.2514/6.2007-6893

Cited by 3 publications

(5 citation statements)

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“…18,30 The problem solving capabilities of genetic algorithms are based on the principle of "survival of the fittest" as considered in evolutionary biology. In such an algorithm, a population of candidate solutions to the optimization problem is subjected to random genetic functions such as selection, mating, crossover and mutation.…”

Section: Iiib Genetic Likelihood Optimizationmentioning

confidence: 99%

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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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Section: Iiib Genetic Likelihood Optimizationmentioning

confidence: 99%

“…[13][14][15][16][17][18] These studies only considered very simple pilot models and provide little detail of the identification procedure.…”

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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“…In order to ensure an accurate description of the pure delay link at high frequency, and avoid a high approximation of the PEC model, this paper uses a fifth-order Padé approximation [25]:…”

Section: Oes-based Mlementioning

confidence: 99%

“…Hence, the relationship between image signal delay τ and seeker tracking loop openloop gain K, under the change of visual perception lead time constant τ L , is expressed as Equation (25).…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Zhang,

Li,

et al. 2023

Aerospace

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In the human-in-loop (HIL) guidance mode, a pilot quickly identifies and flexibly locks on to a target through a real-time image signal transmitted by the aircraft. Then, the line-of-sight (LOS) angle error in the viewing field is tracked and compensated for in order to improve the guidance and control performance of the image-guided aircraft. Based on the physical structure and device parameters of the image seeker, an appropriate correction network is designed to improve the performance of the seeker stability loop. Aiming at a precise-extended crossover (PEC) pilot model, the structure of the dynamic model is optimized, and the maximum likelihood estimation (MLE) method of the output error structure is used to identify the dynamic parameters. This makes up for the deficiency of the existing modeling. In order to solve the nonlinear optimization problems encountered in the identification process, a hybrid strategy of a genetic algorithm (GA) and Gauss–Newton optimization algorithm is used to improve the probability of finding the global optimal solution. The simplex method is also used to improve the robustness of the algorithm. In addition, a hardware-in-the-loop simulation is designed and multi-round HIL experiment flow is performed. Moreover, based on the adaptability of the pilot to different image signal delays, the effects of different image signal delays on the stability and disturbance rejection rate (DRR) of the seeker control system are studied. The results demonstrate that the hybrid gradient optimization algorithm (HGOA) can find the global optimal value, and the identification model can accurately reflect the dynamic characteristics of the pilot. In the HIL guidance mode, the tracking compensation behavior of the pilot can reduce the influence of image signal delay on the disturbance of the aircraft body isolated by the seeker. The optimized PEC model and the identified dynamic parameters improve the efficiency of pilot training and screening.

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Discriminating Between Individuals Based on the Parameters of the Enhanced Model of the Human Operator

Zaychik

Cardullo

2009

AIAA Modeling and Simulation Technologies Conference

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Genetic Algorithm Based Approach for Parameters Estimation of the Hess Operator Model

Cited by 3 publications

References 0 publications

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Parameter Identification of Pilot Model and Stability Analysis of Human-in-Loop Image Seeker

Discriminating Between Individuals Based on the Parameters of the Enhanced Model of the Human Operator

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