Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Modeling Human Multimodal Perception and Control Using Genetic Maximum Likelihood Estimation

Zaal, P.M.T.
Pool, D.M.
Chu, Q.P.
Van Paassen, M.M.
Mulder, M.
Mulder, J.A.

Aerospace Engineering

Control & Operations

2009-07-01

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-in-the-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 is substantial. Some adjustments to the maximum likelihood method are proposed to reduce this bias.

System Identification
Pilot Modeling
Parameter Estimation

http://resolver.tudelft.nl/uuid:8f825342-0860-4316-a3d1-66b375cce610

American Institute of Aeronautics and Astronautics (AIAA)

preprint of Journal of Guidance, Control, and Dynamics, Vol. 32, No. 4 (2009), doi: 10.2514/1.42843

Institutional Repository

journal article

(c) 2009 Delft University of Technology