Adaptive finite state models of the human operator
Human operator adaptive finite state mathematical models
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Human operator adaptive finite state mathematical models
Human operator performance in tracking study of pilots determined by deterministic characterization theory and mathematical model
The worldwide demand for Small Modular Reactors (SMRs) has surged in recent years due to their enhanced safety and versatility in supporting diverse industrial sectors. A unique feature of SMR operation is that a single human operator is responsible for managing multiple modules. Therefore, securing a sufficient amount of human performance data pertaining to this new environment is essential for the safe operation of SMRs. In this explanatory study, a series of experiments were conducted using the NuScale simulator, a representative SMR design, with student operators. A total of 12 student operators were assigned two types of off-normal events and asked to cope with them using paper-based procedures. Subsequently, their task performance times were compared with those of student operators responsible for a single unit based on the Task Complexity (TACOM) measure. Results indicate that the performance of student operators under the experimental conditions of this study degraded by a factor of 2 to 3, depending on the characteristics of the off-normal events.
Mathematical model for human operator
Human operator as control element in man machine systems
Analysis techniques for human operator dynamic characteristics
Time series analysis is applied to model human operator dynamics in pursuit and compensatory tracking modes. The normalized residual criterion is used as a one-step analytical tool to encompass the processes of identification, estimation, and diagnostic checking. A parameter constraining technique is introduced to develop more reliable models of human operator dynamics. The human operator is adequately modeled by a second order dynamic system both in pursuit and compensatory tracking modes. In comparing the data sampling rates, 100 msec between samples is adequate and is shown to provide better results than 200 msec sampling. The residual power spectrum and eigenvalue analysis show that the human operator is not a generator of periodic characteristics.
Decision making mathematical model for control system operation modes, generating human operators heuristics evolution
Except in low-bandwidth systems, knowledge of the human operator transfer function is essential for high-performance telerobotic systems. This information has usually been derived from detailed analyses of tracking performance, in which the human operator is considered as a complete system rather than as a summation of a number of subsystems, each of which influences the operator's output. Studies of one of these subsystems, the limb mechanics system, demonstrate that large parameter variations can occur that can have a profound effect on the stability of force-reflecting telerobot systems. An objective of this research was to decompose the performance of the human operator system in order to establish how the dynamics of each of the elements influence the operator's responses.
The accurate and efficient identification of the human operator is still a need in human factors engineering especially concerning multivariable control. Control theoretic identification methods need to be tested with human operator models under realistic boundary conditons. The requirements and criteria for the use of parametric methods, selected models as well as the Maximum Likelihood Method and the Extended Kalman Filter are displayed. The experiments and results are comparatively discussed from the point of practical engineering.
Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.
Control-theoretic modeling of the human operator dynamic behavior in manual control tasks has a long and rich history. In the last two decades, there has been a renewed interest in modeling the human operator. There has also been significant work on techniques used to identify the pilot model of a given structure. The purpose of this research is to attempt to go beyond pilot identification based on collected experimental data and to develop a predictor of pilot behavior. An experiment was conducted to quantify the effects of changing aircraft dynamics on an operator s ability to track a signal in order to eventually model a pilot adapting to changing aircraft dynamics. A gradient descent estimator and a least squares estimator with exponential forgetting used these data to predict pilot stick input. The results indicate that individual pilot characteristics and vehicle dynamics did not affect the accuracy of either estimator method to estimate pilot stick input. These methods also were able to predict pilot stick input during changing aircraft dynamics and they may have the capability to detect a change in a subject due to workload, engagement, etc., or the effects of changes in vehicle dynamics on the pilot.
Human operator models parameter estimation by stochastic approximation, considering continuous and sampled data models
A technique is described which is utilized for modeling human operator dynamics. The technique is based upon a model-reference system configuration and a class of Liapunov functions formulated for this purpose which possess variable characteristics. It is shown how such a class of Liapunov functions and their time derivatives is formulated. The crux of the formulation lies in three variable positive definite matrices used for the construction of the class of Liapunov functions. The form and order of these matrices depend upon the form and order of the error differential equation of the human operator model-reference system. These matrices can be modified to include nonlinear functions of the human operator model.
A report is given on the ongoing efforts to mode the human operator in the context of the task during the enroute/return phases in the ground based control of multiple flights of remotely piloted vehicles (RPV). The approach employed here uses models that have their analytical bases in control theory and in statistical estimation and decision theory. In particular, it draws heavily on the modes and the concepts of the optimal control model (OCM) of the human operator. The OCM is being extended into a combined monitoring, decision, and control model (DEMON) of the human operator by infusing decision theoretic notions that make it suitable for application to problems in which human control actions are infrequent and in which monitoring and decision-making are the operator's main activities. Some results obtained with a specialized version of DEMON for the RPV control problem are included.
A systematic method is proposed for the development, optimization, and comparison of controller-models for the human operator. This is suitable for any designed model, even multiparameter systems. A random search technique is chosen for the parameter optimization. As valuation criteria for the quality of the model development the criterion function - the comparison between the input and output functions of the human operator and those of the model - and the most important characteristic values and functions of the statistical signal theory are used. A nonlinear multiparameter model for the human operator is being designed which considers the complex input information rate per time in a single display. The nonlinear features of the model are effected by a modified threshold element and a decision algorithm. Different display-configurations as well as various transfer functions of the controlled element are explained by different optimized parameter-combinations.
Asynchronous pulse amplitude, pulse width human operator model for producing discrete outputs in response to continuously presented Gaussian random inputs
A Human Operator Simulator (HOS) based pilot model has been developed for use at NASA LaRC for analysis of flight management problems. The model is currently configured to simulate piloted flight of an advanced transport airplane. The generic HOS operator and machine model was originally developed under U.S. Navy sponsorship by Analytics, Inc. and through a contract with LaRC was configured to represent a pilot flying a transport airplane. A version of the HOS program runs in batch mode on LaRC's (60-bit-word) central computer system. This document provides a guide for using the program and describes in some detail the assortment of files used during its operation.