System identification: theory for the user
System identification: theory for the user
Random Data: Analysis and Measurement Procedures
Random Data: Analysis and Measurement Procedures
Human skill transfer: neural networks as learners and teachers
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 3 - Volume 3
Making Driver Modeling Attractive
IEEE Intelligent Systems
Neuro-fuzzy controller design via modeling human operator actions
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Engineering Applications of Artificial Intelligence
Extracting fuzzy control rules from experimental human operatordata
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An intelligent driver warning system for vehicle collision avoidance
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Identification of driver state for lane-keeping tasks
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Paper: Human dynamics in man-machine systems
Automatica (Journal of IFAC)
Takagi-Sugeno fuzzy modeling incorporating input variables selection
IEEE Transactions on Fuzzy Systems
Fuzzy model based control: stability, robustness, and performance issues
IEEE Transactions on Fuzzy Systems
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Modeling human operator's behavior as a controller in a closed-loop control system recently finds applications in areas such as training of inexperienced operators by expert operator's model or developing warning systems for drivers by observing the driver model parameter variations. In this research, first, an experimental setup has been developed for collecting data from human operators as they controlled a nonlinear system. Appropriate reference signals and scenarios were designed according to the system identification and human operator modeling theory, to collect data from subjects. Different modeling schemes, namely ARX models as linear approach, and adaptive-network-based fuzzy inference system (ANFIS) as intelligent modeling approach have been evaluated. A hybrid modeling method, fuzzy-ARX (F-ARX) model, has been developed and its performance was found to be better in terms of predicting human operator's control actions as well as replacing the operator as a stand-alone controller. It has been concluded that F-ARX models can be a good alternative for modeling the human operator.