Predictive human operator model to be utilized as a controller using linear, neuro-fuzzy and fuzzy-ARX modeling techniques

Abstract

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.