A Comparison of the Control Schemes of Human Response to a Dynamic Virtual 3D Face

Abstract

This paper introduces the application of predictor-based control principles for the control of human response to a virtual 3D face. A dynamic woman 3D face is observed in virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimulus are observed using EEG-based excitement signals – output signal. The technique of dynamic systems identification which ensure stability and possible higher gain of the model for building a predictive input-output model of control plant is applied. Three predictor-based control schemes with a minimum variance or a generalized minimum variance control quality and constrained control signal magnitude and change rate are developed. High prediction accuracies and control quality are demonstrated by modelling results.