Informatica

This paper introduces how predictor-based control principles are applied to the control of human excitement signal as a response to a 3D face virtual stimuli. A dynamic human 3D face is observed in a virtual reality. We use changing distance-between-eyes in a 3D face as a stimulus – control signal. Human responses to the stimuli are observed using EEG-based signal that characterizes excitement. A parameter identification method for predictive and stable model building with the smallest output prediction error is proposed. A predictor-based control law is synthesized by minimizing a generalized minimum variance control criterion in an admissible domain. An admissible domain is composed of control signal boundaries. Relatively high prediction and control quality of excitement signals is demonstrated by modelling results.

Design problems of predictor-based self-tuning digital control systems for different kinds of linear and non-linear dynamical plants are discussed. Special cases include linear plants with unstable and nonminimum-phase control channels, linear plants with inner feedbacks, nonlinear Hammerstein and Wiener-Hammerstein-type plants. Considered are control systems based on generalized minimum variance algorithms with amplitude and introduction rate restrictions for the control signal.