Application of Bayesian Controllers to Dynamic Systems

Abstract

Bayesian networks for the static as well as for the dynamic case have gained an enormous interest in the research community of machine learning and pattern recognition. Although the parallels between dynamic Bayesian networks and Kalman filters are well-known since many years, Bayesian networks have not been applied to problems in the area of adaptive control of dynamic systems. In our work we exploit the well-known similarities between Bayesian networks and Kalman filters to model and control linear dynamic systems using dynamic Bayesian networks. The analytical models are compared with models being trained with step and impulse response. The experiments show that the analytical model as well as the trained model are suitable for control purposes, which leads to the idea of self adaptive controllers.