Fully probabilistic control design in an adaptive critic framework

Authors:
Randa Herzallah;Miroslav Kárný
Affiliations:
Faculty of Engineering Technology, Al-Balsa Applied University, Jordan;Institute of Information Theory and Automation, Academy of Sciences of the Czech Republic, Czech Republic
Venue:
Neural Networks
Year:
2011

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Abstract

Optimal stochastic controller pushes the closed-loop behavior as close as possible to the desired one. The fully probabilistic design (FPD) uses probabilistic description of the desired closed loop and minimizes Kullback-Leibler divergence of the closed-loop description to the desired one. Practical exploitation of the fully probabilistic design control theory continues to be hindered by the computational complexities involved in numerically solving the associated stochastic dynamic programming problem; in particular, very hard multivariate integration and an approximate interpolation of the involved multivariate functions. This paper proposes a new fully probabilistic control algorithm that uses the adaptive critic methods to circumvent the need for explicitly evaluating the optimal value function, thereby dramatically reducing computational requirements. This is a main contribution of this paper.