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 contro 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 short paper.
Bibliographical noteNOTICE: this is the author’s version of a work that was accepted for publication in Neural networks. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Herzallah, R & Kárný, M, 'Fully probabilistic control design in an adaptive critic framework' Neural networks, vol. 24, no. 10 (2011) DOI http://dx.doi.org/10.1016/j.neunet.2011.06.006
- stochastic control design
- fully probabilistic design
- adaptive control
- adaptive critic