We are happy to announce that our conference paper "Linear Optimal Control on Factor Graphs
- A Message Passing Perspective" has been accepted for presentation and publication in the proceedings of the 20th World Congress of the International Federation of Automatic Control. The conference will be held from July 9-14, 2017 in Toulouse, France.
Read the abstract below:
Factor graphs form a class of probabilistic graphical models representing the factorization of probability density functions as bipartite graphs. They can be used to exploit the conditional independence structure of the underlying model to efficiently solve inference problems by message passing. The present paper advocates the use of factor graphs in control and highlights similarities to, e. g., signal processing and communications where this class of models is widely used. By applying the factor graph framework to a probabilistic interpretation of optimal control, several classical results are recovered. The dynamic programming approach to linear quadratic Gaussian control is described as a message passing algorithm on factor graph on which possible extensions are exempli ed. A factor graph-based iterative learning control scheme is outlined and an expectation maximization-based estimation of normal unknown variance priors
is adapted for the derivation of sparse control signals, highlighting the benefits of using a unified framework across disciplines by mixing and matching corresponding graphical algorithms.
