Model adaptive dual control of MIMO stochastic systems

Abstract

An adaptive dual control algorithm is presented for multiple-input, multiple output (MIMO) linear systems with input and output noise and unknown parameters. The system parameters are assumed to belong to a finite set on which a prior probability distribution is available. The difficulties in characterizing the future evolution if MIMO system information as required by the dynamic programming are overcome through a novel way of using preposterior analysis. This provides a probabilistic characterization of the future adaptation process and allows the controller to take advantage of the dual effect.