Abstract
Computationally feasible suboptimal strategies for adaptive control of a multivariable discrete-time stochastic system are described. These strategies are based on the Bayesian approach to the system identification and on approximation of the dynamic programming procedure. Resulting algorithms - with computational simplicity which is comparable with one-step ahead strategy - can be used also for adaptive control of nonminimumphase systems.
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