M. Aronsson, L. Arvastson, J. Holst, B. Lindoff and A. Svensson
Department of Mathematical Statistics,
Lund Institute of Technology,
In this paper we present a new way to control linear stochastic systems.
The method is based on statistical bootstrap techniques. The optimal future
control signal is derived in such a way that unknown noise distribution and
uncertainties in parameter estimates are taken into account. This is achieved
by resampling from existing data when calculating statistical distributions
of future process values. The bootstrap algorithm takes care of arbitrary
loss functions and unknown noise distribution even for small estimation sets.
The efficient way of utilizing data implies that the method is also well
suited for slowly time-varying stochastic systems.
Statistical bootstrap techniques, Resampling, Optimal Control, Generalized
Predictive Control, Stochastic Control, Quality Control, Statistical Process