Numerical Identification of Linear Dynamic Systems from Normal Operating Records
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TL;DR
A technique for numerical identification of a discrete time system from input/output samples using linear stochastic control theory to design strategies for control of the system and its parameters are estimated by Maximum Likelihood.
Abstract
A technique for numerical identification of a discrete time system from input/output samples is described. The purpose of the identification is to design strategies for control of the system. The strategies are obtained using linear stochastic control theory. The parameters of the system are estimated by Maximum Likelihood. An algorithm for solving the M.L. equations is given. The estimates are in general consistent, asymptotically normal and efficient for increasing sample lengths. These properties and also the parameter accuracy are determined by the information matrix. An estimate of this matrix is given. The technique has been applied to simulated data and to plant data.