Abstract
A new method is proposed for adaptation of the plant transition matrix of sampled data systems. Correction matrices are based on the difference between predicted and observed values of the state vector. They are constructed adaptively in a way analogous to the Davidon method for finding the Jacobians in nonlinear equation solving. Two numerical examples are presented. Convergence was obtained on a simulation of a simple two-dimensional process as well as on a six-dimensional process. Two adaptive algorithms are presented, one that is efficient for large initial error and one that converges more slowly but gives smooth behavior when initial error is small.
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