Over the past several years, computational methods and software have been developed to reliably identify system dynamics from input/output data with optimal statistical accuracy. These automatic methods apply to a very general class of linear systems including multi-input/multi-output, state and measurement noise disturbances, possibly unstable or highly resonant dynamics, and systems with unknown feedback, state order, and/or delays. Existing methods for high accuracy identification such as Box/Jenkins and prediction error methods are problematic in that they are both computationally unreliable and involve a tedious toolbox approach requiring graduate level training. The automatic methods presented in this workshop are fundamentally different and involve direct determination of the system states, i.e. system rank, using stable singular value decomposition (SVD) computations. Optimal statistical selection of the state order based on canonical variate analysis (CVA) is related to partial least squares (PLS) and principal component analysis (PCA) methods. The major implications are discussed for analysis, system monitoring, and design and implementation of control systems for industrial processes. Several such examples are presented including an industrial recovery boiler, stirred tank reactor, autothermal reactor, distillation column, and the Tennessee Eastman Challenge Problem. Contents:
Registration and Fees: 100 EURO/ 200 DM. The fee includes a printed copy of the presentation material.
For more information about this workshop contact:
Dr. Wallace E. Larimore, President
1717 Briar Ridge Road
McLean, VA 22101
Phone: 703 532-0062; Fax: 703 536-3319
Click here for more information about the 1999 European Control Conference and other workshops.
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