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, unknown feedback, unknown state order, and possibly unstable or highly resonant dynamics. 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 rank selection based on canonical variate analysis (CVA) is related to partial least squares (PLS) and principal component analysis (PCA) methods. Statistical order selection methods are described which give optimal determination of state order. The state space dynamics are determined by simple multivariate regression.
The concepts are presented in a direct first principles way that is appropriate for advanced undergraduate and graduate curriculum so that automated system identification can be made much more accessible to those in most need of using it. This advance in system identification has major implications for analysis, system monitoring, and design and implementation of control systems for many applications including aerospace systems and industrial process control. Several such examples are presented including an industrial recovery boiler, stirred tank reactor, autothermal reactor, distillation column, and on-line adaptive control of aircraft wing flutter. Automated system identification methods are compared with alternative approaches in terms of model types considered, required user knowledge, computational requirements and reliability, and results of model fitting using simulated data sets.
The intended audience includes those wishing to do model identification on applications data, those wishing an introduction to the concepts of automated system identification, those considering teaching an undergraduate or graduate course on the subject, or those with more advanced background. A draft of an introductory textbook on automated system identification including Matlab compatible software will be included in the course.
Registration and Fees: One workshop: $250 Two workshops: $375 Students: $100 per workshop 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 1997 American Control Conference and other workshops.
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