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) are related to partial least squares (PLS), principal component analysis (PCA), and subspace system identification methods. Statistical order selection methods are described that 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, automotive, and chemical and industrial processes. For process monitoring, CVA provides powerful new methods for the detection and analysis of changes in colinear multivariable processes. For control system implementation, CVA provides high accuracy multivariable models even in the presence of unknown system feedback. CVA provides a starting point for robust control design since accuracy confidence bands on the identified model are automatically provided. Since the computation is completely automatic, it is ideal for online and adaptive control applications. A number of such applications are presented including an industrial recovery boiler, stirred tank reactor, autothermal reactor, distillation column, change monitoring for the Tennessee Eastman Challenge Problem, and online adaptive control in a wind tunnel test of unstable 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 with simulated and real 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.
Registration and Fees: $275 if attending ACC2000, $350 if not attending ACC2000. Students or Retired: $110. 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 2000 American Control Conference and other workshops.
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