On-Line Multivariable System Identification and Adaptive    Control of Industrial Processes
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{'SignBlockName': 'Ritchie B. Coryell', 'PO_EMAI': None, 'PO_PHON': None}
The canonical variate analysis (CVA) method of system identification            will be evaluated for identification and adaptive control of                    industrial processes.  Currently available algorithms for system                identification and adaptive control are not completely reliable for             automatic implementation on microcomputers in real time.  In the CVA            approach, the algorithms are computationally stable and numerically             accurate and can be implemented on inexpensive micro-computers.  The            CVA method automatically determines the dynamical state order and               structure of the process.  The proposed Phase I research is to show             the feasibility of the CVA method on industrial processes that are              difficult to identify, and to extended CVA to the identification of             nonlinear and time varying systems.  The computer simulation software           and experimental process facilities of the University of California at          Santa Barbara will be used for evaluation of feasibility.
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{'Code': '5371', 'Text': 'SBIR Phase I'}
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