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.
