On-line Multivariable System Identification and Adaptive Control of Industrial Processes

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To remain competitive in a rapidly changing economic environment, more efficient operation of production facilities in the United States via on-line control and optimization is a key factor in improving profitability. It is widely recognized that in many industrial plants there are a number of critical control loops where conventional single loop control is inadequate and there are significant economic incentives for advanced control. In the Phase I study, the feasibility of on-line multivariable system identification of industrial processes using canonical variate analysis (CVA) was demonstrated on data from computer simulations and experimental processes at the University of California, Santa Barbara (UCSB). The Phase II, the integration of on-line identification and control design will be a major focus. The CVA method integrated with on-line control design will be extensively demonstrated on several types of processes, and compared with existing methods. Furthermore, a detailed development of an extension of the approach to time varying systems will be provided. The performance of the system identification and control design methods will be experimentally evaluated on a pilot- or full- scale industrial process.