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

系统辨识的典型变量分析(CVA)方法将被用于工业过程的辨识和自适应控制。目前已有的系统辨识和自适应控制算法不能完全可靠地在微型机上实时自动实现。在CVA方法中，算法计算稳定，数值准确，可以在廉价的微型计算机上实现。CVA方法自动确定过程的动态顺序和结构。提出的第一阶段研究是为了证明CVA方法在难以辨识的工业过程中的可行性，并将CVA方法扩展到非线性和时变系统的辨识。加州大学圣巴巴拉分校的计算机模拟软件和实验工艺设施将用于评估可行性。

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.