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

系统辨识的典型变量分析（CVA）方法将被评估用于工业过程的辨识和自适应控制。 目前可用的系统辨识和自适应控制算法对于在微型计算机上实时自动实现并不完全可靠。 在 CVA 方法中，算法计算稳定且数值准确，并且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。 拟议的第一阶段研究旨在展示CVA方法在难以识别的工业过程上的可行性，并将CVA扩展到非线性和时变系统的识别。 加州大学圣塔芭芭拉分校的计算机模拟软件和实验过程设施将用于可行性评估。

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.