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 将被评估用于识别和自适应控制 工业过程。 系统的当前可用算法 识别和自适应控制并不完全可靠， 真实的在微机上自动实现。 在CVA 方法，算法是计算稳定和数值 精确，可以在廉价的微型计算机上实现。 的 CVA方法自动确定动态阶次， 过程的结构。 拟议的第一阶段研究将显示 CVA方法在工业过程中的可行性， 难以识别，并将CVA扩展到识别 非线性时变系统。 计算机模拟软件 加州大学的实验工艺设施， 圣巴巴拉将用于可行性评价。

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.