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

为了在瞬息万变的经济环境中保持竞争力， 美国生产设施的高效运营， 在线控制和优化是改善的关键因素 盈利 人们普遍认为，在许多工业工厂中， 存在许多关键的控制回路，其中传统的单个 循环控制不足，经济效益显著 先进控制的激励措施。 在第一阶段的研究中，在线多变量系统的可行性 用典型变量辨识工业过程 CVA分析（CVA）是在计算机模拟的数据上进行的， 圣巴巴拉的加州大学的实验过程 （UCSB）。 第二阶段，在线识别和控制的集成 设计将是重点。 CVA方法与在线集成 控制设计将在几种类型的 方法，并与现有方法进行比较。 而且有 详细发展的方法扩展到时间变化 将提供系统。 系统的性能 辨识和控制设计方法将实验 在中试或全规模工业过程中进行评估。

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.