Research Initiation Award: System Identification for ProcessControl: Control Relevant Identification

研究启动奖：过程控制系统识别：控制相关识别

• 批准号: 9110528
• 负责人: Daniel Rivera
• 金额: $ 6.32万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-09-01 至 1994-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9110528&HistoricalAwards=false
• 关键词: Research; Initiation; Award; System; Identification

Process control systems enable plants to meet objectives such as maintaining product quality and minimizing energy consumption, which in turn maximize the plant's profitability and rate-of-return or investment. Because they enable a rapid response to change, process control systems play a vital part in a company's ability to remain profitable in an uncertain economic climate. The first step in the design of an advanced control system is to build a model that represents the dynamics of the plant. Most plants are too complex or the underlying processes too poorly understood to be adequately modeled using first principles. The most reasonable way to obtain reliable dynamic models is from data generated through well designed experiments. In the petrochemical and refining industries, black-box models obtained from experiments are by far the most common means of obtaining dynamic models. The task of obtaining dynamic models from data is referred to as system identification. Control-relevant system identification is motivated by the desire to increase the utility and acceptance of advanced identification concepts in the process industries. It takes into consideration the closed-loop control objectives and the skill level of the user. Control-relevant identification therefore offers the opportunity for the migration of advanced identification concepts to nonexpert users and for the development of computer-aided design tools for identification that can be used by practicing engineers with a B.S. level of education. The main objective of this project is to investigate the subject of control-relevant identification. The basis for the control-relevant approach is the relationship between the design variables of the identification problem and the performance objective of the control problem. To obtain this, bias and variance expressions in the frequency domain and representations of the control problem in terms of linear fractional transformations are used. This analysis leads to a systematic procedure for prefilter design that substantially improves the performance of prediction-error algorithms without demanding substantial increases in skill from the user. In addition, use of the Structured Singular Value leads to a model validation procedure for identified models that provides a clear picture of model limitations to achievable control performance. By providing the theoretical basis for improved computer-aided design tools, these results should make the application of advanced identification concepts a more commonplace practice by engineers in the process industries.

过程控制系统使工厂能够满足诸如保持产品质量和最小化能源消耗等目标，从而最大限度地提高工厂的盈利能力和投资回报率。由于过程控制系统能够对变化做出快速反应，因此在不确定的经济环境中，过程控制系统对公司保持盈利的能力起着至关重要的作用。设计先进控制系统的第一步是建立一个代表工厂动态的模型。大多数植物太过复杂，或者对其潜在的过程了解得太少，无法用第一性原理充分地建模。获得可靠的动态模型的最合理方法是通过精心设计的实验产生的数据。在石油化工和炼油工业中，从实验中得到的黑箱模型是迄今为止获得动态模型的最常用手段。从数据中获取动态模型的任务称为系统识别。控制相关系统识别的动机是希望在过程工业中增加先进识别概念的效用和接受度。它考虑了闭环控制目标和用户的技术水平。因此，控制相关识别提供了向非专业用户迁移高级识别概念的机会，并为具有学士学位教育水平的实践工程师提供了用于识别的计算机辅助设计工具的开发。​这个项目的主要目的是调查控制相关识别的主题。控制相关方法的基础是识别问题的设计变量与控制问题的性能目标之间的关系。为了得到这一点，在频域中使用偏差和方差表达式以及用线性分数变换表示控制问题。这种分析导致预滤波器设计的系统程序，大大提高了预测误差算法的性能，而不需要用户大幅度提高技能。此外，结构化奇异值的使用导致了对已识别模型的模型验证过程，该过程为可实现的控制性能提供了模型限制的清晰图像。通过为改进计算机辅助设计工具提供理论基础，这些结果应该使过程工业中工程师更普遍地应用先进的识别概念。