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

过程控制系统使工厂能够满足目标， 保持产品质量和最大限度地减少能源 这反过来又使工厂的盈利能力最大化 和回报率或投资。 因为它们能让 响应变化，过程控制系统发挥着至关重要的作用， 在一个不确定的环境中， 经济气候。 设计先进的 控制系统是建立一个模型，代表 植物的动态。 大多数植物都太复杂了， 对基本过程了解太少， 使用第一原理建模。 最合理的方法 获得可靠的动态模型是从数据生成， 精心设计的实验。 在石油化工和炼油领域， 工业，从实验中获得的黑箱模型是由 这是获得动态模型的最常用方法。 的 从数据获得动态模型的任务被称为 系统识别 控制相关系统辨识的动机是 希望提高先进的实用性和接受度 过程工业中的识别概念。 需要 考虑到闭环控制目标和 用户的技能水平。 控制相关识别 因此，为先进的移民提供了机会， 识别概念，非专家用户和 开发计算机辅助设计工具， 有学士学位的工程师都能用水平 教育 本项目的主要目的是调查 控制相关标识的主题。 的基础 控制相关方法是 设计变量的识别问题和 控制问题的性能目标。 为了达到这个目的， 频域中的偏差和方差表达式， 控制问题的线性表示 使用分数变换。 这一分析导致了 用于预滤波器设计系统过程，其基本上 提高预测误差算法的性能 而不需要大量提高技能， user. 此外，使用结构化奇异值导致 涉及用于识别的模型的模型验证过程， 提供了一个清晰的图片模型的限制，以实现 控制性能 通过提供理论基础， 改进的计算机辅助设计工具，这些结果应该 使先进的识别概念的应用， 工程师在此过程中更常见的做法是， 行业

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.