CAREER: Control Design using Data-Driven Models: Exploiting Model Structure

职业：使用数据驱动模型进行控制设计：利用模型结构

• 批准号: 0134129
• 负责人: Robert Parker
• 金额: $ 37.5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-01-01 至 2007-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0134129&HistoricalAwards=false
• 关键词: CAREER; Control; Design; using; Data

ABSTRACTPI: Robert S. Parker Institution: University of PittsburghProposal Number: 0134129Industry is demanding higher and higher levels of performance from their processes and little capital investment is available for retrofitting or additional sensors. Thus processes must operate in highly nonlinear regimes, likely under linear control algorithms. This CAREER award addresses the integration of research and education with the goal of developing tools and students capable of solving problems in nonlinear model identification and control.Specifically, the research will focus on the use of control-relevant process models in nonlinear model-based optimal controller design. The modeling studies use third-order Volterra and Volterra-Laguerre representations, a subclass of nonlinear polynomial moving-average models. The selection of these model structures is motivated by (i) the ability to tailor input sequences to excite (or cancel) particular model dynamics; (ii) the opportunity to reduce the coefficient space and noise corruption with little to no decrease in model accuracy; (iii) the wide class of systems these model structures can approximate (e.g. polymerizers, distillation columns, bioreactors); and (iv) the utility of these structures in controller synthesis. Based on a polymerization reactor case study, third-order Volterra models will be identified using tailored input sequences, design to excite the linear, nonliear diagonal, or nonlinear off-diagonal terms. The resulting model will then be projected onto the orthonormal Laguerre basis to reduce coefficient dimension and noise effects on the output estimate. These models will then be used to synthesize a nonlinear model predictive control algorithm. The culmination of the project will be the integration of the identification and control results into a single algorithm for simultaneous identification and control.The educational component has two primary aims. The first is to help the student retain material from class to class, and to be able to employ material from a given class on an application example. Consistent with ABET 2000 criteria, these cross-curriculum problems will probe the students' mathematical skills. The type of problems examined serve to vertically integrate the curriculum with a recurring case study which draws on earlier courses, and therefore reinforces retention of material across classes. The integration of research and education is accomplished through the use of research results in the development of two new units for the Process Control Modules, case studies that examine data-driven model identification and nonlinear model predictive control at a level accessible to the undergraduate.

摘要PI：Robert S. Parker 机构：匹兹堡大学提案编号：0134129 行业对流程的性能要求越来越高，但可用于改造或添加传感器的资本投资很少。 因此，过程必须在高度非线性的状态下运行，可能是在线性控制算法下。 该职业奖致力于研究和教育的整合，旨在开发能够解决非线性模型识别和控制问题的工具和学生。具体来说，该研究将重点关注在基于非线性模型的优化中使用控制相关过程模型控制器设计。 建模研究使用三阶 Volterra 和 Volterra-Laguerre 表示法，这是非线性多项式移动平均模型的一个子类。 选择这些模型结构的动机是（i）能够定制输入序列以激发（或取消）特定模型动态； (ii) 有机会减少系数空间和噪声损坏，而模型精度几乎不降低； (iii) 这些模型结构可以模拟的广泛系统（例如聚合器、蒸馏塔、生物反应器）； (iv) 这些结构在控制器综合中的实用性。 基于聚合反应器案例研究，将使用定制的输入序列来识别三阶 Volterra 模型，设计用于激发线性、非线性对角线或非线性非对角线项。 然后将所得模型投影到正交拉盖尔基础上，以减少系数维数和噪声对输出估计的影响。 然后，这些模型将用于合成非线性模型预测控制算法。 该项目的最终目标是将识别和控制结果集成到单一算法中，以实现同步识别和控制。教育部分有两个主要目标。 第一个是帮助学生记住课堂上的材料，并能够在应用程序示例中使用给定课程的材料。 与 ABET 2000 标准一致，这些跨课程问题将考察学生的数学技能。 所检查的问题类型有助于将课程与借鉴早期课程的重复案例研究垂直整合，从而加强跨班级的材料保留。 研究和教育的整合是通过利用研究成果开发过程控制模块的两个新单元来实现的，这两个单元是在本科生可以理解的水平上检查数据驱动模型识别和非线性模型预测控制的案例研究。