GOALI: Performance Monitoring Principles for Nonlinear and Linear Model Predictive Control

GOALI：非线性和线性模型预测控制的性能监控原理

• 批准号: 1159088
• 负责人: James Rawlings
• 金额: $ 35万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1159088&HistoricalAwards=false
• 关键词: GOALI; Performance; Monitoring; Principles; Nonlinear

Current estimates of the economic impact in the process industries of model predictive control (MPC) are in the billions of dollars per year. But the process industries currently have no accepted methods tailored to the problem of assessing the performance of the thousands of loops currently running under model predictive control.The goal of this project is to establish principles for performing such assessment for installed MPC technology. Establishing these principles will enable the development of software tools to automatically quantify the key performance index (KPI) for each MPC control system under study. Rank ordering of the different loops' KPIs enables comparison, prioritization, and pinpoints those systems requiring further attention. Comparisons of each loop's actual KPI to three other calculated KPIs allow further diagnosis including: impact of process constraints, poor process models, inappropriate noise filtering, and large disturbances. Longitudinal analysis allows the user to select appropriate time periods for benchmarking performance and spotting seasonal trends and variations. Feedback from this assessment analysis to the control system technology vendors creates incentives and points to promising areas for further improving the control technology.There is little established theory for assessing performance of MPC based on nonlinear models, so this part of the research ventures into a completely new area. The industrial partner in this GOALI proposal, ExxonMobil, will provide the relevant industrial data sets and supervise the supported Ph.D. student for two summers at ExxonMobil.Intellectual Merit:The problems to be addressed in this research are fundamental and far reaching. The required theory draws upon: optimization; probability, statistics and statistical inference; random variables and Markov chains; nonlinear control theory; numerical simulation and time-averaging of numerical simulations to determine statistical properties (Monte Carlo methods). The theory draws from current techniques developed in these fundamental disciplines and will expose new problems that cannot be addressed with the currenttools provided by these fields.Broader Impact:Advance technology has been acquired and implemented, but users have no clearinghouse of objective data and information telling them where these systems are working well, and perhaps more importantly, where they are not working well. Developing new systems to provide this information will enable: (i) users to systematically manage larger installations, (ii) identify and prioritize the troublesome parts of their systems, and (iii) spur development of new control theory tailored to addressing the identified, unmet practical needs. The people responsible for implementing and maintaining advanced technology require management tools to assess the technology. This is true of almost any advanced technology.The systems theory and algorithms developed in this research are completely general and can be applied to any class of dynamic manufacturing processes for which a reasonable predictive model can be developed.

目前对模型预测控制（MPC）在过程工业中的经济影响的估计是每年数十亿美元。但是，过程工业目前还没有公认的方法来评估目前运行在模型预测控制下的数千个回路的性能。本项目的目标是建立对已安装的MPC技术进行此类评估的原则。建立这些原则将使软件工具的开发，自动量化的关键性能指标（KPI）的每个MPC控制系统的研究。不同回路的KPI的等级排序可以进行比较、优先级排序，并确定需要进一步关注的系统。每个回路的实际KPI与其他三个计算的KPI的比较允许进一步诊断，包括：过程约束的影响，不良的过程模型，不适当的噪声滤波和大的干扰。纵向分析允许用户选择适当的时间段，用于基准性能和发现季节性趋势和变化。从评估分析到控制系统技术供应商的反馈创造了激励，并指出了进一步改进控制技术的有前途的领域。基于非线性模型评估MPC性能的既定理论很少，因此这部分研究进入了一个全新的领域。该GOALI提案的工业合作伙伴埃克森美孚将提供相关的工业数据集，并监督支持的博士学位。在埃克森美孚公司学习了两个夏天。智力优势：本研究中要解决的问题是基础性的，影响深远。所需的理论借鉴：优化;概率，统计和统计推断;随机变量和马尔可夫链;非线性控制理论;数值模拟和数值模拟的时间平均，以确定统计特性（蒙特卡洛方法）。该理论借鉴了在这些基础学科中发展起来的现有技术，并将揭示这些领域提供的现有工具无法解决的新问题。更广泛的影响：先进的技术已经获得并实施，但用户没有客观数据和信息的交换中心，告诉他们这些系统在哪里工作得很好，也许更重要的是，他们在哪里工作得不好。开发新的系统来提供这些信息将使：（i）用户能够系统地管理更大的安装，（ii）识别和优先考虑他们系统中的麻烦部分，以及（iii）刺激新的控制理论的发展，以解决已识别的，未满足的实际需求。负责实施和维护先进技术的人员需要管理工具来评估技术。这是真实的几乎任何先进的technology.The系统的理论和算法在这项研究中开发的是完全通用的，可以应用于任何类的动态制造过程，为一个合理的预测模型可以开发。