CAREER: Robust Control of Large-Scale Constrained Nonlinear Systems

职业：大规模约束非线性系统的鲁棒控制

• 批准号: 9984104
• 负责人: Zhi (Alex) Zheng
• 金额: $ 20万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-10-01 至 2004-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9984104&HistoricalAwards=false
• 关键词: CAREER; Robust; Control; Large; Scale

Abstract - Zheng - 9984104Most chemical process are nonlinear. In the past, process control techniques based on linear models had been adequate largely due to relatively loose environmental regulations and industry's ability (e.g., via process over-design) to design processes to operate in regions where assumptions of linearity are valid. However, a combination of stiff global economic competition and strict environmental regulations is pushing process operational windows into regions where assumptions of linearity break down and where operating constraints (e.g., equipment, environmental, safety, etc.) must be satisfied. Furthermore, today's processes are large-scale in nature as they are becoming more integrated to improve capital productivity. Consequently, these trends have driven the process industry's need for improved control technologies, which can deal effectively with large-scale nonlinear processes subject to operating constraints. The rapid development in computational hardware and software has provided the opportunity for developing advanced control techniques, which optimize process profitability in real time.The goal of the planned research is to develop a novel nonlinear model predictive control (NLMPC) technology which addresses practical concerns (e.g., low on-line computational demand) and is theoretically sound (e.g., robust stability and performance guarantees) for control of large-scale constrained non-linear systems. The research involves a combination of theory, simulation studies, and industrial applications and, specifically, consists of the following projects: (1) Develop a computationally efficient MPC algorithm which can be implemented on nonlinear systems with tens of inputs and outputs.(2) (2) Prove that the closed-loop system is asymptotically stable in the presence of both model uncertainty and constraints(3) (3) Derive necessary and sufficient conditions for the robust stabilization of constrained nonlinear systems.(4) Develop a first-principles based model for a terephthalonitrile (TPN) plant, a large-scale chemical process, and carry out simulation studies on it.(5) Implement the algorithm on a methyl acetate reactive distillation column where it is known that linear controllers would fail in some operating regions.(6) Develop educational software based on the theoretical results and disseminate via CACHE.Several educational innovations are planned:(1) Develop interactive-learning modules, which allow the instructor to focus more on the "discovery-based" teaching method than the traditional "I-talk-you-listen" teaching method. The modules would transform the students from passive listeners to active participants.(2) Develop open-ended problems which require not only engineering know-how, but also other aspects of the decision-making process.(3) Develop projects to get selected undergraduate students more involved with frontier engineering research.(4) Have the PI's students take industrial assignments.

摘要- Zheng -9984104大多数化学过程是非线性的。 在过去，基于线性模型的过程控制技术已经足够了，这主要是由于相对宽松的环境法规和工业的能力（例如，通过工艺过度设计）来设计工艺以在线性假设有效的区域中操作。 然而，激烈的全球经济竞争和严格的环境法规的组合正在将过程操作窗口推入线性假设被打破并且操作约束（例如，设备、环境、安全等）必须得到满足。 此外，今天的过程在本质上是大规模的，因为它们变得更加一体化以提高资本生产率。 因此，这些趋势推动了过程工业对改进控制技术的需求，这些技术可以有效地处理受操作约束的大规模非线性过程。 计算硬件和软件的快速发展为开发先进的控制技术提供了机会，这些技术可以在真实的时间内优化过程的盈利能力。计划研究的目标是开发一种新的非线性模型预测控制（NLMPC）技术，它解决了实际问题（例如，低的在线计算需求）并且在理论上是合理的（例如，鲁棒稳定性和性能保证）的大规模约束非线性系统的控制。 本研究集理论研究、仿真研究和工业应用于一体，具体包括以下几个方面：（1）开发一种计算效率高的MPC算法，该算法可以应用于具有数十个输入和输出的非线性系统。(2)（2）证明了在存在模型不确定性和约束的情况下闭环系统是渐近稳定的（3）（3）推导了约束非线性系统鲁棒镇定的充分必要条件。(4)建立了一个基于第一性原理的大规模化工过程--乙腈（TPN）装置的模型，并对其进行了模拟研究。(5)在乙酸甲酯反应精馏塔上实现该算法，已知线性控制器在某些操作区域会失效。(6)在此基础上开发教学软件，通过CACHE进行传播。计划进行以下几项教学创新：（1）开发交互式学习模块，使教师能够更专注于“发现式”教学方法，而不是传统的“我说你听”教学方法。 这些单元将使学生从被动的倾听者转变为积极的参与者。(2)开发开放式的问题，不仅需要工程知识，而且还需要决策过程的其他方面。(3)开发项目，让选定的本科生更多地参与前沿工程研究。(4)让私家侦探的学生去做工业作业。