NSF Summer School on Model Predictive Control

NSF 模型预测控制暑期学校

• 批准号: 1714232
• 负责人: James Rawlings
• 金额: $ 3.35万
• 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: 2017
• 资助国家: 美国
• 起止时间: 2017-05-15 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1714232&HistoricalAwards=false
• 关键词: NSF; Summer; Model; Predictive; Control

This grant is to provide travel support for four leading US-based researchers/instructors and forty US-based graduate students to attend the summer school on Model Predictive Control (MPC). MPC has emerged as both a leading industrial technology for advanced control, as well as an academic discipline for graduate level research. On the industrial applications side, the scope of the technology has increased steadily during the last 30 years from its roots in process control to encompassing many industrial sectors including: aerospace, energy systems, automotive, robotic systems, etc. During the same time period, the scope of MPC research has also widened considerably and includes both theoretical and computational components. The purpose of the summer school is to bring researchers and practitioners together to provide state-of-the-art overviews and tutorials on the latest MPC research and applications. Because of its use in real time applications, the computational aspects of the many forms of MPC formulations have been the focus of intense research efforts resulting in improvements in both online and offline versions of MPC algorithms. The topics that will be covered in the summer school include classical MPC (regulation, estimation, and disturbance models), robust MPC, stochastic MPC, economic MPC, MPC for sampled-data systems, online optimization for MPC, and industrial applications of MPC. The summer school approach to graduate-level education in MPC offers several significant advantages. The one-week time investment is relatively modest, making the class available to a much larger numbers of US-based graduate students. By engaging leading experts in the different MPC topics, the graduate students will be exposed to more expertise than is available in a single semester university course offered by a single instructor. By illustrating the MPC concepts with state-of-the-art and freely available software, the attendees can take home with them the computational tools required for implementation of MPC on challenging applications. The ultimate objective is to raise the technology standard of American manufacturing and operations in a variety of industries.

该补助金将为四名美国领先的研究人员/教师和四十名美国研究生提供旅行支持，以参加模型预测控制（MPC）暑期学校。MPC已经成为先进控制的领先工业技术，以及研究生水平研究的学科。在工业应用方面，该技术的范围在过去30年中稳步增长，从过程控制的根源到涵盖许多工业部门，包括：航空航天，能源系统，汽车，机器人系统等。 暑期学校的目的是将研究人员和从业人员聚集在一起，提供最新MPC研究和应用的最新概述和教程。由于其在真实的时间应用中的使用，许多形式的MPC公式化的计算方面已经成为激烈的研究工作的焦点，导致MPC算法的在线和离线版本的改进。暑期学校将涵盖的主题包括经典MPC（调节，估计和干扰模型），鲁棒MPC，随机MPC，经济MPC，采样数据系统MPC，MPC在线优化以及MPC的工业应用。MPC研究生教育的暑期学校方法提供了几个显着的优势。为期一周的时间投资相对温和，使类提供给更多的美国研究生。通过让顶尖专家参与不同的MPC主题，研究生将接触到更多的专业知识，而不是在一个单一的讲师提供的一个学期的大学课程。通过使用最先进的免费软件来说明MPC概念，与会者可以带回家在具有挑战性的应用中实施MPC所需的计算工具。最终目标是提高美国制造业的技术标准和各行业的运营水平。