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.

该基金将为4名美国顶尖的研究人员/教师和40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已成为先进控制的领先工业技术，以及研究生水平研究的学术学科。在工业应用方面，该技术的范围在过去30年中稳步增长，从其根源于过程控制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。在同一时期，MPC研究的范围也大大扩大，包括理论和计算两个部分。暑期学校的目的是将研究人员和实践者聚集在一起，就最新的MPC研究和应用提供最先进的概述和教程。由于其在实时应用中的应用，许多形式的MPC公式的计算方面一直是密集研究工作的重点，从而改进了MPC算法的在线和离线版本。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，采样数据系统的MPC， MPC的在线优化以及MPC的工业应用。MPC研究生阶段的暑期学校教育有几个显著的优势。一周的时间投入相对较少，这使得更多的美国研究生可以上这门课。通过与不同MPC主题的顶尖专家合作，研究生将接触到比单个讲师提供的单学期大学课程更多的专业知识。通过使用最先进的免费软件说明MPC概念，与会者可以将在具有挑战性的应用中实施MPC所需的计算工具带回家。最终目标是提高美国制造业和各行各业的技术水平。