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.

这笔赠款将为四名美国顶尖研究人员/讲师和四十名美国研究生提供参加模型预测控制 (MPC) 暑期学校的旅费支持。 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科。在工业应用方面，该技术的范围在过去 30 年中稳步扩大，从最初的过程控制扩展到涵盖许多工业领域，包括：航空航天、能源系统、汽车、机器人系统等。在同一时期，MPC 研究范围也大大拓宽，包括理论和计算部分。 暑期学校的目的是将研究人员和从业者聚集在一起，提供有关最新 MPC 研究和应用的最先进的概述和教程。由于其在实时应用中的使用，多种形式的 MPC 公式的计算方面一直是大量研究工作的重点，从而导致 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、估计和扰动模型）、鲁棒MPC、随机MPC、经济MPC、采样数据系统的MPC、MPC在线优化以及MPC的工业应用。 MPC 研究生水平教育的暑期学校方法具有几个显着的优势。一周的时间投入相对适中，使得该课程可供更多美国研究生参加。通过聘请不同 MPC 主题的领先专家，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，与会者可以带回家在具有挑战性的应用程序中实施 MPC 所需的计算工具。最终目标是提高美国制造和各行业运营的技术标准。