开始日期: 2023-12-16

　　课时安排: 7周在线小组科研学习+5周不限时论文指导学习

　　适合人群

　　适合年级 (Grade): 大学生及以上

　　适合专业 (Major): 机械工程、自动化与控制理论、机器人学、人工智能、智能制造等专业或者希望修读相关专业的学生

　　学生需要具备微积分、信号与系统基础，并会使用MATLAB编程语言

　　导师介绍

　　Harrison

　　牛津大学 University of Oxford终身教授

　　导师Harrison现任牛津大学工程科学系教授，兼由14名博士生和博士后组成的研究小组组长、哈里斯曼彻斯特学院讲师;曾任麻省理工学院机械工程系客座研究员。担任Science, Nature Communications, PLoS journals, ACS journals, Royal Society Interface, IEEE Transactions等十多种知名学术期刊审稿人，UKRI, NSF, Swiss National Science Foundation等机构奖金评审人。开源Chi.Bio实验自动化平台的创始人和发明人，约100个学术和工业实验室使用(https://chi.bio/)。OxVent(由来自牛津大学和伦敦国王学院的工程师和医生组成的多学科团队)首席技术官兼电子团队负责人。曾在德国知名研究机构DESY担任工程师，致力于下一代粒子加速技术。曾在美国航天局艾姆斯研究中心担任研究科学家，从事南极环境项目以及火星和木卫二的行星任务。曾获得Philip Leverhulme奖(英国国家奖每三年颁发给五名工程师，表彰那些工作已经获得国际认可的早期职业研究人员)、EPSRC新视野奖(开发控制细胞进化的机器人显微镜新技术)等多个奖项。Pro.Harrison is currently a professor of the College of Engineering Sciences, Oxford University, and the leader of the research team composed of 14 doctoral students and postdoctoral students, as well as a lecturer of Harris Manchester College; Previously served as a visiting researcher in the Department of Mechanical Engineering at Massachusetts Institute of Technology. Served as a reviewer for over ten well-known academic journals such as Science, Nature Communications, PLoS journals, ACS journals, Royal Society Interface, IEEE Transactions, and as a prize reviewer for institutions such as UKRI, NSF, and Swiss National Science Foundation. Founder and inventor of the open-source Chi Bio experimental automation platform, used by approximately 100 academic and industrial laboratories( https://chi.bio/ )。 Chief Technical Officer and Electronic Team Leader of OxVent, a multidisciplinary team of engineers and doctors from Oxford University and King's College London. Formerly worked as an engineer at the renowned German research institution DESY, dedicated to the next generation of particle acceleration technology. He once worked as a research scientist in the Ames Research Center of NASA, engaged in the Antarctic environment project and the planetary missions of Mars and Europa. He has won multiple awards, including the Philip Leverhulme Award (which is awarded every three years to five engineers by the British National Award to recognize early career researchers whose work has been internationally recognized), the EPSRC New Horizons Award (which develops new technologies for robotic microscopes that control cell evolution), and more.

　　任职学校

　　牛津大学(University of Oxford)，是一所位于英国牛津的世界顶尖公立研究型大学。其与剑桥大学并称为牛剑，是罗素大学集团成员，被誉为“金三角名校”和“G5超级精英大学”。牛津大学是英语世界中最古老的大学，也是世界上现存第二古老的高等教育机构。该校涌现了一批引领时代的科学巨匠，培养了大量开创纪元的艺术大师、国家元首，其中包括28位英国首相及数十位世界各国元首、政商界领袖。牛津大学在数学、物理、医学、法学、商学等多个领域拥有崇高的学术地位及广泛的影响力，被公认为是当今世界最顶尖的高等教育机构之一。截止至2019年3月，牛津大学的校友、教授及研究人员中，共有72位诺贝尔奖得主(世界第九)、3位菲尔兹奖得主(世界第二十)、6位图灵奖得主(世界第九)。牛津大学名列2023QS英国最佳大学排名第2名、2023QS世界大学排名第4名、2022QS英国最佳大学排名第1名、2022QS世界大学排名第2名、2022软科世界大学学术排名第7位。

　　项目背景

　　当前，云计算、物联网和大数据等新兴信息通信技术快速发展，为机器人的高端制造应用提供了新的思路和发展契机。数字化、网络化、智能化融合创新引领未来工业发展已取得广泛共识。我国在《十四五规划和2035年远景目标纲要》也明确提出要“加快推进制造强国、质量强国建设。深入实施智能制造和绿色制造工程。发展服务型制造新模式。推动制造业高端化智能化绿色化”。而传统的人工以及专机加工。加工质量一致性差、效率低且只能实现小批量生产，已无法满足智能制造需求。区别于传统工业机器人简单、重复性劳动，高端制造机器人是更信息化、网络化、智能化、柔性化的机器人。在智能工厂中，机器人的作业环境日益非结构化。生产的动态性对机器人的环境感知与控制技术提出了越来越高的要求。在未来的智能工厂中，机器人也将得到更加广泛的应用，其控制性能不仅影响到最终的加工效果，还影响到加工过程的安全。高精、高效、高柔性、高稳定性的控制策略能为工件的安全制造提供坚实的保障。Currently, emerging information and communication technologies such as cloud computing, the Internet of Things, and big data are rapidly developing, providing new ideas and development opportunities for high-end manufacturing applications of robots. The integration and innovation of digitization, networking, and intelligence have achieved broad consensus in leading future industrial development. In the 14th Five Year Plan and the 2035 Long Range Goals Outline, China also explicitly proposes to "accelerate the construction of a strong manufacturing and quality country, deepen the implementation of intelligent manufacturing and green manufacturing projects, develop new service-oriented manufacturing models, and promote the high-end, intelligent, and green manufacturing industry. And traditional manual and specialized machine processing. The consistency of processing quality is poor, the efficiency is low, and only small batch production can be achieved, which can no longer meet the needs of intelligent manufacturing. Unlike traditional industrial robots with simple and repetitive labor, high-end manufacturing robots are more information-based, networked, intelligent, and flexible robots. In intelligent factories, the working environment of robots is becoming increasingly unstructured. The dynamic nature of production places increasing demands on the environmental perception and control technology of robots. In future intelligent factories, robots will also be more widely used, and their control performance not only affects the final processing effect, but also affects the safety of the processing process. The control strategy of high precision, efficiency, flexibility, and stability can provide a solid guarantee for the safe manufacturing of workpieces.

　　项目介绍

　　本项目主要研究自动化控制和数字技术的原理。通过学习，学生将能够了解离散控制系统相关知识，掌握机器人系统等数字技术中自动控制器的设计和分析方法。具体内容涵盖控制方法论、控制系统的数学分析、控制设计方法、离散控制系统及其前沿应用。为以后学生能够在机电一体化、机器人等智能制造领域深入研究打下坚实的基础。This project mainly studies the principles of automation control and digital technology. Through learning, students will be able to understand knowledge related to discrete control systems and master the design and analysis methods of automatic controllers in digital technologies such as robot systems. The specific contents include control methodology, mathematical analysis of control system, control design method, discrete control system and its cutting-edge applications. To lay a solid foundation for future students to conduct in-depth research in intelligent manufacturing fields such as mechatronics and robotics.

　　项目大纲

　　机器人和控制导论：使用拉普拉斯变换对时域和频域中的反馈控制器进行建模，以及控制性能和稳定性分析 Introduction to robotics and control

　　离散控制系统简介：用于分析和设计机器人和实验系统离散控制器的财产和z变换的使用 Introduction to discrete control systems

　　离散控制系统的系统规范和性能：用于机器人动力学分析的状态空间形式的离散控制 System specification and performance of discrete control systems

　　控制系统的应用：在机器人和其他数字技术中的应用示例 pplication of Control Systems

　　学术研讨1：教授与各组学生探讨并评估个性化研究课题可行性，帮助学生明晰后续科研思路 Final Project Preparation Session I

　　学术研讨2：教授将根据各组进度进行个性化指导，确保学生优质的终期课题产出 Final Project Preparation Session II

　　项目成果展示 Final Presentation

　　论文指导 Project Deliverables Tutoring

　　项目收获

　　7周在线小组科研学习+5周不限时论文指导学习 共125课时

　　项目报告

　　优秀学员获主导师Reference Letter

　　EI/CPCI/Scopus/ProQuest/Crossref/EBSCO或同等级别索引国际会议全文投递与发表指导(可用于申请)

　　结业证书

　　成绩单