Collaborative Research: CISE-MSI: RCBP-RF: CPS: Develop Scalable and Reliable Deep Learning-driven Embedded Control Applied in Renewable Energy Integration

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。最近深度学习主要在图像处理，语言处理领域取得了成功。然而，在实时控制领域，深度学习刚刚开始挑战比例-积分-微分控制器在工业应用中的主导地位，例如，实时控制用于可再生能源集成的功率转换器。训练难度大、在嵌入式设备上实现困难等问题，制约着深度学习在嵌入式控制环境中的发展和实现。 为了克服这些困难，该项目旨在开发新型可扩展训练算法和新型深度神经网络控制器架构，以满足嵌入式控制设置的严格要求。该跨学科项目将开发可扩展和可靠的深度学习驱动的实时功率转换器嵌入式控制，以整合可再生能源，如太阳能。 具体而言，该项目旨在（a）开发可扩展的，并行的，快速的训练算法，用于高采样频率和长时间持续的轨迹学习，使用高性能计算或云平台，将训练时间从几天，甚至几周减少到几个小时，（B）开发可以在嵌入式设备中实现的新型深度神经网络架构，例如，数字信号处理器/现场可编程门阵列，而不影响神经网络的泛化能力和额外的计算能力和存储需求。该项目将建立和加强两个少数民族服务机构之间的跨学科和机构间的合作：得克萨斯州A M大学金斯维尔和北卡罗来纳州A T州立大学。该项目将吸引，留住和教育更多的少数民族，特别是西班牙裔，非洲裔美国人和女学生参加博士学位。程序.所开发的嵌入式控制的新训练算法和新架构可以扩展到其他领域，例如，生物信息学、图像、机器人技术等，开发的技术将为可再生资源的电网整合带来深度学习驱动的智能控制，并有助于解决美国将更多可再生能源整合到电网中的迫切需求。研究存储库（数据、代码、模拟等）从该项目产生的信息将被存放在德克萨斯州A M大学金斯维尔分校和北卡罗来纳州A T州立大学的数字存储库中，并确保更广泛的计算机科学和可持续能源研究社区可以按照国家科学基金会的规定长期访问至少三年。 公共使用的数据文件可通过两个园区的数字储存库直接通过项目网站（https://sites.google.com/view/dr-xingang-fu/home和https://sites.google.com/view/letuqingge/home）查阅。限制使用的数据文件是在删除可能会严重损害数据分析潜力的潜在识别信息后分发的。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Recently deep learning has succeeded mainly in image processing, language processing fields. However, in the real-time control field, deep learning has just started to challenge the dominant role of proportional-integral-derivative controllers in industrial applications, e.g., real-time control of power converters for renewable energy integration. Many urgent problems including training difficulty, the implementation challenges on embedded devices, are curbing deep learning from the development and implementation in embedded control settings. To overcome these difficulties, this project aims to develop novel scalable training algorithms and novel deep neural network controller architectures to fit the strict requirement of embedded control settings.The interdisciplinary project will develop scalable and reliable deep learning-driven embedded control of power converters in real-time for integrating renewable energy such as solar power. Specifically, this project aims (a) to develop scalable, parallel, fast training algorithms for high sampling frequency, and long-time duration trajectory learning using an high performance computing or cloud platform that will significantly reduce training time from several days, even weeks to several hours, (b) to develop novel deep neural network architectures that can be implemented in embedded devices, e.g., Digital Signal Processors / Field-programmable Gate Arrays without compromising the neural network generalizability and extra computing power and storage requirements.The project will build and enhance interdisciplinary and inter-institution collaborations between two Minority Serving Institutions: Texas A&M University-Kingsville and North Carolina A&T State University. The project will attract, retain, and educate more minorities particularly Hispanic, African-American, and female students to attend Ph.D. programs. The developed new training algorithm and new architectures for embedded control can be extended to other fields, e.g., bioinformatics, image, robotics, etc. The developed technologies will result in deep learning-driven intelligent control for grid integration of renewable resources and help solve the urgent need to integrate more renewable energy into the power grid in the United States. The research repository (data, code, simulations, etc.) generated from the project will be deposited with the digital repository at Texas A&M University-Kingsville and North Carolina A&T State University and ensure that the broader computer science and sustainable energy research community have long-term access for a minimum of three years prescribed by the National Science Foundation. Public-use data files can be accessed directly through the project websites ( https://sites.google.com/view/dr-xingang-fu/home and https://sites.google.com/view/letuqingge/home ) via the digital repository on both campuses. Restricted-use data files are distributed after removing potentially identifying information that would significantly impair the analytic potential of the data.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.