CAREER: Data-Driven Control of High-Rate Dynamic Systems

职业：高速动态系统的数据驱动控制

• 批准号: 2237696
• 负责人: Austin Downey
• 金额: $ 55.19万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-02-01 至 2028-01-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237696&HistoricalAwards=false
• 关键词: CAREER; Data; Driven; Control; Rate

This NSF CPS CAREER project studies the hardware/software co-design of sub-millisecond machine learning control for high-rate dynamic systems with non-stationary inputs that change the system’s state (i.e., damage). Such systems include combustion processes in jet engines, vehicle structures during crashes, and active blast mitigation structures. The novelty of the approach taken in this project is to co-design the control systems with the computing hardware they will run on to constrain system latency to within 1 millisecond. The developed solutions will be able to learn a system’s dynamics at the data rates required by high-rate dynamic systems. Machine learning models will learn the dynamics of the non-linear system online, which will then be used to model the dynamics of the system to the appropriate prediction horizon. The project is developing an automated programming methodology that enables the deployment of these real-time controllers onto compact and power-efficient computing devices. It follows that this research will impact society and the mission of the NSF by enabling a better understanding of dynamic systems operating in high-rate environments while enabling intelligent decision-making capabilities at speeds never before reached. The project will leverage existing and valuable resources at the University of South Carolina to involve several high school and undergraduate students in the project; with emphasis on providing research experiences to underrepresented, first-generation, and low-income students. This project will also train Ph.D. students in real-time machine learning and control.More specifically, this research is addressing the fundamental question of how programmable hardware can be used to enable machine learning and control for systems that demand ultra-low latency. This is being done by formulating a framework for real-time machine learning control that co-designs hardware and software and provides a path to deployment on field programmable gate arrays (FPGAs). The project is: 1) Training a novel long short-term memory (LSTM) model on-chip with a custom online trainer that maps sensor signal and actuator input for a high-rate system to system state in real-time. 2) Developing approaches to share FPGA signal processing and memory resource for the parallel utilization of multiple LSTM forward-pass cores while maintaining deterministic timing. 3) Studying trade-offs between accuracy, performance, and resource requirements for real-time machine learning control at the microsecond timescale. Validation of the developed approach is being performed using a hardware-in-the-loop testing methodology with fast-acting actuators to control the outer mold line of a structural panel in simulated hypersonic flight.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.

该NSF CPS职业项目研究了高利率动态系统的子毫秒机器学习控制的硬件/软件共同设计，其非平稳输入会改变系统状态（即损坏）。这样的系统包括喷气发动机中的组合过程，撞车过程中的车辆结构以及主动的爆炸结构。该项目采用的方法的新颖性是将控制系统与他们将运行的计算硬件共同设计，以将系统延迟限制为1毫秒以内。开发的解决方案将能够按照高速动态系统所需的数据速率学习系统的动态。机器学习模型将在线学习非线性系统的动态，然后将其用于建模系统的动力学到适当的预测范围。该项目正在开发一种自动化编程方法，该方法可以将这些实时控制器部署到紧凑型和发电的计算设备上。因此，这项研究将通过更好地了解在高速环境中运行的动态系统，同时以前所未有的速度实现智能决策能力，从而影响社会和NSF的使命。该项目将利用南卡罗来纳大学的现有和宝贵的资源来参与该项目的几名高中和本科生。重点是为代表性不足，第一代和低收入学生提供研究经验。该项目还将培训博士学位。具体来说，这项研究是针对实时机器学习和控制的学生，该研究正在解决如何使用可编程硬件的基本问题来启用机器学习和控制需要超低潜伏期的系统。这是通过制定实时机器学习控制框架来共同设计硬件和软件的框架来完成的，并为现场可编程门阵列（FPGAS）提供了部署的路径。该项目是：1）使用自定义的在线培训师在片上训练新型的长期短期内存（LSTM）模型，该模型将传感器信号和执行器输入映射到高速系统中，以实时系统状态。 2）开发用于共享FPGA信号处理和内存资源的方法，以在维护确定性时机的同时并行利用多个LSTM前向核心。 3）研究在微秒时间范围内实时机器学习控制的准确性，性能和资源要求之间的权衡。正在使用具有快速执行器的硬件测试方法对开发方法进行验证，以控制模拟超音速飞行中的结构面板的外模线。该奖项反映了NSF的法定任务，并被认为是通过使用基金会的知识和更广泛影响的评估来审查Criteria来通过评估来通过评估来获得支持的。

项目成果

Optimal Sampling Methodologies for High-rate Structural Twinning

• DOI: 10.23919/fusion52260.2023.10224187
• 发表时间: 2023-06
• 期刊: 2023 26th International Conference on Information Fusion (FUSION)
• 作者: A. Vereen;Emmanuel A. Ogunniyi;Austin Downey;Erik Blasch;Jason D. Bakos;J. Dodson

Biased Electropermanent Magnetic Docking Design for Neutral Buoyancy UAV Deployment

用于中性浮力无人机部署的偏置电永磁对接设计

• DOI: 10.2514/6.2024-1694
• 发表时间: 2024
• 期刊: AIAA SCITECH 2024 Forum.
• 作者: Martin, Jacob;Satme, Joud;Downey, Austin R.
• 通讯作者: Downey, Austin R.

Extending Battery Life via Load SExtending Battery Life via Load Sharing in Electric Aircraft

通过负载延长电池寿命通过电动飞机的负载共享延长电池寿命

• DOI: 10.2514/6.2024-2154
• 发表时间: 2024
• 期刊: AIAA SCITECH 2024 Forum
• 作者: Anthony, George;Peskar, Jarrett;Downey, Austin R.J.;Booth, Kristen
• 通讯作者: Booth, Kristen