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

这个NSF CPS CAREER项目研究亚毫秒机器学习控制的硬件/软件协同设计，用于具有改变系统状态的非平稳输入的高速动态系统（即，损坏）。这些系统包括喷气发动机中的燃烧过程、碰撞期间的车辆结构以及主动爆炸缓解结构。该项目中采用的方法的新奇在于，将控制系统与它们将运行的计算硬件共同设计，以将系统延迟限制在1毫秒以内。开发的解决方案将能够以高速率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，然后将其用于将系统的动态建模为适当的预测范围。该项目正在开发一种自动编程方法，使这些实时控制器部署到紧凑和节能的计算设备。因此，这项研究将影响社会和NSF的使命，使人们能够更好地了解在高速率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。该项目将利用南卡罗来纳州大学现有的宝贵资源，让几名高中和本科生参与该项目;重点是为代表性不足的第一代和低收入学生提供研究经验。 该项目还将培养博士。更具体地说，这项研究正在解决如何使用可编程硬件来实现机器学习和控制要求超低延迟的系统的基本问题。这是通过制定一个实时机器学习控制框架来实现的，该框架共同设计硬件和软件，并为现场可编程门阵列（FPGA）的部署提供了一条途径。 该项目是：1）使用自定义在线训练器在片上训练新型长短期记忆（LSTM）模型，该训练器将高速率系统的传感器信号和致动器输入实时映射到系统状态。2)开发共享FPGA信号处理和存储器资源的方法，以并行利用多个LSTM前向传递核，同时保持确定性时序。3)研究微秒级实时机器学习控制的准确性、性能和资源需求之间的权衡。正在使用硬件在环测试方法对所开发的方法进行验证，该方法使用快速作用致动器在模拟高超音速飞行中控制结构板的外模线。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 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.