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CPS: Small: Real-Time Machine Learning-based Control of Human Cyber-Physical Balance Systems

CPS：小型：基于实时机器学习的人类网络物理平衡系统控制

基本信息

批准号：
1932370
负责人：
Jingang Yi
金额：
$ 50万
依托单位：
Rutgers University New Brunswick
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1932370&HistoricalAwards=false
关键词：
CPS Small Real Time Machine

项目摘要

The goal of this project is the advancement of machine learning dynamic models and real-time control systems for human cyber-physical balance systems. Ranging from biped walkers and human bicycle riding to human-controlled helicopters, human cyber-physical balance systems maintain challenging tasks of simultaneously trajectory-tracking and unstable platforms balancing. Although many physical models were developed in past decades, it is still challenging to safely and effectively operate these human-in-the-loop balance machines in highly variable, uncertain environments. This project will develop machine learning-based mathematical models and robust control strategies for human cyber-physical balance systems. The researchers will also develop a number of integrated research and education programs to attract students from underrepresented groups into engineering and involve undergraduate students into research.Human cyber-physical balance systems involve human movements as physical and forceful interactions with unstable, underactuated platforms. It is challenging to capture and control physical human-machine or human-robot interactions in complex, uncertain environments. This project will focus on: (1) development of machine learning-based models and characterization for human cyber-physical balance systems; (2) development of new hardware/software co-design accelerated learning-based real-time control to handle human cyber-physical balance system dynamics in highly variable, uncertain environments; and (3) robotic testbeds development, experimental validation and performance evaluation. The integration of data-driven model and learning-based control strategies, along with the hardware/software co-design enabled real-time implementation, provides new perspectives on performance enhancement of safety-critical or mission-critical cyber-physical systems in dynamic, uncertain environments.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.

该项目的目标是推进机器学习动态模型和人类网络物理平衡系统的实时控制系统。从步行者和人类骑自行车到人类控制的直升机，人类的网络-物理平衡系统保持着同时跟踪和不稳定平台平衡的挑战性任务。尽管在过去的几十年中开发了许多物理模型，但在高度可变、不确定的环境中安全有效地操作这些人在回路平衡机仍然具有挑战性。该项目将为人类网络物理平衡系统开发基于机器学习的数学模型和鲁棒控制策略。研究人员还将开发一些综合研究和教育项目，以吸引来自代表性不足的群体的学生进入工程领域，并让本科生参与研究。人类网络物理平衡系统涉及人类运动，即与不稳定、驱动不足的平台进行物理和有力的交互。在复杂、不确定的环境中捕捉和控制物理人机或人机交互是一项挑战。该项目将侧重于：（1）开发基于机器学习的模型和人类网络物理平衡系统的表征;（2）开发新的硬件/软件协同设计加速基于学习的实时控制，以处理高度可变，不确定环境中的人类网络物理平衡系统动态;以及（3）机器人测试台开发，实验验证和性能评估。数据驱动模型和基于学习的控制策略的集成，沿着硬件/软件协同设计使能的实时实现，提供了关于在动态、动态和动态环境中安全关键或任务关键网络物理系统的性能增强的新视角。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进行评估，被认为值得支持的搜索.

项目成果

期刊论文数量（12）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Learning-based Safe Motion Control of Vehicle Ski-Stunt Maneuvers

基于学习的车辆滑雪特技动作安全运动控制

DOI：
10.1109/aim52237.2022.9863309
发表时间：
2022
期刊：
Proceedings of the 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics
影响因子：
0
作者：
Han, Feng;Yi, Jingang
通讯作者：
Yi, Jingang

VLSI Hardware Architecture for Gaussian Process