NCS-FO: Collaborative Research: Integrative Foundations for Interactions of Complex Neural and Neuro-inspired Systems with Realistic Environments

NCS-FO：协作研究：复杂神经和神经启发系统与现实环境相互作用的综合基础

• 批准号: 1735003
• 负责人: John Doyle
• 金额: $ 36.92万
• 依托单位: California Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1735003&HistoricalAwards=false
• 关键词: NCS; FO; Collaborative; Research; Integrative

This project will integrate the capabilities of deep learning networks into a biologically inspired architecture for sensorimotor control that can be used to design more robust platforms for complex engineered systems. Studies of the flexibility and contextual aspects of sensorimotor planning and control will extend existing paradigms for human-robot interactions and serve as the foundation for creating personal assistants that are able to operate in natural settings. Growing understanding of how these layered architectures are organized in the brain to produce highly robust, flexible, and efficient behavior will have many applications to rapidly evolving technologies in complex environments, including the Internet of Things, autonomous transportation, and sustainable energy networks.The nervous system is a layered architecture, seamlessly integrating high-level planning with fast lower level sensing, reflex, and action in a way that this project aims to both understand more deeply and mimic in advanced technology. The central goal is to develop a theoretical framework for layered architectures that takes into account both system level functional requirements and hardware constraints. There are both striking commonalities and significant differences between biology and technology in using layered architectures for active feedback control. The most salient and universal hardware constraints are tradeoffs between speed, accuracy, and costs (to build, operate, and maintain), and successful architectures cleverly combine diverse components to create systems that are both fast and accurate, despite being built from parts that are not. Recent progress has made it possible to integrate realistic features and constraints for sensorimotor coordination in a coherent and rigorous way using worst-case L-infinity bounded uncertainty models from robust control, but much remains to explore to realize its potential in neuroscience and neuro-inspired engineering. Another application of this framework will be to software defined networking, which explicitly separates data forwarding and data control, and provides an interface through which network applications (such as traffic engineering, congestion control and caching) can programmatically control the network. This makes it a potential "killer app" for a theory of integrated planning/reflex layering; research collaborators will be eager to deploy new protocols on testbeds and at scale.

该项目将深度学习网络的功能整合到一个受生物学启发的感觉运动控制架构中，该架构可用于为复杂的工程系统设计更强大的平台。对感觉运动规划和控制的灵活性和上下文方面的研究将扩展现有的人机交互范例，并作为创造能够在自然环境中操作的个人助理的基础。对这些分层架构如何在大脑中组织以产生高度健壮、灵活和高效的行为的理解日益加深，将在复杂环境中快速发展的技术中有许多应用，包括物联网、自动运输和可持续能源网络。神经系统是一个分层结构，无缝地将高级规划与快速的低级感知、反射和行动结合在一起，该项目旨在更深入地理解和模仿先进技术。中心目标是为考虑系统级功能需求和硬件约束的分层体系结构开发一个理论框架。在使用分层结构进行主动反馈控制方面，生物学和技术之间既有显著的共性，也有显著的差异。最显著和通用的硬件约束是速度、精度和成本（构建、操作和维护）之间的权衡，成功的体系结构巧妙地组合了不同的组件，以创建既快速又准确的系统，尽管它是由不准确的部件构建的。最近的进展使得使用鲁棒控制的最坏情况l -∞有界不确定性模型以连贯和严格的方式整合感觉运动协调的现实特征和约束成为可能，但要实现其在神经科学和神经启发工程中的潜力，仍有许多有待探索的地方。该框架的另一个应用将是软件定义的网络，它明确地分离数据转发和数据控制，并提供一个接口，通过该接口，网络应用程序（如流量工程、拥塞控制和缓存）可以以编程方式控制网络。这使得它成为综合规划/反射分层理论的潜在“杀手级应用”；研究合作者将渴望在试验台和规模上部署新的协议。

项目成果

Scalable Robust Adaptive Control from the System Level Perspective

从系统级角度看可扩展的鲁棒自适应控制

• DOI: 10.23919/acc.2019.8814896
• 发表时间: 2019
• 期刊: 2019 American Control Conference (ACC
• 作者: Ho, Dimitar;Doyle, John C.
• 通讯作者: Doyle, John C.

Experimental and educational platforms for studying architecture and tradeoffs in human sensorimotor control

用于研究人类感觉运动控制的架构和权衡的实验和教育平台

• DOI: 10.23919/acc.2019.8814470
• 发表时间: 2019
• 期刊: 2019 American Control Conference
• 作者: Liu, Quanying;Nakahira, Yorie;Mohideen, Ahkeel;Dai, Adam;Choi, Sunghoon;Pan, Angelina;Ho, Dimitar M.;Doyle, John C.
• 通讯作者: Doyle, John C.

An integrative perspective to LQ and L-infinity control for delayed and quantized systems

延迟和量化系统的 LQ 和 L-无穷控制的综合视角

• DOI: 10.1109/tac.2020.2968856
• 发表时间: 2020
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Nakahira, Yorie;Chen, Lijun
• 通讯作者: Chen, Lijun

Robust Model-Free Learning and Control without Prior Knowledge

• DOI: 10.1109/cdc40024.2019.9029986
• 发表时间: 2019-12
• 期刊: 2019 IEEE 58th Conference on Decision and Control (CDC)
• 作者: Dimitar Ho;J. Doyle