CAREER: Real-time Convex Optimization for High-Performance Control of Autonomous Systems
职业:自治系统高性能控制的实时凸优化
基本信息
- 批准号:1613235
- 负责人:
- 金额:$ 48.72万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2016
- 资助国家:美国
- 起止时间:2016-01-01 至 2021-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
This Faculty Early Career Development (CAREER) Program grant will build innovative mathematical and computational methods of decision making for control of next generation high-performance autonomous systems. A broad range of emerging technologies utilize autonomy, including self-driving vehicles, quadrotors for delivery, search and rescue robots, mobile sensors for environmental monitoring, climate control systems for warehouses and buildings, automated drug dispensing, and next-generation power networks. Large-scale deployment of autonomous systems is a critical technological leap that will fundamentally transform our lives. Autonomous systems will maximize efficiency and reliability. Furthermore, they will improve safety by reducing or removing direct human involvement in hazardous tasks. However, autonomous systems present fundamentally difficult technical challenges to go beyond the limitations of the current state of the art in automation. This project lays the mathematical and computational foundations for a transformative approach to autonomy, one that makes previously impractical calculations tractable, and provides guaranteed levels of performance and stability. To meet fundamentally new and distinctly challenging performance and reliability requirements, future autonomous systems must make best possible decisions to control their actions without a human operator in the loop. They must utilize their full performance envelopes, while simultaneously satisfying critical mission and environmental constraints. Consequently, mathematical optimization problems are ubiquitous in autonomous control. Though optimization provides a powerful formulation framework, thus far real-time optimization based control has not transitioned to common practice due to shortcomings in the current algorithmic capabilities. To enable real-time optimization based control, this CAREER grant will develop a comprehensive theory of convexification -- formulation of control problems as tractable convex optimization problems -- enabling high performance control that is currently not achievable in many applications. Specifically, it will develop: accurate formulations of autonomous control problems as convex optimizations; customized numerical optimization algorithms that exploit problem structure for extremely fast and reliable onboard computations; and rigorous verification methods to certify the performance and robustness of the resulting autonomous control algorithms. These advances and resulting insights will also benefit systematic systems engineering and design for autonomous systems, high-level autonomous mission planning, and control of autonomous multi-agent systems. Collectively the theoretical and algorithmic products will form a strong technical foundation allowing the transition of real-time optimization-based control for autonomous systems into practice.
这项学院早期职业发展(Career)计划拨款将为控制下一代高性能自主系统建立创新的数学和计算决策方法。广泛的新兴技术利用自动驾驶技术,包括自动驾驶车辆、用于运送的四轴飞行器、搜救机器人、用于环境监测的移动传感器、用于仓库和建筑物的气候控制系统、自动配药和下一代电力网络。自主系统的大规模部署是一项关键的技术飞跃,将从根本上改变我们的生活。自主系统将最大限度地提高效率和可靠性。此外,它们将通过减少或消除人类对危险任务的直接参与来提高安全性。然而,自主系统提出了根本困难的技术挑战,以超越目前自动化技术水平的限制。该项目为实现自主的变革性方法奠定了数学和计算基础,这种方法使以前不切实际的计算变得容易,并提供了有保证的性能和稳定性水平。为了从根本上满足新的和具有明显挑战性的性能和可靠性要求,未来的自主系统必须做出尽可能好的决策,以在没有人工操作员参与的情况下控制它们的行为。他们必须充分利用其全部性能,同时满足关键任务和环境限制。因此,数学优化问题在自主控制中是普遍存在的。虽然优化提供了一个强大的公式框架,但由于当前算法能力的缺陷,基于实时优化的控制到目前为止还没有过渡到通用实践。为了实现基于实时优化的控制,这份职业资助金将开发一种全面的凸化理论--将控制问题表述为易于处理的凸优化问题--实现目前在许多应用程序中无法实现的高性能控制。具体地说,它将开发:作为凸优化的自主控制问题的准确公式;利用问题结构进行极快和可靠的机载计算的定制数值优化算法;以及验证所产生的自主控制算法的性能和稳健性的严格验证方法。这些进展和由此产生的见解也将有助于自主系统的系统工程和设计、高级自主任务规划和自主多智能体系统的控制。理论和算法产品将共同构成强大的技术基础,使自主系统的基于实时优化的控制转变为实践。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
数据更新时间:{{ journalArticles.updateTime }}
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
数据更新时间:{{ journalArticles.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ monograph.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ sciAawards.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ conferencePapers.updateTime }}
{{ item.title }}
- 作者:
{{ item.author }}
数据更新时间:{{ patent.updateTime }}
Behcet Acikmese其他文献
Behcet Acikmese的其他文献
{{
item.title }}
{{ item.translation_title }}
- DOI:
{{ item.doi }} - 发表时间:
{{ item.publish_year }} - 期刊:
- 影响因子:{{ item.factor }}
- 作者:
{{ item.authors }} - 通讯作者:
{{ item.author }}
{{ truncateString('Behcet Acikmese', 18)}}的其他基金
Collaborative Research: Negotiated Planning for Stochastic Control of Dynamical Systems
协作研究:动力系统随机控制的协商规划
- 批准号:
2105502 - 财政年份:2021
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CPS: Medium: Collaborative Research: Optimization-Based Planning and Control for Assured Autonomy: Generalizing Insights From Autonomous Space Missions
CPS:中:协作研究:基于优化的规划和控制以确保自主:概括自主空间任务的见解
- 批准号:
1931744 - 财政年份:2019
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CPS: Synergy: Collaborative Research: Autonomy Protocols: From Human Behavioral Modeling to Correct-By-Construction, Scalable Control
CPS:协同:协作研究:自主协议:从人类行为建模到构建纠正、可扩展控制
- 批准号:
1624328 - 财政年份:2016
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CPS: Synergy: Collaborative Research: Semantics of Optimization for Real Time Intelligent Embedded Systems (SORTIES)
CPS:协同:协作研究:实时智能嵌入式系统(SORTIES)优化的语义
- 批准号:
1619729 - 财政年份:2016
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CPS: Synergy: Collaborative Research: Semantics of Optimization for Real Time Intelligent Embedded Systems (SORTIES)
CPS:协同:协作研究:实时智能嵌入式系统(SORTIES)优化的语义
- 批准号:
1446520 - 财政年份:2015
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CAREER: Real-time Convex Optimization for High-Performance Control of Autonomous Systems
职业:自治系统高性能控制的实时凸优化
- 批准号:
1454543 - 财政年份:2015
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
CPS: Synergy: Collaborative Research: Autonomy Protocols: From Human Behavioral Modeling to Correct-By-Construction, Scalable Control
CPS:协同:协作研究:自主协议:从人类行为建模到构建纠正、可扩展控制
- 批准号:
1446578 - 财政年份:2014
- 资助金额:
$ 48.72万 - 项目类别:
Standard Grant
相似国自然基金
Immuno-Real Time PCR法精确定量血清MG7抗原及在早期胃癌预警中的价值
- 批准号:30600737
- 批准年份:2006
- 资助金额:22.0 万元
- 项目类别:青年科学基金项目
无色ReAl3(BO3)4(Re=Y,Lu)系列晶体紫外倍频性能与器件研究
- 批准号:60608018
- 批准年份:2006
- 资助金额:28.0 万元
- 项目类别:青年科学基金项目
相似海外基金
CAREER: Real-Time First-Principles Approach to Understanding Many-Body Effects on High Harmonic Generation in Solids
职业:实时第一性原理方法来理解固体高次谐波产生的多体效应
- 批准号:
2337987 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Secure Miniaturized Bio-Electronic Sensors for Real-Time In-Body Monitoring
职业:用于实时体内监测的安全微型生物电子传感器
- 批准号:
2338792 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Towards Safety-Critical Real-Time Systems with Learning Components
职业:迈向具有学习组件的安全关键实时系统
- 批准号:
2340171 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Personalized, wearable robot mobility assistance considering human-robot co-adaptation that incorporates biofeedback, user coaching, and real-time optimization
职业:个性化、可穿戴机器人移动辅助,考虑人机协同适应,结合生物反馈、用户指导和实时优化
- 批准号:
2340519 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: SHF: Bio-Inspired Microsystems for Energy-Efficient Real-Time Sensing, Decision, and Adaptation
职业:SHF:用于节能实时传感、决策和适应的仿生微系统
- 批准号:
2340799 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Real-time control of elementary catalytic steps: Controlling total vs partial electrocatalytic oxidation of alkanes and olefins
职业:实时控制基本催化步骤:控制烷烃和烯烃的全部与部分电催化氧化
- 批准号:
2338627 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Frequency Agile Real-Time Reconfigurable RF Analog Co-Processor Design Leveraging Engineered Nanoparticle and 3D Printing
职业:利用工程纳米颗粒和 3D 打印进行频率捷变实时可重构射频模拟协处理器设计
- 批准号:
2340268 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Foundations of Scalable and Resilient Distributed Real-Time Decision Making in Open Multi-Agent Systems
职业:开放多代理系统中可扩展和弹性分布式实时决策的基础
- 批准号:
2339509 - 财政年份:2024
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Semantic and Goal-oriented Status Updating for Real-time Inference, Monitoring, and Decision-Making
职业:语义和目标导向的状态更新,用于实时推理、监控和决策
- 批准号:
2239677 - 财政年份:2023
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant
CAREER: Learning for Real-Time Embedded Optimization
职业:学习实时嵌入式优化
- 批准号:
2239771 - 财政年份:2023
- 资助金额:
$ 48.72万 - 项目类别:
Continuing Grant