CAREER: Control of a Long and Curved String for Deep Underground Exploration
CAREER: Control of a Long and Curved String for Deep Underground Exploration
批准号:
2045894
负责人:
Xingyong Song
金额:
$63.81万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2021
资助国家:
美国
项目状态:
未结题
起止时间:
2021-09-01 至 2026-08-31
中文摘要
这项由教师早期职业发展计划(Career)资助的研究将为具有长弯曲弦状几何结构的大型系统的建模和控制提供新的基础知识。这将导致地下深层定向钻井系统的进步,影响国家战略领域,包括能源、环境和外层空间探索。在能源领域,它将实现自动化定向钻井,用于增强地热能源系统和非常规天然气生产。这将大大降低可再生能源和清洁能源的能源生产成本,更重要的是,可以减少对环境的影响,提高生产安全。在环境研究方面,该项目将解决进入南极古冰芯的关键技术障碍,以评估大规模气候模式并预测未来的气候变化,如全球变暖的演变。在外太空探索中,它将为控制钻探机器人到达火星上潜在的微生物生命迹象和水资源奠定基础,以完成火星探测的最终任务。在这些应用中,定向钻井控制具有挑战性,因为在地下深处,由于振动和井筒地层相互作用,潜在的不良工作条件是难以避免的。现有的定向钻井控制研究并不能保证避免这些不良工况的发生。地质挑战和对更环保生产过程的需求共同促使钻井过程更安全、更深、更准确、更可靠。在进行研究的同时,本项目将通过新课程开发、教师教育、远程实验室设施开发和外展活动,鼓励未被充分代表的学生群体参与控制工程。该项目的研究目标是创建一种新的框架来控制具有长,弯曲的弦状几何结构的大型系统,以避免在深地下勘探中出现不期望的操作条件。研究结果包括利用独特的管柱几何形状的新型面向控制模型,以及一种可以解决复杂障碍的状态-障碍避免控制新方法。在建模方面,研究了一种新的将解析方法与数值解相结合的混合方案,该方案既能提高计算效率,又能保证控制设计的高保真度。在控制方面,研究了一种以级联方式解决障碍回避的新方法。该方法首次实现了对形状复杂的状态障碍的系统求解,将状态-障碍回避控制的应用范围扩大到更多类型的障碍和系统(特别是高阶动态)。该奖项反映了美国国家科学基金会的法定使命,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。
英文摘要
The research funded by this Faculty Early Career Development Program (CAREER) grant will contribute new fundamental knowledge related to modeling and control of a large-scale system with a long, curved string-like geometry. This will lead to advances in deep underground directional drilling systems impacting national strategic areas including energy, the environment and outer space exploration. In energy, it will enable automated directional drilling for enhanced geothermal energy systems and unconventional natural gas production. This will significantly reduce the cost of energy production of renewables and clean energy, and more importantly, can reduce environmental impact and enhance production safety. In environmental research, the project will address a critical technical barrier to accessing ancient ice cores in the South Pole, to evaluate large-scale climate patterns and predict future climate changes such as the evolution of global warming. In outer space exploration, it will build the fundamental foundation to control a drilling robot to reach potential signs of microbial life and water resources on Mars, to fulfill the ultimate mission of the Mars exploration. Directional drilling control in these applications is challenging, because potentially undesirable working conditions due to vibrations and wellbore formation interaction in the deep underground are difficult to avoid. Existing studies on the directional drilling control cannot ensure avoiding these undesired operating conditions. The geological challenge and the need for a more environment-friendly production process together urge safer, deeper, more accurate and reliable drilling process. Along with the research, this project will encourage controls engineering among underrepresented student groups through new curriculum development, teacher education, remote lab facilities development and outreach activities.The research goal of this project is to create a new framework of controlling a large-scale system with a long, curved string-like geometry to avoid undesired operating conditions for deep underground exploration. The outcome includes a novel control-oriented model by leveraging the unique string geometry, and a new method for state-barrier avoidance control that can address complex barriers. For modeling, a new hybrid scheme that can integrate an analytical approach with a numerical solution is researched , and can achieve both computation-efficiency and high fidelity to enable control design. For control, a novel method that resolves the barrier avoidance in a cascade fashion is researched. This method enables addressing state barriers with complex shape in a systematic way for the first time, and can broaden the range of applications of state-barrier avoidance control to more types of barriers and systems (especially with high order dynamics).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.
期刊论文(3)
专著(0)
科研奖励(0)
会议论文
Designing Hybrid Neural Network Using Physical Neurons - A Case Study of Drill Bit-Rock Interaction Modeling
使用物理神经元设计混合神经网络 - 钻头-岩石相互作用建模案例研究
DOI:
10.23919/acc55779.2023.10156067
发表时间:
2023
期刊:
2023 American Control Conference (ACC)
影响因子:
--
作者:
[Zihan Zhang, Xingyong Song]
通讯作者:
Xingyong Song
Designing Hybrid Neural Network Using Physical Neurons—A Case Study of Drill Bit-Rock Interaction Modeling
使用物理神经元设计混合神经网络——钻头-岩石相互作用建模案例研究
DOI:
10.1115/1.4062631
发表时间:
2023
期刊:
and Control
影响因子:
--
作者:
[Zhang, Zihang, Song, Xingyong]
通讯作者:
Song, Xingyong
DOI:
10.1080/00207179.2022.2036371
发表时间:
2022-01
期刊:
International Journal of Control
影响因子:
2.1
作者:
[D. Tian;Xingyong Song]
通讯作者:
D. Tian;Xingyong Song
Control of Energy Efficient Powertrain for Autonomous and Connected Vehicles in a Mixed Autonomous and Human Driving Environment
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批准号:1826410
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项目类别:Standard Grant
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资助金额:$39.94万
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财政年份:2019
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负责人:Xingyong Song
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依托单位:
国内基金
海外基金
Cortical control of internal state in the insular cortex-claustrum region
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批准号:--
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项目类别:--
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资助金额:25万元
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批准年份:2020
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负责人:Robert Konrad Naumann
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依托单位: