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Award Abstract # 2045894
CAREER: Control of a Long and Curved String for Deep Underground Exploration

NSF Org: CMMI
Division of Civil, Mechanical, and Manufacturing Innovation
Recipient: TEXAS A&M ENGINEERING EXPERIMENT STATION
Initial Amendment Date: April 26, 2021
Latest Amendment Date: May 10, 2023
Award Number: 2045894
Award Instrument: Standard Grant
Program Manager: Marcello Canova
mcanova@nsf.gov
 (703)292-2576
CMMI
 Division of Civil, Mechanical, and Manufacturing Innovation
ENG
 Directorate for Engineering
Start Date: September 1, 2021
End Date: August 31, 2026 (Estimated)
Total Intended Award Amount: $638,088.00
Total Awarded Amount to Date: $646,088.00
Funds Obligated to Date: FY 2021 = $638,088.00
FY 2023 = $8,000.00
History of Investigator:
  • Xingyong Song (Principal Investigator)
    songxy@tamu.edu
Recipient Sponsored Research Office: Texas A&M Engineering Experiment Station
3124 TAMU
COLLEGE STATION
TX  US  77843-3124
(979)862-6777
Sponsor Congressional District: 10
Primary Place of Performance: Texas A&M Engineering Experiment Station
3367 TAMU
College Station
TX  US  77843-3367
Primary Place of Performance
Congressional District:
10
Unique Entity Identifier (UEI): QD1MX6N5YTN4
Parent UEI: QD1MX6N5YTN4
NSF Program(s): CAREER: FACULTY EARLY CAR DEV,
Dynamics, Control and System D
Primary Program Source: 01002324DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 030E, 034E, 1045, 116E, 8024, 9178, 9231, 9251
Program Element Code(s): 104500, 756900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.041

ABSTRACT

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.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Tian, D. and "Addressing complex state constraints in the integral barrier Lyapunov function-based adaptive tracking control" International journal of control , 2022 https://doi.org/10.1080/00207179.2022.2036371 Citation Details
Zhang, Z and Song, X. "Designing Hybrid Neural Network Using Physical Neurons-A Case Study of Drill Bit-Rock Interaction Modeling" 2023 American Control Conference , 2023 https://doi.org/10.23919/ACC55779.2023.10156067 Citation Details
Zhang, Zihang and Song, Xingyong "Designing Hybrid Neural Network Using Physical NeuronsA Case Study of Drill Bit-Rock Interaction Modeling" Journal of Dynamic Systems, Measurement, and Control , v.145 , 2023 https://doi.org/10.1115/1.4062631 Citation Details

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