Research on the drilling state intelligent sensing and adaptive optimization control of the fast drilling robot

The fast drilling robot is the most intelligent mining drilling equipment at present. The drilling state intelligent sensing and adaptive control technology are developed to solve the problem of the intelligent control of the drilling process. According to the engineering practice, with drilling eff...

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Vydáno v:Journal of physics. Conference series Ročník 2902; číslo 1; s. 12045 - 12051
Hlavní autoři: Liu, Xiaohua, Wang, Qingfeng, Liu, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Bristol IOP Publishing 01.11.2024
Témata:
Adaptive algorithms
Adaptive control
Boundary conditions
Control equipment
Drilling machines (tools)
Evolutionary algorithms
Evolutionary computation
Optimization
Parameters
Robot control
Robots
Smoothness
ISSN:1742-6588, 1742-6596
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Shrnutí:The fast drilling robot is the most intelligent mining drilling equipment at present. The drilling state intelligent sensing and adaptive control technology are developed to solve the problem of the intelligent control of the drilling process. According to the engineering practice, with drilling efficiency as the control target, the characteristics of the working process of the fast drilling robot and the influence of boundary conditions such as sticking probability, slag-discharge smoothness, and anchorage stability on the drilling process are analyzed. Corresponding mathematical formulas are derived, and the mathematical model of the drilling state intelligent sensing and adaptive control is constructed. The adaptive control technology based on the differential evolution algorithm is developed to realize the automatic optimization of control parameters. The ground drilling tests prove that intelligent sensing and adaptive control technology can adjust the control parameters quickly when the drilling rock changes so that the drilling robot can maintain an efficient and stable drilling state.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/2902/1/012045