Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 820-830.doi: 10.13229/j.cnki.jdxbgxb20200130

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Nonlinear dynamic analysis and stability control of drilling tool conveying mechanism

Ping YU(),Te MU,Li-hui ZHU,Zi-ye ZHOU,Jie SONG   

  1. College of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
  • Received:2020-03-05 Online:2021-05-01 Published:2021-05-07

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Abstract:

The drilling tool conveying mechanism is a rigid-flexible coupling multibody system. Due to the uncertainty of system parameters and external disturbances during operation， its working stability is reduced. To solve this problem， a nonlinear dynamic model of the drilling tool conveying mechanism was established using the multibody system transfer matrix method， and a trajectory function was set according to the work requirements. The working stability control of the drilling tool conveying mechanism is divided into two parts： working position and attitude tracking control and nonlinear vibration suppression of the flexible body in the mechanism. A PID control algorithm based on BP neural network is proposed to dynamically track the posture of the whole mechanism. For the vibration of the flexible body， a piezoelectric material is pasted on the surface of the flexible member to form an intelligent composite material， and the sliding mode control algorithm based on fuzzy RBF neural network is used for suppression. The simulation results show that the working position accuracy of the whole mechanism is significantly improved， and the vibration of the flexible body is effectively suppressed.

Key words: mechatronic engineering, nonlinear dynamics, forced vibration, stability control

CLC Number: 

  • TE928

Fig.1

Sichuan Honghua drilling toolconveying mechanism"

Fig.2

Simplified schematic diagram of drillingtool conveying mechanism"

Fig.3

Block diagram of position tracking andcontrol based on BP-PID controller"

Fig.4

BP neural network topology"

Fig.5

Schematic of vibration detection/control ofpiezoelectric materials"

Fig.6

sgn function"

Fig.7

T-S function(β1<β2<β3)"

Fig.8

Block diagram of sliding mode control system based on fuzzy neural network"

Fig.9

Fuzzy RBF neural network topology"

Fig.10

Trajectory curve of each component"

Fig.11

BP-PID control of Adams-Simulinkco-simulation"

Table 1

BP neural network initial weights"

输入层至隐含层初始权值w1,1=0.25w1,2=-0.13w1,3=-0.02w1,4=-0.12
w1,5=-0.43w1,6=0.05w1,7 = 0.64w1,8=-0.04
w2,1=-0.23w2,2=0.33w2,3=-0.05w2,4=0.43
w2,5=0.18w2,6=0.00w2,7=-0.02w2,8=-0.75
w3,1=0.46w3,2=0.71w3,3=-0.17w3,4=-0.03
w3,5=0.83w3,6=0.25w3,7=0.00w3,8=0.64
w4,1=0.22w4,2=0.79w4,3=-0.06w4,4=0.10
w4,5=-0.20w4,6=0.20w4,7=-0.01w4,8=0.46
隐含层至输出层初始权值w1,1=0.49w1,2=-0.07w1,3=-0.14w2,1=0.13
w2,2=0.00w2,3=-0.10w3,1=0.63w3,2=0.40
w3,3=0.71w4,1=-0.77w4,2=0.80w4,3=0.04
w5,1=0.35w5,2=-0.24w5,3 = 0.02w6,1=-0.12
w6,2=0.00w6,3=0.72w7,1=-0.09w7,2=0.20
w7,3=-0.30w8,1=0.60w8,2=-0.01w8,3=0.02

Fig.12

Controlled trajectory curve of each component"

Table 2

Initial weight of fuzzy RBF neural network"

w1,1=0.54w1,2=-0.07w2,1=-0.26w2,2=0.13
w3,1=0.22w3,2=-0.01w4,1=-0.30w4,2=0.29
w5,1=0.81w5,2=-0.15w6,1=0.03w6,2=0.10
w7,1=-0.23w7,2=0.09w8,1=0.11w8,2=0.34
w9,1=-0.20w9,2=0.20w10,1=-0.01w10,2=0.46
w11,1=0.17w11,2=0.23w12,1=-0.64w12,2=-0.52
w13,1=0.55w13,2=-0.43w14,1=-0.23w14,2=-0.31
w15,1=0.15w15,2=-0.26w16,1=-0.08w16,2=0.37
w17,1=0.28w17,2=0.04w18,1=0.12w18,2=-0.47
w19,1=0.00w19,2=0.72w20,1=-0.09w20,2=0.20
w21,1=0.80w21,2=0.46w22,1=-0.22w22,2=-0.32
w23,1=-0.08w23,2=0.23w24,1=-0.06w24,2=0.33
w25,1=-0.18w25,2=0.37w26,1=-0.14w26,2=0.02
w27,1=-0.33w27,2=-0.21w28,1=0.84w28,2=0.74
w29,1=0.12w29,2=-0.27w30,1=-0.35w30,2=0.53
w31,1=-0.28w31,2=0.11w32,1=-0.07w32,2=0.00
w33,1=0.26w33,2=-0.45w34,1=-0.31w34,2=0.01
w35,1=-0.25w35,2=0.17w36,1=0.20w36,2=0.39
w37,1=0.13w37,2=-0.38w38,1=0.26w38,2=0.00
w39,1=0.41w39,2=0.67w40,1=-0.05w40,2=0.71
w41,1=0.03w41,2=0.22w42,1=-0.17w42,2=0.64
w43,1=-0.08w43,2=0.23w44,1=0.73w44,2=-0.59
w45,1=0.81w45,2=-0.47w46,1=0.61w46,2=-0.09
w47,1=0.00w47,2=-0.19w48,1=0.43w48,2=0.74
w49,1=-0.09w49,2=0.38

Fig.13

Transverse centroid deformation of crank"

1 Sun Y H, Zhang F Y, Wang Q Y, et al. Application of "Crust 1" 10k ultra-deep scientific drilling rig in Songliao Basin Drilling Project (CCSD-SKII)[J]. Journal of Petroleum Science and Engineering,2016,145: 222-229.
2 柴汇,荣学文,唐兴鹏,等. 基于能量规划的崎岖地面四足机器人平面跳跃控制[J]. 吉林大学学报:工学版,2017,47(2):557-566.
Chai Hui,Rong Xue-wen,Tang Xing-peng.Telescoping path optimization of a single-cylinder pin-type multi-section boom based on Hopfield neural network[J]. Journal of Jilin University(Engineering and Technology Edition),2017,47(2):557-566.
3 Wang F, Chao Z Q, Huang L B, et al. Trajectory tracking control of robot manipulator based on RBF neural network and fuzzy sliding mode[J]. Cluster Computing, 2019,22:5799-5809.
4 Cao F F, Liu J K. Optimal trajectory control for a two-link rigid-flexible manipulator with ODE-PDE model[J]. Optimal Control Applications and Methods, 2018, 39(4): 1515-1529.
5 朱伟,王传伟,顾开荣,等. 一种新型张拉整体并联机构刚度及动力学分析[J]. 吉林大学学报:工学版,2018,48(6):1777-1786.
Zhu Wei,Wang Chuan-wei,Gu Kai-rong. Stiffness and dynamics analysis of a new type of tensegrity parallel mechanism[J]. Journal of Jilin University(Engineering and Technology Edition),2018,48(6):1777-1786.
6 Chiang C J, Chen Y C. Neural network fuzzy sliding mode control of pneumatic muscle actuators[J]. Engineering Applications of Artificial Intelligence, 2017, 65: 68-86.
7 毛艳,成凯. 基于Hopfield神经网络的单缸插销式伸缩臂伸缩路径优化[J].吉林大学学报:工学版,2020,50(1):53-65.
Mao Yan,Cheng Kai. Telescoping path optimization of a single-cylinder pin-type multi-section boom based on Hopfield neural network[J]. Journal of Jilin University(Engineering and Technology Edition), 2020,50(1):53-65.
8 Qiu Zhi-cheng, Wang Xian-feng, Zhang Xian-min, et al. A novel vibration measurement and active control method for a hinged flexible two-connected piezoelectric plate[J]. Mechanical Systems and Signal Processing, 2018,107:357-395.
9 王杰,钱利勤,陈新龙,等. 自动猫道起升系统动力学模型与分析[J]. 工程设计学报, 2016,23(5):437-443, 460.
Wang Jie,Qian Li-qin,Chen Xin-long, et al. Dynamics model and analysis of the lifting system of automatic catwalk[J]. Journal of Engineering Design,2016,23(5):437-443, 460.
10 于萍,宋杰,赵楠. 自动猫道翻转机构失效研究[J]. 机械设计与制造,2019(10):257-261.
Yu Ping,Song Jie,Zhao Nan. Failure analysis of turnover mechanism in automatic catwalk[J]. Machinery Design & Manufacture,2019(10):257-261.
11 Pawlus W, Choux M, Hansen M R, et al. Load torque estimation method to design electric drivetrains for offshore pipe handling equipment[J]. Journal of Offshore Mechanics and Arctic Engineering, 2016, 138(4):041304.
12 黄悦华,赵亮亮,李峰,等. 基于ADAMS的动力猫道扶持臂变幅机构优化设计[J]. 石油矿场机械,2016,44(7):36-38.
Huang Yue-hua,Zhao Liang-liang,Li Feng. Optimization design of the amplitude variation mechanism of supportive arm of power catwalk based on ADAMS[J]. Oil Field Equipment, 2016,44(7):36-38.
13 康思杰. 基于刚柔耦合模型的钻具自动输送装置动态特性研究[D]. 长春: 吉林大学机械与航空航天工程学院,2017.
Kang Si-jie. Study on the dynamic characteristics of drilling tool automatic conveying mechanism based on rigid-flexible coupling model[D]. Changchun:College of Mechanical and Aerospace Engineering, Jilin University, 2017.
14 Rong Bao, Rui Xiao-ting, Wang Guo-ping, et al. Erratum: new efficient method for dynamic modeling and simulation of flexible multibody systems moving in plane[J]. Multibody System Dynamics, 2010, 24(2):201-202.
15 Rong Bao, Rui Xiao-ting, Tao Ling. Dynamics and genetic fuzzy neural network vibration control design of a smart flexible four-bar linkage mechanism[J]. Multibody System Dynamics, 2012, 38(4):291-311.
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