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"

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