Journal of Textile Research ›› 2025, Vol. 46 ›› Issue (02): 92-99.doi: 10.13475/j.fzxb.20240904301

• Textile Engineering • Previous Articles     Next Articles

Hyper basis function-based adaptive inverse non-singular method for constant-tension yarn transport

WANG Luojun1,2, PENG Laihu1(), XIONG Xuyi1, LI Yang2, HU Xudong1   

  1. 1. Key Laboratory of Modern Textile Machinery & Technology of Zhejiang Province, Zhejiang Sci-Tech University, Hangzhou, Zhejiang 310018, China
    2. School of Automation, Zhejiang Polytechnic University of Mechanical and Electrical Engineering, Hangzhou, Zhejiang 310053, China
  • Received:2024-09-29 Revised:2024-10-23 Online:2025-02-15 Published:2025-03-04
  • Contact: PENG Laihu E-mail:laihup@zstu.edu.cn

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Abstract:

Objective In high-speed and precision knitting process, the complex dynamic behavior of yarn transmission not only affects the accuracy of tension control, but also increases the complexity and maintenance cost of the system. It is hence necessary to explore new control methods to improve the accuracy and reliability of yarn tension stability control. Sensorless tension control method reduces the dependence on sensors. By optimizing the structure and material of the yarn transmission mechanism, the influence of adverse factors such as vibration and friction is reduced, and the production efficiency and product quality of the circular machine are improved.

Method The yarn motion during knitting was decoupled into two independent systems using the inversion method, and an inverse non-singular terminal sliding mode controller was designed to improve the sliding mode surface to make the yarn real-time tension error converge quickly in a short time. The hyper basis function (HBF) neural network was introduced into the interval state observer of the yarn transmission system, which was close to the random response caused by the changes of parameters such as weft storage radius and the inertia of the knitting area.

Results The designed HBF neural network interval observer was used to estimate the boundary value of the moving yarn system. After the operation of the three controllers, the controller designed in this research was shown to stabilize the tension in 1.6, which is significantly better than the 3.5 s of the conventional sliding mode and the 2.4 s described in related literature, and the adjustment time was reduced by 57% and 33% respectively. The experimental results showed that the sliding mode controller designed in this paper has faster response and higher tracking accuracy, which is significantly better than the other two controllers. The traditional proportional-integral-differential (PID) controller performed the worst for the yarn relaxation problem when the moving yarn system is started, while the improved sliding mode controller can stabilize the winding speed faster. In addition, the terminal sliding mode controller designed in this paper can quickly restore the tension stability after the random disturbance is added in the 8th s of the system movement, showing excellent robust performance. The sliding mode controller quickly converges the tension error to zero within 1.6 s after the start-up of the winding system. Compared with the other three controllers, the sliding mode controller has the optimal response speed and adjustment ability, ensuring that the tension control system can recover to steady state operation in a short time. It obviously improves the steady-state operation ability and dynamic adjustment ability of the yarn system, so as to realize the constant tension control of the yarn. Through experiments, it can be verified that the method in this paper can quickly and accurately follow the change of the target tension, and has low sensitivity to external interference. Even in the face of the sudden change of the tension setting, it can effectively inhibit the overshoot, show stronger stability and response speed, and has better robustness, faster response speed and higher control accuracy in the actual production environment, which is more in line with the production requirements.

Conclusion The relationship between yarn tension and motion speed is established by modeling the motion yarn system of yarn feeder and loop forming mechanism. The neural network technology is used to approximate the influence of unknown time-varying parameters, and an interval observer is constructed based on it to realize the effective observation of key state variables. The traditional nonsingular fast terminal sliding mode controller is improved. By designing a new sliding mode surface function, not only the finite time convergence of tracking error is guaranteed, but also the convergence rate is accelerated. Combined with the inversion control algorithm, the robustness and stability of the system are significantly enhanced. Simulation and experimental results show that the proposed RBF neural network interval observer can accurately track the system state and improve the control accuracy. Compared with the traditional method, the improved sliding mode controller shows faster error convergence speed and higher response efficiency.

Key words: yarn tension, hyper basis function neural network, state observer, tension error, sliding mode controller, circular knitting machine

CLC Number: 

  • TS181.8

Fig.1

Schematic diagram of yarn conveying system of circular weft machine"

Fig.2

Yarn constant tension control system"

Tab.1

Parameters of motion yarn system"

参数 单位 参数取值
E cN/m2 1.2×104
ρ tex 27.4
L m 1.2
ε kg/m2 1.0×10-3
J kg/m2 2×10-4
R 0 m 0.5
F 0 cN 30
V 0 m/s 0.6

Fig.3

Boundary value of yarn tension monitored by observer"

Fig.4

Boundary value of yarn speed monitored by observer"

Fig.5

Yarn tension comparison"

Fig.6

Yarn speed comparison"

Fig.7

Yarn tension error curve"

Fig.8

Experimental platform for yarn transportation under constant tension"

Fig.9

Comparison of control results.(a) Methodology of this paper; (b) Literature [18] methodology; (c) Conventional sliding form; (d)PID"

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