Active tension control of the conical winding system based on the neural network control algorithm of radial-basis functions

Abstract

A conical winding formation and tension control system was proposed in the doubling operation based on the yarn guide mode of a single spindle in this study. Conical winding formation realized the radial unwinding of wound package. An overfeed mechanism was introduced to achieve closed-loop control of yarn tension. The overfeed wheel was driven by a brushless Direct Current motor. The tension control system combined a Proportion Integration Differentiation controller with a radial-basis-function neural network, whose purpose was to meet the control requirements of the brushless DC motor. This system consisted of three main steps: Firstly, the radial-basis-function neural network was used to identify the system online. Secondly, the gradient descent method was used to adjust the node weight, center vector, and baseband width. Finally, incremental PID parameters online were adjusted according to the identified Jacobian information. A mathematical model of a control system was established in Matrix Laboratory. An experimental platform was designed for doubling winder to compare the control effects of Radial-basis-function-PID with traditional PID. The simulation results showed that the RBF-PID had a smaller overshoot of yarn tension, shorter adjustment time, and smaller steady-state error compared with the traditional PID controller in doubling operation by simulating the mathematical model. Experimental results showed the RBF-PID controller had good performance and stability and could be applied to yarns with different average linear velocity, yarn counts and strands . The yarn tension fluctuation will not exceed ±3% of the target value when the experimental materials and the cone angles are unchanged.