A novel neural network warp tension control combined with online identification

A novel neural network warp tension control combined with online identification

Blog Article

In order to solve the tension fluctuation of long warp yarn during loom weaving, a control method of differential separation I-RBF-PID let-off system based on online identification transfer function was proposed.It is difficult to establish an accurate mathematical model because of the nonlinear, time-varying and complex coupling characteristics of the let-off system caused by the clearance of the transmission system and the change of the reel diameter of the weaving shaft.LMS algorithm was used to identify the time-varying and high-order transfer function of Ski de fond - Enfant - Skis - Classic loom, and the PID parameters were set by RBF neural network optimized by improved PSO.Step size optimizer and differential separation PID control strategy Ball - Accessories - Sunglasses were introduced to improve the convergence accuracy and reduce overshoot of RBF-PID.

The time-varying transfer function was applied to the I-RBF-PID control system by designing the transfer function updating formula.The results are compared with those of conventional PID controller and RBF-PID controller under the performance conditions of overshoot, arrival time and response speed.The effectiveness of the proposed high-precision tension control scheme was verified by experiments.