融合在线辨识的新型神经网络经纱张力控制

A novel neural network warp tension control combined with online identification

为解决织机织造过程中长片段经纱张力波动,提出基于在线辨识传递函数的微分分离I-RBF-PID送经系统控制方法。针对织机送经传动系统的间隙和织轴卷径变化等因素造成送经系统非线性、时变和耦合关系复杂等特点导致的精确数学模型难以建立问题,利用LMS算法辨识织机时变,采用高阶的传递函数,由改进PSO优化的RBF神经网络整定PID参数,引入步长优化器和微分分离PID控制策略,提高RBF-PID收敛精度和减少超调。设计传递函数更新公式,将辨识得到的时变传递函数应用于I-RBF-PID控制系统,并与常规PID控制器和RBF-PID控制器在超调量、到达稳态时间和上升时间等性能条件下的结果进行了比较。仿真实验验证所提出的高精度张力控制方案的有效性。

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 loom,and the PID parameters were set by RBF neural network optimized by improved PSO.Step size optimizer and differential separation PID control strategy 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.