基于提升小波的轧辊偏心信号提取及自适应控制

由于基于频域的经典小波变换运算时间较长,不能很好地满足轧辊偏心信号在线实时控制的要求,提出了用提升结构小波变换对偏心信号进行不同分辨率下分解处理的新方法.通过对轧制力信号和厚差信号的分析,利用提升和对偶提升原理将偏心信号从干扰信号和噪声信号中提取出来并通过参数自校正控制实现对轧辊偏心的在线动态控制.仿真结果表明,该方法获得了比较理想的效果,并且在同样数据长度下,提升小波变换运算速度比经典小波变换至少提高1倍以上.

Roller eccentricity signal pick-up and adaptive control based on lifting wavelet transform and self-optimization

Traditional wavelet transform based on frequency domain is too long to meet the need for real-time control of roller eccentricity.A novel wavelet based on lifting scheme is used to decompose and deal with eccentricity signals at different resolutions.Through analyzing roll force and thickness deviation signals,the lifting and dual lifting scheme theory is applied to distinguish eccentricity signals from disturbances and noise,and self-optimization is employed to real-time control the roller eccentricity.The results of simulation show that the control strategy is effective and at the same data length,the operational speed of lifting scheme is enhanced at least twice as that of traditional wavelet.