电火花加工放电状态的自适应滤波

发布时间：2018-06-28 10:16

本文选题：自适应滤波 + 放电状态　；参考：《哈尔滨工程大学学报》2015年11期

【摘要】：电火花加工过程具有非线性、弱稳态的特性,加工过程中放电状态变化剧烈,导致控制变量随之大幅度震动,对系统稳定性极为不利,因此需要对放电状态曲线进行光滑滤波。由于二阶自回归放电状态模型的参数辨识是有偏估计,提出采用中间变量的方法消除参数的有偏估计。采用一个卡尔曼滤波器建立中间变量,另一个卡尔曼滤波器用于二阶自回归放电状态模型的参数辨识,2个卡尔曼滤波器共同组成了一个自适应滤波器。经实验数据验证:该自适应滤波方法不仅能够光滑放电状态曲线,而且能够消除线性滤波本身固有的相移和延迟,同时,自适应滤波方法不会像线性滤波方法造成整个控制系统阶次大幅升高、稳定性下降,能够充分保障实时控制和系统稳定性。
[Abstract]:The process of EDM has the characteristics of nonlinear and weak steady state, and the discharge state changes dramatically during the machining process, which results in the control variables shaking greatly, which is extremely unfavorable to the stability of the system, so it is necessary to smooth filter the discharge state curve. Because the parameter identification of the second order autoregressive discharge state model is biased estimation, an intermediate variable method is proposed to eliminate the biased estimation of the parameters. One Kalman filter is used to establish the intermediate variable and another Kalman filter is used to identify the parameters of the second order autoregressive discharge state model. Two Kalman filters are combined to form an adaptive filter. The experimental data show that the adaptive filtering method can not only smooth the discharge state curve, but also eliminate the inherent phase shift and delay of the linear filter, at the same time, The adaptive filtering method can not increase the order of the whole control system greatly and decrease the stability of the whole control system, which can fully guarantee the real-time control and the stability of the system.
【作者单位】：北京建筑大学机电与车辆工程学院;
【基金】：国家自然科学基金资助项目(51004005) 北京市自然科学基金资助项目(4122021) 北京市教委科技计划面上基金资助项目(051101904)
【分类号】：TG661

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