贴片机X/Y轴快速高精度定位的实现

负载惯量和外界干扰是影响贴片机X,Y轴快速高精度定位的两个关键因素。本文针对负载惯量和外界干扰对控制性能的影响,提出了基于RBF神经网络的自适应滑模控制算法。利用RBF神经网络的万能逼近特性实现对外加干扰和被控对象模型信息的逼近,运用自适应控制算法计算前馈补偿量以补偿负载惯量和摩擦力对运动性能的影响,采用滑模控制算法以抑制其他不确定干扰对运动控制的影响。通过仿真分析可以得出,所采用的控制算法能够有效地补偿负载惯量和外界干扰对定位性能的影响,从而实现贴片机X,Y轴的快速高精度定位。

High-precision rapid positioning of X/Y axis in SMT placement

The load inertia and the outside disturbance are the key factors that affect X/Y-axis high-precision fast position of SMT placement. An adaptive sliding mode control algorithm based on RBF neural network is proposed in this paper to eliminate the influence of load inertia and outside interference. The universal approximation property of RBF neural network is used to realize approximation of external disturbance and model information of the controlled object. Feed-forward compensation amount calculat-ed by using the adaptive control algorithm is used to compensate the load moment of inertia and friction effects on motion perfor-mance. The sliding mode control algorithm is adopted to suppress the influence of other uncertain disturbance on motion control. The simulation analysis indicates that the control algorithm can effectively compensate the efforts of load inertia and outside inter-ference on positioning performance,so as to realize the high-precision rapid positioning of X- and Y-axis in SMT placement.