鲁棒滑模递归神经网络CNC机X-Y平面控制

CNC Machine X-Y Table Control of Robust Recurrent-Neural-Network Sliding-Mode

鲁棒递归神经网络的滑模控制能跟踪参考轮廓的双轴运动机构,双轴运动机构是由2个矢量控制永磁同步电机驱动的计算机控制的X-Y平面的数字控制机。RRNN滑模控制系统是基于分别控制X轴和Y轴运动获得的近似极佳的动力特性,其运动跟踪性能通过使用对参考变化、外来干扰正交耦合干涉和摩擦转矩的鲁棒性的控制技术而得到改善,在圆形参考轮廓上提供的实验结果表明,所提出的控制系统的动态特性的抗干扰性达到可不考虑所存在的不确定性。

Robust recurrent-neural- network（RRNN） sliding-mode control may track biaxial motion mechanism of reference contour. The biaxial motion mechanism is a computer-controlled X-Ytable numerical machine that is driven by two vector controlled permanent-magnet synchronous motors. A RRNN sliding-mode control SYStem is based on the excellent dynamic characteristics derived from controlling X-axis and Y-axis respectively. The motion tracking performance is significantly improved through employing robustness control techniques of parameter variations, external disturbances, cross coupled interference and frictional torque. Experimental results on circular reference contours show that the dynamic behaviour of the antidisturbance of the proposed control system reaches without considering existing uncertainties.