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GTCF based LARC contouring motion control of an industrial X-Y linear-motor-driven stage

机译:基于GTCF的工业X-Y直线电机驱动平台的LARC轮廓运动控制

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摘要

A global task coordinate frame (GTCF) based learning adaptive robust controller (LARC) is practically synthesized for an industrial X-Y linear-motor-driven stage to achieve not only good parametric adaptation ability and uncertain disturbance robustness, but also excellent transient/steady-state contouring performance. With the employment of GTCF which is globally based on the shape of the desired contour, the real-time contouring error calculation model is rather accurate and the coordination of multi-axes motion is guaranteed. Then a LARC control framework is proposed for the coupled system dynamics in GTCF, where the adaptive robust control term is to deal with the strongly coupled system model, and the iterative learning control term is to address the effect of unmodelled dynamics. Comparative experiments are carried out on an industrial linear-motor-driven stage, and the results consistently verify that the proposed GTCF based LARC controller has excellent contouring performance, and robustness to parametric variations and external disturbances.
机译:基于全局任务坐标框架(GTCF)的学习自适应鲁棒控制器(LARC)实际上是为工业XY线性电动机驱动的平台而合成的,不仅可以获得良好的参数自适应能力和不确定的干扰鲁棒性,而且还具有出色的瞬态/稳态轮廓加工性能。通过全局基于所需轮廓形状的GTCF,实时轮廓误差计算模型相当准确,并且可以保证多轴运动的协调性。然后为GTCF中的耦合系统动力学提出了一个LARC控制框架,其中自适应鲁棒控制项用于处理强耦合系统模型,而迭代学习控制项则用于解决非建模动力学的影响。在工业线性电动机驱动的平台上进行了比较实验,结果一致地证明了所提出的基于GTCF的LARC控制器具有出色的轮廓绘制性能,以及对参数变化和外部干扰的鲁棒性。

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