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实现前所未有的运动性能水平。

采用全新的基于学习的控制算法。

滚动开始

超越传统控制的极限

基于反馈和前馈的控制算法可以实现高水平的性能,但存在根本性限制。

跟踪误差

预防性干扰补偿

针对高精度运动系统的机器学习控制算法,可以预先补偿干扰,实现反馈和前馈控制规律的智能实时优化。

跟踪误差

多种学习模式提升性能

学习不同先前的执行模式,可以最小化运动和稳定时间,减少动态跟随误差,并在广泛的操作范围内最大化稳定性。

基于学习的运动控制软件屏幕1 PI
基于学习的运动控制软件屏幕2 PI
基于学习的运动控制软件屏幕3 PI
笔记本电脑正面
基于学习的运动控制视觉对象PI

实现更高的运动性能水平。

“推动我们前进的动力是实现他人认为不可能的运动性能”

工程师Fabian Rudnick深入探讨了PI基于学习的运动控制的未来开发过程。

推动市场和应用

使用基于机器学习的控制算法,从先前的执行过程中进行学习,从而避免或补偿重复误差。

PI的光子学市场 光子学
PI的半导体市场 半导体
PI的显微镜和生命科学市场 显微镜和生命科学
PI的工业自动化市场 工业自动化
基于学习的运动控制视觉对象PI

联系我们

我们期待您的项目,与您共同迎接挑战并携手推动技术极限的发展。