On correcting systematic errors without analyzing them by performing a repetitive task

Antti Autere
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Abstract

Describes a method for reducing systematic errors encountered between a true system behavior and the one predicted by a model. The structure of the model corresponds to the structure of the system only up to a certain limit. Error correcting is formulated as an optimization problem where the norm of the difference between the measured and the predicted system behavior is minimized. The solution is searched iteratively by doing the same task or experiment repeatedly and utilizing previously observed results. It is argued that the optimization approach may be useful in understanding the problems encountered in memory-based modeling, particularly in robot control. An iterative algorithm is given to correct the robot positioning errors. It is shown to converge to the right solution by making some general assumptions of the existing robot controller. An example is given with the PUMA robot where the precision of the arm movement is increased by repeatedly doing the movement task.<>
在不进行重复分析的情况下纠正系统错误
描述一种方法,用于减少真实系统行为与模型预测的系统行为之间遇到的系统误差。模型的结构只在一定限度内与系统的结构相对应。误差校正被表述为一个优化问题,其中测量和预测系统行为之间的差的范数是最小的。通过重复执行相同的任务或实验并利用先前观察到的结果来迭代地搜索解决方案。本文认为,优化方法可能有助于理解基于记忆的建模中遇到的问题,特别是在机器人控制中。给出了一种修正机器人定位误差的迭代算法。通过对现有机器人控制器的一些一般假设,证明了它收敛于正确的解。以PUMA机器人为例,通过重复执行运动任务来提高手臂运动的精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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