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首页> 外文期刊>IEEE Transactions on Circuits and Systems. I, Regular Papers >Simple learning control made practical by zero-phase filtering:applications to robotics
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Simple learning control made practical by zero-phase filtering:applications to robotics

机译：零相位滤波使简单的学习控制变得切实可行：机器人技术的应用

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摘要

Iterative learning control (ILC) applies to control systems that perform the same finite-time tracking command repeatedly. It iteratively adjusts the command from one repetition to the next in order to reduce the tracking error. This creates a two-dimensional (2-D) system, with time step and repetition number as independent variables. The simplest form of ILC uses only one gain times one error in the previous repetition, and can be shown to converge to the zero-tracking error independent of the system dynamics. Hence, it appears very effective from a mathematical perspective. However, in practice, there are unacceptable learning transients. A zero-phase low-pass filter is introduced here to eliminate the worst transients. The main purpose of this paper is to supply a presentation of experiments on a commercial robot that demonstrate the effectiveness of this approach, improving the tracking accuracy of the robot performing a high speed maneuver by a factor of 100 in six repetitions. Experiments using a two-gain ILC reaches this error level in only three iterations. It is suggested that these two simple ILC laws are the equivalent for learning control of proportional and PD control in classical control system design. Thus, what was an impractical approach, becomes practical, easy to apply, and effective
机译：迭代学习控制（ILC）适用于重复执行相同的有限时间跟踪命令的控制系统。迭代地将命令从一个重复调整到下一个重复，以减少跟踪错误。这将创建一个二维（2-D）系统，其中时间步长和重复次数为自变量。 ILC的最简单形式是在上一次重复中仅使用一个增益乘以一个误差，并且可以证明收敛到零跟踪误差，而与系统动态无关。因此，从数学角度看，它看起来非常有效。但是，实际上，存在不可接受的学习过渡。这里引入了零相低通滤波器，以消除最坏的瞬变。本文的主要目的是在商用机器人上提供实验演示，以证明这种方法的有效性，并在六次重复中将执行高速机动的机器人的跟踪精度提高了100倍。使用两增益ILC的实验仅在三个迭代中就达到了此错误级别。建议在经典控制系统设计中，这两个简单的ILC律等效于比例控制和PD控制的学习控制。因此，一种不切实际的方法变得实用，易于应用且有效

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