Iterative learning of feed-forward corrections for high-performance tracking

Fabian L. Mueller, Angela P. Schoellig, R. D’Andrea
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引用次数: 50

Abstract

We revisit a recently developed iterative learning algorithm that enables systems to learn from a repeated operation with the goal of achieving high tracking performance of a given trajectory. The learning scheme is based on a coarse dynamics model of the system and uses past measurements to iteratively adapt the feed-forward input signal to the system. The novelty of this work is an identification routine that uses a numerical simulation of the system dynamics to extract the required model information. This allows the learning algorithm to be applied to any dynamic system for which a dynamics simulation is available (including systems with underlying feedback loops). The proposed learning algorithm is applied to a quadrocopter system that is guided by a trajectory-following controller. With the identification routine, we are able to extend our previous learning results to three-dimensional quadrocopter motions and achieve significantly higher tracking accuracy due to the underlying feedback control, which accounts for non-repetitive noise.
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高性能跟踪前馈修正的迭代学习
我们回顾了最近开发的迭代学习算法,该算法使系统能够从重复操作中学习,目标是实现给定轨迹的高跟踪性能。学习方案基于系统的粗动力学模型,并使用过去的测量来迭代地适应系统的前馈输入信号。这项工作的新颖之处在于一个识别程序,它使用系统动力学的数值模拟来提取所需的模型信息。这使得学习算法可以应用于任何动态模拟可用的动态系统(包括具有底层反馈回路的系统)。将所提出的学习算法应用于由轨迹跟踪控制器引导的四旋翼飞行器系统。通过识别例程,我们能够将以前的学习结果扩展到三维四轴飞行器运动,并由于潜在的反馈控制而实现显着更高的跟踪精度,这说明了非重复噪声。
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