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• W2091219706 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." @default.
• W2091219706 created "2016-06-24" @default.
• W2091219706 creator A5050557198 @default.
• W2091219706 creator A5052147335 @default.
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• W2091219706 date "2012-10-01" @default.
• W2091219706 modified "2023-09-23" @default.
• W2091219706 title "Iterative learning of feed-forward corrections for high-performance tracking" @default.
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• W2091219706 doi "https://doi.org/10.1109/iros.2012.6385647" @default.
• W2091219706 hasPublicationYear "2012" @default.
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