Publication:Iterative Learning of Feed-Forward Corrections for High-Performance Tracking

Publication: Iterative Learning of Feed-Forward Corrections for High-Performance Tracking


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  • Authors:
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    Mueller, F. L.; Schoellig, A. P.; D’Andrea, R.
  • Presented at: IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura, Algarve, Portugal, October 7-12, 2012
  • Publication date: 2012

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 nonrepetitive noise.

    Reference

    Posted on: August 27, 2012