Infoscience

Conference paper

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

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

    • EPFL-CONF-181045

    Record created on 2012-08-27, modified on 2016-08-09

Fulltext

  • There is no available fulltext. Please contact the lab or the authors.

Related material