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Paper FrA6.3

Purwin, Oliver (Kiva Systems), D'Andrea, Raffaello (ETHZ)

Performing Aggressive Maneuvers Using Iterative Learning Control

Scheduled for presentation during the Regular Sessions "Learning and Adaptive Systems - I" (FrA6), Friday, May 15, 2009, 09:10−09:30, Room: 404

2009 IEEE International Conference on Robotics and Automation, May 12 - 17, 2009, Kobe, Japan

This information is tentative and subject to change. Compiled on January 24, 2022

Keywords Learning and Adaptive Systems, Motion Control, Autonomous Agents

Abstract

This paper presents an algorithm to iteratively drive a system quickly from one state to another. A simple model which captures the essential features of the system is used to compute the reference trajectory as the solution of an optimal control problem. Based on a lifted domain description of that same model an iterative learning controller is synthesized by solving a linear least-squares problem. The non-causality of the approach makes it possible to anticipate recurring disturbances. Computational requirements are modest, allowing controller update in real-time. The experience gained from successful maneuvers can be used to significantly reduce transients when performing similar motions. The algorithm is successfully applied to a real quadrotor unmanned aerial vehicle. The results are presented and discussed.

 

 

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