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Paper TuA07.6

Bouffard, Patrick Michael (University of California, Berkeley), Aswani, Anil (University of California at Berkeley), Tomlin, Claire (UC Berkeley)

Learning-Based Model Predictive Control on a Quadrotor: Onboard Implementation and Experimental Results

Scheduled for presentation during the Regular Session "Robust and Adaptive Control of Robotic Systems" (TuA07), Tuesday, May 15, 2012, 09:45−10:00, Meeting Room 7 (Remnicha)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on December 13, 2017

Keywords Learning and Adaptive Systems, Control Architectures and Programming, Aerial Robotics

Abstract

In this paper, we present details of the real time implementation onboard a quadrotor helicopter of learning-based model predictive control (LBMPC). LBMPC rigorously combines statistical learning with control engineering, while providing levels of guarantees about safety, robustness, and convergence. Experimental results show that LBMPC can learn physically based updates to an initial model, and how as a result LBMPC improves transient response performance. We demonstrate robustness to mis-learning. Finally, we show the use of LBMPC in an integrated robotic task demonstration---The quadrotor is used to catch a ball thrown with an a priori unknown trajectory.

 

 

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