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Mathematical Problems in Engineering
Volume 2015, Article ID 905184, 12 pages
Research Article

Extreme Learning Machine Assisted Adaptive Control of a Quadrotor Helicopter

1School of Aeronautics and Astronautics, Zhejiang University, Zhejiang, Hangzhou 310027, China
2State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Liaoning, Shenyang 110189, China
3Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received 21 August 2014; Revised 10 December 2014; Accepted 14 December 2014

Academic Editor: Yi Jin

Copyright © 2015 Yu Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Control of quadrotor helicopters is difficult because the problem is naturally nonlinear. The problem becomes more challenging for common model based controllers when unpredictable uncertainties and disturbances in physical control system are taken into account. This paper proposes a novel intelligent controller design based on a fast online learning method called extreme learning machine (ELM). Our neural controller does not require precise system modeling or prior knowledge of disturbances and well approximates the dynamics of the quadrotor at a fast speed. The proposed method also incorporates a sliding mode controller for further elimination of external disturbances. Simulation results demonstrate that the proposed controller can reliably stabilize a quadrotor helicopter in both agitated attitude and position control tasks.