Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

IEEE Transactions on Neural Networks and Learning Systems
Guanyu LaiC L Philip Chen

Abstract

This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.

References

Sep 24, 2004·IEEE Transactions on Neural Networks·Shuzhi Sam Ge, Cong Wang
Mar 28, 2007·IEEE Transactions on Neural Networks·Changchun HuaPeng Shi
Dec 8, 2009·IEEE Transactions on Neural Networks·Travis Dierks, Sarangapani Jagannathan
Jan 15, 2010·IEEE Transactions on Neural Networks·Weisheng Chen, Licheng Jiao
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Nov 1, 2012·IEEE Transactions on Neural Networks and Learning Systems·Jeng-Tze Huang
May 9, 2014·IEEE Transactions on Neural Networks and Learning Systems·David NodlandSarangapani Jagannathan
Jun 27, 2014·IEEE Transactions on Cybernetics·Yan-Jun Liu, Shaocheng Tong
Nov 25, 2014·IEEE Transactions on Neural Networks and Learning Systems·Zhi LiuChun Lung Philip Chen
Dec 2, 2014·IEEE Transactions on Neural Networks and Learning Systems·Yan-Jun LiuDong-Juan Li

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