Adaptive neural network control of helicopters

Authors:
Shuzhi Sam Ge;Keng-Peng Tee
Affiliations:
Department of Electrical & Computer Engineering, National University of Singapore, Singapore;Department of Electrical & Computer Engineering, National University of Singapore, Singapore
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 6
Cited 0

Adaptive NN control of uncertain nonlinear pure-feedback systems

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Nonlinear modelling and control of helicopters

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Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks

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Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback

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Helicopter trimming and tracking control using direct neural dynamic programming

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Adaptive neural control of uncertain MIMO nonlinear systems

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Abstract

In this paper, we propose robust adaptive neural network (NN) control for helicopter systems by using the Implicit Function Theorem and the Mean Value Theorem, which are useful tools for handling nonlinear nonaffine systems. We focus on single-input single-output (SISO) helicopter systems, which are exemplified by certain single-channel modes of operation, such as vertical flight and pitch regulation, and also by special conditions under which the multiple channels become decoupled. It is shown that under the proposed NN control, the output tracking error converges to a small neighbourhood of the origin, while all closed loop signals are Semi-Globally Uniformly Ultimately Bounded (SGUUB).