期刊
ISA TRANSACTIONS
卷 73, 期 -, 页码 208-226出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.isatra.2017.12.011
关键词
Unmanned helicopters; Distributed formation control; Finite-time multivariable neural network disturbance observer; Nonsingular fast terminal sliding mode; Integral filters
资金
- National Natural Science Foundation of China [61673294, 61573060, 61503323]
- Natural Science Foundation of Hebei Province [F2015202150, F2017203130]
- Natural Science Foundation of Tianjin [17JCQNJC04400]
- Youth Foundation of Hebei Educational Committee [QN2015068]
The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law, Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. (C) 2017 ISA. Published by Elsevier Ltd. All rights reserved.
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