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- W2071597614 abstract "In this paper, an observer-based adaptive neural network (OBANN) tracking control scheme is proposed for uncertain nonlinear systems with time-delays and external disturbances. The adaptive neural network model is used to approximate the dynamics of the nonlinear system, while an observer-based control scheme is to stabilize the system. By applying the adaptive neural dynamics, we can on-line tune the weights of the neurons of the neural model and the bounds of the gains of delay states directly using linear analytical results. From Lyapunov criterion and Riccati-inequality, it is shown that the stability of the closed-loop system is guaranteed and the closed loop system signals are uniform ultimate boundedness and achieve H¿ tracking performance. Finally, a numerical example of a two-links rolling cart is given to illustrate the effectiveness of the proposed control scheme." @default.
- W2071597614 created "2016-06-24" @default.
- W2071597614 creator A5021767539 @default.
- W2071597614 date "2013-07-01" @default.
- W2071597614 modified "2023-10-16" @default.
- W2071597614 title "An observer-based adaptive neural network tracking control for nonlinear systems" @default.
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- W2071597614 doi "https://doi.org/10.1109/icmlc.2013.6890806" @default.
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