Neural network control-based adaptive design for a class of DC motor systems with the full state constraints
作者： Rui Bai
DOI: 10.1016/J.NEUCOM.2015.04.090
关键词: Artificial neural network 、 Compact space 、 Mathematics 、 Trajectory 、 State variable 、 Scheme (programming language) 、 Lyapunov function 、 DC motor 、 Control theory 、 Bounded function
摘要: In the paper, an adaptive neural controller for tracking problem of a direct-current (DC) motor is investigated. Because unknown functions are included in systems, networks used to estimate functions. this study, state variables DC required be constrained compact set. The main contribution paper that proposed scheme successfully integrate barrier Lyapunov function avoid violation constraints. Based on analysis, it proved output follows desired trajectory and all signals systems guaranteed bounded. A simulation result shown confirm effectiveness scheme.
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sci-hub.se 参考文章(49)
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