Robust adaptive dynamical neural control for uncertain chaotic system

Wen Tan, Yaonan Wang, Shaowu Zhou, Zu-run Liu
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引用次数: 1

Abstract

Robust adaptive control of chaotic system with uncertainty was investigated in the presence of modeling error. The scheme of adaptation was based on identification estimates via dynamical neural networks. By using proposed nonlinear adaptive controller, the chaotic signal of the unknown system dynamics tends to be driven into a well controlled steady state. Moreover, the mathematical proof of stability properties of the system was guaranteed. Finally, simulation results have demonstrated the effectiveness of the proposed method through application on the Chen's chaotic system.
不确定混沌系统的鲁棒自适应动态神经控制
研究了存在建模误差的不确定混沌系统的鲁棒自适应控制问题。自适应方案基于动态神经网络的辨识估计。采用所提出的非线性自适应控制器,使未知系统动力学的混沌信号易于被驱动到一个可控的稳态。此外,还保证了系统稳定性的数学证明。最后,通过对陈氏混沌系统的仿真,验证了该方法的有效性。
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