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Design of RBF Neural Robust Controller with Differencial Reconstruction and for a Class of Nonlinear Uncertain Chaotic Systems

机译：一类非线性不确定混沌系统的带差分重构的RBF神经鲁棒控制器设计

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摘要

An adaptive neural robust controller was designed by using adaptive backstepping method for a class of nonlinear uncertain chaotic systems which could be turned to "standard block control type", Furthermore, It is possible to make the network more stable and make the selection of simulation parameter more easy due to the introduction of differential reconstruction which increased the damp of the system. The known dynamics are used to design a feedback controller which ensure the stability of the system. A neural network-based adaptive compensator is designed for compensation of the system uncertainties. It was proved by constructing Lyapunov function step by step that all signals of the system are bounded and exponentially converge to the neighborhood of the origin globally
机译：针对一类非线性不确定混沌系统，采用自适应反步法设计了一种自适应神经鲁棒控制器，可以转化为“标准块控制型”，而且可以使网络更加稳定，并可以选择仿真参数。由于引入了差分重建，因此更加容易，从而增加了系统的阻尼。已知的动力学用于设计确保系统稳定性的反馈控制器。基于神经网络的自适应补偿器设计用于补偿系统不确定性。通过逐步构造李雅普诺夫函数证明，系统的所有信号都是有界的，并且在全局范围内呈指数收敛于原点的邻域。

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