在Wiggins S.所著的书《Global Bifurcations and Chaos》的第三章中分别讨论了在双曲奇点附近庞加莱映射与其线性逼近的误差,以及它们的导数之间的误差,即其证明了:P0-PL0=Ο(ε2)与DP0-DPL0=O(ε2).针对该本书中提出来的庞卡莱映射线...在Wiggins S.所著的书《Global Bifurcations and Chaos》的第三章中分别讨论了在双曲奇点附近庞加莱映射与其线性逼近的误差,以及它们的导数之间的误差,即其证明了:P0-PL0=Ο(ε2)与DP0-DPL0=O(ε2).针对该本书中提出来的庞卡莱映射线性逼近理论,构造出一个反例,通过利用等价关系和不等式等一些技巧,不仅说明了书中的上述两个逼近误差是错误的,而且指出了书中用来证明该线性逼近理论的引理都是不正确的.展开更多
A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback c...A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.展开更多
文摘在Wiggins S.所著的书《Global Bifurcations and Chaos》的第三章中分别讨论了在双曲奇点附近庞加莱映射与其线性逼近的误差,以及它们的导数之间的误差,即其证明了:P0-PL0=Ο(ε2)与DP0-DPL0=O(ε2).针对该本书中提出来的庞卡莱映射线性逼近理论,构造出一个反例,通过利用等价关系和不等式等一些技巧,不仅说明了书中的上述两个逼近误差是错误的,而且指出了书中用来证明该线性逼近理论的引理都是不正确的.
基金Project(61433004)suppouted by the National Natural Science Foundation of China
文摘A robust adaptive control is proposed for a class of uncertain nonlinear non-affine SISO systems. In order to approximate the unknown nonlinear function, an affine type neural network(ATNN) and neural state feedback compensation are used, and then to compensate the approximation error and external disturbance, a robust control term is employed. By Lyapunov stability analysis for the closed-loop system, it is proven that tracking errors asymptotically converge to zero. Moreover, an observer is designed to estimate the system states because all the states may not be available for measurements. Furthermore, the adaptation laws of neural networks and the robust controller are given based on the Lyapunov stability theory. Finally, two simulation examples are presented to demonstrate the effectiveness of the proposed control method. Finally, two simulation examples show that the proposed method exhibits strong robustness, fast response and small tracking error, even for the non-affine nonlinear system with external disturbance, which confirms the effectiveness of the proposed approach.
