中文摘要：

这份报纸与未知控制获得符号为交换非线性的系统探讨适应追踪控制计划。途径放松功能控制获得的上面的界限是已知的常数和外部骚乱的界限，神经网络的近似错误被知道的假设。RBF 神经网络(NN ) 习惯于近似未知函数，一个 H 无穷控制器被介绍提高坚韧性。适应更新法律和可被考虑的切换信号从交换多重 Lyapunov 功能方法被导出。它的证明结果关上了循环系统 asymptotically Lyapunov 马厩以便追踪错误性能和 H 无穷骚乱变细水平的输出很好被获得。最后，未击中系统强迫的一个模拟例子被给说明建议控制计划的有效性并且显著地改进短暂表演。

英文摘要：

This paper addresses the adaptive tracking control scheme for switched nonlinear systems with unknown control gain sign. The approach relaxes the hypothesis that the upper bound of function control gain is known constant and the bounds of external disturbance and approximation errors of neural＇networks are known. RBF neural networks （NNs） are used to approximate unknown functions and an H-infinity controller is introduced to enhance robustness. The adaptive updating laws and the admissible switching signals have been derived from switched multiple Lyapunov function method. It＇s proved that the resulting closed loop system is asymptotically Lyapunov stable such that the output tracking error performance and H-infinity disturbance attenuation level are well obtained. Finally, a simulation example of Forced Duffing systems is given to illustrate the effectiveness of the proposed control scheme and improve significantly the transient performance.