CAJ | 학술논문

对一类含时滞的单输入单输出的非线性系统提出了一种新的自适应神经网络控制方法.利用径向基神经网络来估计未知的连续函数.在设计中利用积分型Lyapunov-Krasovskii来消除未知的时滞.该文的结果的主要优点是:不仅有效地避免了控制奇异问题,也消除了对未知虚拟控制条件的限制.
대일류함시체적단수입단수출적비선성계통제출료일충신적자괄응신경망락공제방법.이용경향기신경망락래고계미지적련속함수.재설계중이용적분형Lyapunov-Krasovskii래소제미지적시체.해문적결과적주요우점시:불부유효지피면료공제기이문제,야소제료대미지허의공제조건적한제.
In this paper, adaptive neural control is proposed for a class of single-input single-output (SISO) nonlinear Unknown Time Delays systems. Radial basis function(RBF) neural networks (NNs) are utilized to estimate the unknown continuous functions. The unknown time delays are ompensated for using integral-type Lyapunov- Krasovskii functionals in the design. The main advantage of our result not only effciently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coeffcients.