摘要
针对一类严格反馈非线性时滞系统,提出了一种自适应神经网络动态面控制方案。通过引入一阶滤波器,避免了传统反演设计中的"计算膨胀"问题。通过构造恰当的Lyapunov-Krasovskii函数,对未知时滞项进行了补偿。此外,基于Lyapunov稳定性理论,证明了闭环系统所有信号半全局一致最终有界。最后,仿真实例表明了所提控制方案的有效性。
This paper investigates the adaptive neural network dynamic surface control problem for a class of strict-feedback nonlinear systems with unknown time delays.The problem of ＂explosion of complexity＂ in traditional backstepping design is avoided by introducing the first order filter.By constructing appropriate Lyapunov-Krasovskii functions,the unknown time delay terms have been compensated.Furthermore,based on Lyapunov theory,all signals in the closed loop system are guaranteed to be semi-globally uniformly ultimately bounded.Finally,simulation results are presented to demonstrate the effectiveness of the approach.
出处
《台州学院学报》
2011年第3期7-13,共7页
Journal of Taizhou University
关键词
自适应控制
神经网络
动态面控制
非线性时滞系统
adaptive control
neural network
dynamic surface control
nonlinear time delay system
