CAJ | 학술논문

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。
침대일류엄격반궤비선성시체계통,제출료일충자괄응신경망락동태면공제방안。통과인입일계려파기,피면료전통반연설계중적＂계산팽창＂문제。통과구조흡당적Lyapunov-Krasovskii함수,대미지시체항진행료보상。차외,기우Lyapunov은정성이론,증명료폐배계통소유신호반전국일치최종유계。최후,방진실례표명료소제공제방안적유효성。
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.