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基于神经网络的非线性连续系统的自适应调节器

Adaptive Regulator of Nonlinear Continuous Systems Using Neural Networks
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摘要 对于一类连续时间的非线性动态系统 x=f(x) +Bu+d ,当系统中的非线性函数f(x)满足线性增长条件时 ,首先证明了f(x)中的x落入一紧集中 ,然后根据神经网络的逼近性质 ,给出了自适应调节器的设计方法 .利用李雅普诺夫稳定性理论 ,证明了控制算法是全局稳定的 。 For a class of nonlinear dynamical systems,when nonlinear function f(x) satisfies linear growth condtion. We first prove that x falls into a compact set, then the adaptive regulator is proposed based on the approximation capability of neural networks. According to the Lyapunov stability theory, the algorithm is proved to be globally stable, uniform ultimate boundedness of closed loop system is guaranteed.
出处 《控制理论与应用》 EI CAS CSCD 北大核心 2001年第z1期31-34,共4页 Control Theory & Applications
基金 山东省自然科学基金 (Y2 0 0 0G0 8)资助项目
关键词 自适应调节器 神经网络 线性增长 非线性系统 daptive regulation neural networks linear growth nonlinear systems.
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参考文献10

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