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Adaptive Neural Network Control for a Class of Nonlinear Systems

机译:一类非线性系统的自适应神经网络控制

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An adaptive neural network control scheme is developed for perturbed nonlinear systems with unknown functions. To avoid the curse of dimensionality, dynamic surface-control (DSC) technique is introduced in the progress of controller design. Moreover, the problem of singularity is solved in estimation of the unknown functions by designing a novel strategy of estimation. It is shown that the DSC-based controller can ensure semi-global uniform ultimate bounded of the closed-loop system, and the tracking error can be arbitrarily small with appropriate design parameters. A simulation example is used to demonstrate the validness of the proposed algorithm.
机译:针对功能未知的摄动非线性系统,开发了一种自适应神经网络控制方案。为了避免出现尺寸诅咒,在控制器设计过程中引入了动态表面控制(DSC)技术。此外,通过设计一种新颖的估计策略,解决了未知函数估计中的奇异性问题。结果表明,基于DSC的控制器可以保证闭环系统的半全局一致极限极限,并且通过适当的设计参数可以使跟踪误差任意小。仿真实例验证了所提算法的有效性。

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