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Exponential Stabilization for Sampled-Data Neural-Network-Based Control Systems

机译:基于采样数据神经网络的控制系统的指数镇定

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摘要

This paper investigates the problem of sampled-data stabilization for neural-network-based control systems with an optimal guaranteed cost. Using time-dependent Lyapunov functional approach, some novel conditions are proposed to guarantee the closed-loop systems exponentially stable, which fully use the available information about the actual sampling pattern. Based on the derived conditions, the design methods of the desired sampled-data three-layer fully connected feedforward neural-network-based controller are established to obtain the largest sampling interval and the smallest upper bound of the cost function. A practical example is provided to demonstrate the effectiveness and feasibility of the proposed techniques.
机译:本文研究了具有最优保证成本的基于神经网络的控制系统的采样数据稳定问题。利用时间相关的Lyapunov函数方法,提出了一些新颖的条件来保证闭环系统指数稳定,该条件充分利用了有关实际采样模式的可用信息。基于导出的条件,建立了所需的采样数据三层全连接前馈神经网络控制器的设计方法,以获得最大的采样间隔和最小的成本函数上限。提供了一个实际的例子来证明所提出的技术的有效性和可行性。

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