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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Decentralized Adaptive Command Filtered Neural Tracking Control of Large-Scale Nonlinear Systems: An Almost Fast Finite-Time Framework
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Decentralized Adaptive Command Filtered Neural Tracking Control of Large-Scale Nonlinear Systems: An Almost Fast Finite-Time Framework

机译：分散的Adaptive命令过滤大型非线性系统的神经跟踪控制：几乎快速的有限时间框架

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摘要

In this article, a decentralized adaptive finite-time tracking control scheme is proposed for a class of nonstrict feedback large-scale nonlinear interconnected systems with disturbances. First, a practical almost fast finite-time stability framework is established for a general nonlinear system, which is then applied to the design of the large-scale system under consideration. By fusing command filter technique and adaptive neural control and introducing two smooth functions, the "singular" and "explosion of complex" problems in the backstepping procedure are circumvented, while the obstacles caused by unknown interconnections are overcome. Moreover, according to the framework of practical almost fast finite-time stability, it is shown that all the closed-loop signals of the large-scale system are almost fast finite-time bounded, and the tracking errors can converge to arbitrarily small residual sets predefined in an almost fast finite time. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed finite-time decentralized control scheme.
机译：在本文中，提出了一种分散的自适应有限时间跟踪控制方案，用于一类具有扰动的非突出反馈大规模非线性互联系统。首先，为一般非线性系统建立了实用的几乎快速的有限时间稳定性框架，然后将其应用于所考虑的大规模系统的设计。通过定影命令滤波器技术和自适应神经控制并引入两个平滑函数，避免了反向手术过程中的“奇异”和“复杂的爆炸”问题，而克服了未知互连引起的障碍。此外，根据实际几乎快速有限的有限时间稳定性的框架，显示大规模系统的所有闭环信号几乎快速有限界限，并且跟踪误差可以收敛到任意小的残差集在几乎快速的有限时间内预定义。最后，提出了一种模拟示例以证明所提出的有限时间分散控制方案的有效性。

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