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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Observer-Based Adaptive Neural Networks Control for Large-Scale Interconnected Systems With Nonconstant Control Gains
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Observer-Based Adaptive Neural Networks Control for Large-Scale Interconnected Systems With Nonconstant Control Gains

机译：基于观察者的自适应神经网络控制，具有非合作控制增益的大型互联系统

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摘要

In this article, an adaptive neural network (NN) decentralized output-feedback control design is studied for the uncertain strict-feedback large-scale interconnected nonlinear systems with nonconstant virtual and control gains. NNs are utilized to approximate the unknown nonlinear functions, and the immeasurable states are estimated via designing an NN decentralized state observer. By constructing the logarithm Lyapunov functions, an observer-based NN adaptive decentralized backstepping output-feedback control is developed in the framework of the decentralized backstepping control. The proposed adaptive decentralized backstepping output-feedback control can make that the closed-loop system is semiglobally uniformly ultimately bounded (SGUUB) and that the tracking and observer errors converge to a small neighborhood of the origin. The most important contribution of this article is that it removes the restrictive assumption in the existing results that both virtual and control gain functions in each subsystem must be constants. A numerical simulation example is provided to validate the effectiveness of the proposed control method and theory.
机译：在本文中，研究了具有不合适的虚拟和控制增益的不确定严格反馈大规模互连的非线性系统研究了自适应神经网络（NN）分散的输出 - 反馈控制设计。 NNS用于近似未知的非线性功能，并且通过设计NN分散状态观察者估计无法估量的状态。通过构建对数Lyapunov函数，在分散的BackStepping控件的框架中开发了一种基于观察者的NN自适应分散的BackStepping输出反馈控制。所提出的自适应分散的BackStepping输出 - 反馈控制可以使闭环系统半球形均匀偏置（SGUB），并且跟踪和观察者错误会聚到原点的小邻域。本文最重要的贡献是，它会删除现有结果中的限制假设，即每个子系统中的虚拟和控制增益函数都必须是常量的。提供了数值模拟示例以验证所提出的控制方法和理论的有效性。

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