Neural-network-based decentralized adaptive output-feedback control for large-scale stochastic nonlinear systems.

Qi Zhou, Peng Shi, Honghai Liu, Shengyuan Xu
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引用次数: 276

Abstract

This paper focuses on the problem of neural-network-based decentralized adaptive output-feedback control for a class of nonlinear strict-feedback large-scale stochastic systems. The dynamic surface control technique is used to avoid the explosion of computational complexity in the backstepping design process. A novel direct adaptive neural network approximation method is proposed to approximate the unknown and desired control input signals instead of the unknown nonlinear functions. It is shown that the designed controller can guarantee all the signals in the closed-loop system to be semiglobally uniformly ultimately bounded in a mean square. Simulation results are provided to demonstrate the effectiveness of the developed control design approach.

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大规模随机非线性系统的神经网络分散自适应输出反馈控制。
研究了一类非线性严格反馈大型随机系统的基于神经网络的分散自适应输出反馈控制问题。采用动态曲面控制技术,避免了反推设计过程中计算量的爆炸。提出了一种新的直接自适应神经网络逼近方法来逼近未知和期望的控制输入信号,而不是未知的非线性函数。结果表明,所设计的控制器能保证闭环系统中所有信号最终半全局一致有界于均方。仿真结果验证了所提出的控制设计方法的有效性。
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