基于神经网络的高阶随机非线性系统的状态反馈控制

闵惠芳; 段纳

doi:10.3724/SP.J.1004.2014.02968

基于神经网络的高阶随机非线性系统的状态反馈控制

doi: 10.3724/SP.J.1004.2014.02968

闵惠芳,
段纳

1.
江苏师范大学电气工程及其自动化学院徐州 221116

基金项目:

Supported by National Natural Science Foundation of China (61104222, 61305149), Natural Science Foundation of Jiangsu Province (BK2011205), 333 High-Level Talents Training Program in Jiangsu Province, Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province (11KJB510026), and Natural Science Foundation of Jiangsu Normal University (11XLR08)

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出版历程
- 收稿日期: 2013-09-05
- 修回日期: 2014-06-10
- 刊出日期: 2014-12-20

Nonlinear Control for Multi-agent Formations with Delays in Noisy Environments

1.
School of Electrical Engineering &Automation, Jiangsu Normal University, Xuzhou 221116, China

Funds:

摘要

摘要: 基于神经网络(NN)研究了一类含有未知非线性项的高阶随机不确定系统的自适应状态反馈控制问题. 通过引入径向基函数神经网络(RBF NN) 逼近方法, 运用 backstepping 技术以及选择合适的 Lyapunov 函数, 我们构造了一个自适应状态反馈控制器使得闭环系统是半全局一致最终有界的. 仿真例子验证了设计方法的有效性.
- 高阶随机非线性系统 /
- 状态反馈控制 /
- 神经网络 /
- 反推(backstepping)
Abstract: This paper focuses on investigating the issue of adaptive state-feedback control based on neural networks (NNs) for a class of high-order stochastic uncertain systems with unknown nonlinearities. By introducing the radial basis function neural network (RBFNN) approximation method, utilizing the backstepping method and choosing an approximate Lyapunov function, we construct an adaptive state-feedback controller which assures the closed-loop system to be mean square semi-global-uniformly ultimately bounded (M-SGUUB). A simulation example is shown to illustrate the effectiveness of the design scheme.
- High-order stochastic nonlinear systems /
- state-feedback control /
- neural networks /
- backstepping

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参考文献(14)

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