| 引用本文: | 余昭旭,杜红彬.基于神经网络的不确定随机非线性时滞系统自适应有界镇定[J].控制理论与应用,2010,27(7):855~860.[点击复制] |
| YU Zhao-xu,DU Hong-bin.Neural-network-based bounded adaptive stabilization for uncertain stochastic nonlinear systems with time-delay[J].Control Theory and Technology,2010,27(7):855~860.[点击复制] |
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| 基于神经网络的不确定随机非线性时滞系统自适应有界镇定 |
| Neural-network-based bounded adaptive stabilization for uncertain stochastic nonlinear systems with time-delay |
| 摘要点击 1763 全文点击 1154 投稿时间：2009-03-31 修订日期：2009-10-14 |
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| DOI编号 10.7641/j.issn.1000-8152.2010.7.CCTA090353 |
| 2010,27(7):855-860 |
| 中文关键词 自适应控制 神经网络 Backstepping 随机系统 时滞 |
| 英文关键词 adaptive control neural network(NN) Backstepping stochastic systems time-delay |
| 基金项目 国家自然科学基金青年基金资助项目(60704013); 上海市重点学科建设项目(B504). |
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| 中文摘要 |
| 针对一类不确定严格反馈随机非线性时滞系统的自适应有界镇定问题, 利用神经网络参数化和Backstepping方法, 提出一种新的且含较少学习参数的神经网络自适应控制策略, 以保证系统半全局随机有界. 稳定性分析证明闭环系统的所有误差信号概率意义下有界. 仿真结果表明所提出控制器设计方法的有效性. |
| 英文摘要 |
| The problem of bounded adaptive stabilization is investigated for a class of uncertain stochastic nonlinear strict-feedback systems with unknown time-delay. Based on the technique of neural-network(NN) parameterization and the Backstepping method, we develop a novel adaptive neural control scheme which contains fewer learning parameters to solve the stabilization problem of such systems. In addition, the stability analysis is given to show that all the error variables in the closed-loop system are bounded in probability. The effectiveness of the proposed design is verified by simulation results. |