Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone
Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone
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DOI：
10.1002/acs.2300
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发表时间：
2013-04
影响因子：
3.1
通讯作者：
Huanqing Wang;Bing Chen;Chong Lin
中科院分区：
文献类型：
--
作者：
Huanqing Wang;Bing Chen;Chong Lin
研究了一类具有未知死区的非线性随机纯反馈系统的自适应神经跟踪控制问题。基于径向基函数神经网络的在线逼近能力，利用反推技术提出了一种新的自适应神经网络控制器。结果表明，所设计的控制器保证闭环系统的所有信号是半全局的、概率一致有界的，并且跟踪误差收敛到原点附近的一个任意小的四次均值邻域.仿真结果进一步验证了所提控制方案的有效性。版权所有© 2012约翰威利父子有限公司.
This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure‐feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed‐loop system are semi‐globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.