Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone
Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone
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DOI：
10.3969/j.issn.1004-4132.2011.03.020
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发表时间：
2011-06
影响因子：
2.1
通讯作者：
Zhaoxu Yu;H. Du
中科院分区：
文献类型：
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作者：
Zhaoxu Yu;H. Du
针对一类具有未知死区和未知增益函数的不确定随机非线性严格反馈系统，解决了自适应稳定问题。通过使用反步法和神经网络（NN）参数化，开发了一种包含较少学习参数的新型自适应神经控制方案来解决此类系统的稳定性问题。同时，提出了稳定性分析，以保证所有误差变量半全局一致地最终以紧凑集合中的期望概率为界。仿真结果说明了所提出设计的有效性。
The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions. By using the backstepping method and neural network (NN) parameterization, a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems. Meanwhile, stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set. The effectiveness of the proposed design is illustrated by simulation results.