In this paper, the issue of adaptive neural control is discussed for a class of stochastic nonstrict‐feedback constrained nonlinear systems with input and state unmodeled dynamics. A dynamic signal produced by the first‐order auxiliary system is employed to deal with the dynamical uncertain terms. Radial basis function neural networks are used to reconstruct unknown nonlinear continuous functions. With the help of the mean value theorem and Young's inequality, only one learning parameter is adjusted online at recursive each step. Using the hyperbolic tangent function as nonlinear mapping, the output constrained stochastic nonstrict‐feedback system in the presence of unmodeled dynamics is transformed into a novel unconstrained stochastic nonstrict‐feedback system. Based on dynamic surface control technology and the property of Gaussian function, adaptive neural control is developed for the transformed stochastic nonstrict‐feedback system. The output abides by stochastic constraints in probability. By the Lyapunov method, all signals of the closed‐loop control system are proved to be semi‐global uniform ultimate bounded (SGUUB) in probability. The obtained theoretical findings are verified by two numerical examples.

中文翻译：

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具有输入和状态未建模动力学的随机非严格反馈约束非线性系统的自适应动态表面控制

本文针对一类具有输入和状态未建模动力学的随机非严格反馈约束非线性系统，讨论了自适应神经控制的问题。一阶辅助系统产生的动态信号用于处理动态不确定项。径向基函数神经网络用于重构未知的非线性连续函数。借助于平均值定理和杨氏不等式，每步递归仅在线调整一个学习参数。使用双曲正切函数作为非线性映射，在存在未建模动力学的情况下将输出约束随机非严格反馈系统转换为新颖的无约束随机非严格反馈系统。基于动态表面控制技术和高斯函数的性质，针对转换后的随机非严格反馈系统开发了自适应神经控制。输出遵守概率的随机约束。通过李雅普诺夫方法，证明闭环控制系统的所有信号的概率都是半全局一致的极限极限（SGUUB）。通过两个数值例子验证了所获得的理论发现。