This article addresses the issue of adaptive intelligent asymptotic tracking control for a class of stochastic nonlinear systems with unknown control gains and full state constraints. Unlike the existing systems in the literature in which the prior knowledge of the control gains is available for the controller design, the salient feature of our considered system is that the control gains are allowed to be unknown but have a positive sign. By introducing an auxiliary virtual controller and employing the new properties of Numbness functions, the major technique difficulty arising from the unknown control gains is overcome. At the same time, the -type barrier Lyapunov functions are introduced to prevent the violation of the state constraints. What's more, neural networks' universal online approximation ability and gain suppression inequality technology are combined in the frame of adaptive backstepping design, so that a new control method is proposed, which cannot only realize the asymptotic tracking control in probability, but also meet the requirement of the full state constraints imposed on the system. In the end, the simulation results for a practical example demonstrate the effectiveness of the proposed control method.

中文翻译：

---

一类具有未知控制增益和全状态约束的随机非线性系统的自适应神经网络渐近跟踪控制

本文解决了一类具有未知控制增益和全状态约束的随机非线性系统的自适应智能渐近跟踪控制问题。与文献中控制增益的先验知识可用于控制器设计的现有系统不同，我们考虑的系统的显着特征是允许控制增益未知但具有正号。通过引入辅助虚拟控制器并利用麻木函数的新特性，克服了未知控制增益带来的主要技术难点。同时，该引入 - 型势垒李雅普诺夫函数以防止违反状态约束。并且在自适应反步设计的框架内结合神经网络的通用在线逼近能力和增益抑制不等式技术，提出了一种新的控制方法，既能实现概率上的渐近跟踪控制，又能满足要求强加于系统的全状态约束。最后，一个实例的仿真结果证明了所提出的控制方法的有效性。