This paper investigates a scheme of adaptive neural network control for a stochastic switched system with input saturation. The unknown smooth nonlinear functions are approximated directly by neural networks. A modified approach is proposed to deal with unknown functions with nonstrict feedback form in the design process. Furthermore, by combining the auxiliary design signal and the adaptive backstepping design, a valid adaptive neural tracking controller design algorithm is presented such that all the signals of the switched closed-loop system are in probability semiglobally, uniformly, and ultimately bounded, and the tracking error eventually converges to a small neighborhood of the origin in probability. In the end, the effectiveness of the proposed method is verified by a simulation example.

中文翻译：

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输入饱和的切换系统自适应神经网络控制方案

本文研究了一种具有输入饱和度的随机切换系统的自适应神经网络控制方案。未知的平滑非线性函数直接由神经网络近似。提出了一种改进的方法来处理设计过程中具有非严格反馈形式的未知功能。此外，通过将辅助设计信号和自适应反步设计相结合，提出了一种有效的自适应神经跟踪控制器设计算法，从而使开关闭环系统的所有信号均具有半全局，均匀且最终有界的概率，并且可以进行跟踪错误最终会收敛到原点的一小部分邻域中。最后，通过仿真实例验证了所提方法的有效性。