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Adaptive neural tracking control of nonlinear stochastic switched non-lower triangular systems with input saturation

机译：输入饱和的非线性随机切换非下三角系统的自适应神经跟踪控制

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This paper focuses on the problem of a class of nonlinear stochastic switched non-lower triangular systems with input saturation. A novel adaptive neural tracking controller is developed by constructing the appropriately common Lyapunov function and applying backstepping technique. The difficulties in the design process are how to deal with the non-lower triangular structure and input saturation. In response to these questions, the variable separation technique is used to address the problem of non-lower triangular structure and the input saturation function is approximated by the efficient dynamical system. Anything else, neural networks, as universal function approximators, are employed to estimate the unknown continuous functions. Finally, it is shown that all signals in the resulting closed-loop system are uniformly bounded and the tracking error converges to a small neighbourhood around zero. In order to highlight the effectiveness of the presented control strategy, two vivid simulation examples are presented at the end. (C) 2019 Elsevier B.V. All rights reserved.
机译：本文重点研究一类具有输入饱和的非线性随机切换非下三角系统。通过构造适当的通用Lyapunov函数并应用反推技术，开发了一种新型的自适应神经跟踪控制器。设计过程中的难点是如何处理非下三角结构和输入饱和度。针对这些问题，使用了变量分离技术来解决非下三角结构的问题，并且通过有效的动力学系统来近似输入饱和函数。除此之外，神经网络作为通用函数逼近器可用于估计未知的连续函数。最终，表明所产生的闭环系统中的所有信号都是均匀有界的，并且跟踪误差收敛到零附近的小邻域。为了突出所提出的控制策略的有效性，最后给出了两个生动的仿真示例。 （C）2019 Elsevier B.V.保留所有权利。

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