Various zeroing neural network (ZNN) models have been investigated to address the tracking control of robot manipulators for the capacity of parallel processing and nonlinearity handling. However, two limitations occur in the existing ZNN models. The first one is the convergence time that tends to be infinitely large. The second one is the research of robustness that remains in the analyses of stability and asymptotic convergence. To simultaneously enhance the convergence performance and robustness, this article proposes a new ZNN model by using a supertwisting (ST) algorithm, termed STZNN model, for the tracking control of mobile robot manipulators. The proposed STZNN model inherently possesses the advantages of finite-time convergence and robustness making the control process fast and robust. The bridge from the sliding mode control to the ZNN is built, and the essential connection between the ST algorithm and ZNN is explored by constructing a unified design process. Theorems and proofs about global stability, finite-time convergence, and robustness are provided. Finally, path-tracking applications, comparisons, and tests substantiate the effectiveness and superiority of the STZNN model for the tracking control handling of mobile robot manipulators.

中文翻译：

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一种适用于移动机器人机械手的新型超扭曲归零神经网络。

已经研究了各种归零神经网络 (ZNN) 模型来解决机器人机械手的跟踪控制，以实现并行处理和非线性处理的能力。然而，现有的 ZNN 模型存在两个限制。第一个是趋于无限大的收敛时间。二是稳定性和渐近收敛性分析中的鲁棒性研究。为了同时增强收敛性能和鲁棒性，本文提出了一种新的 ZNN 模型，该模型使用超扭曲 (ST) 算法，称为 STZNN 模型，用于移动机器人机械手的跟踪控制。所提出的 STZNN 模型固有地具有有限时间收敛性和鲁棒性的优点，使得控制过程快速而鲁棒。搭建了从滑模控制到ZNN的桥梁，通过构建统一的设计流程，探索了ST算法与ZNN之间的本质联系。提供了关于全局稳定性、有限时间收敛和鲁棒性的定理和证明。最后，路径跟踪应用、比较和测试证实了 STZNN 模型在移动机器人机械手跟踪控制处理方面的有效性和优越性。