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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators
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A Novel Supertwisting Zeroing Neural Network With Application to Mobile Robot Manipulators

机译：具有应用于移动机器人机械手的新型超级超级归零神经网络

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摘要

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.
机译：已经研究了各种归零神经网络（ZnN）模型以解决机器人操纵器的跟踪控制，以实现并行处理和非线性处理的容量。但是，在现有ZnN模型中发生了两个限制。第一个是往往是无限大的收敛时间。第二个是对稳定性和渐近收敛分析的鲁棒性的研究。为了同时增强收敛性能和鲁棒性，本文通过使用SuperTwisting（ST）算法，称为STZNN模型，用于移动机器人操纵器的跟踪控制来提出新的ZnN模型。所提出的STZNN模型本身具有有限时间收敛和稳健性的优点，使控制过程快速且坚固。构建了从滑动模式控制到ZnN的桥梁，通过构建统一的设计过程，探讨了ST算法和ZnN之间的基本连接。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据。最后，路径跟踪应用，比较和测试证实了STZNN模型的效果和优越性，用于移动机器人操纵器的跟踪控制处理。

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