Terminal Recurrent Neural Networks for Time-Varying Reciprocal Solving With Application to Trajectory Planning of Redundant Manipulators

计算机科学 趋同(经济学) 基质(化学分析) 互惠的 控制理论(社会学) 职位(财务) 激活函数 弹道 人工神经网络 功能(生物学) 数学优化 算法 循环神经网络 控制(管理) 数学 人工智能 材料科学 经济 生物 复合材料 哲学 物理 天文 进化生物学 经济增长 语言学 财务
作者
Ying Kong,Yunliang Jiang,Xiaoyun Xia
出处
期刊:IEEE transactions on systems, man, and cybernetics [Institute of Electrical and Electronics Engineers]
卷期号:: 1-13 被引量:1
标识
DOI:10.1109/tsmc.2020.2998485
摘要

Time-varying matrix reciprocal problems are widely appeared in different matrix computations and engineering fields. Neural networks as a powerful tool have been developed to solve the time-varying problems. Recurrent neural networks (RNNs) are designed considering mainly for two aspects: 1) convergent precision and 2) convergent time. The core part of the existed neural methods is to design various kinds of activation function for time-varying matrix solving. However, most of the activation functions of neural networks are with infinite value, which demands long convergent time and are not applicable in practical engineering fields. This note proposes theoretical analyses and simulation results on the performance of terminal RNN (TRNN) and accelerated TRNN (ATRNN) with finite-time convergence, which is not only designed for constant matrix inversions but also for time-varying reciprocal matrix. Compared to the traditional RNNs, TRNNs are of limit-valued activation function and possess a finite time convergence property. The simulation results for time-varying reciprocal solving validate the perfect performance solved by TRNN and ATRNN. In addition, a quadratic program (QP) of velocity minimization based on TRNN is proposed to solve the trajectory tracking problems without considering the initial position error of the redundant manipulators. Finally, practical experiments of the redundant manipulators based on PUMA560 show the effectiveness and accuracy of the proposed approaches.
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