Chen Dechao, Zhang Yunong
IEEE Trans Neural Netw Learn Syst. 2018 Sep;29(9):4385-4397. doi: 10.1109/TNNLS.2017.2764529. Epub 2017 Nov 9.
This paper proposes a novel robust zeroing neural-dynamics (RZND) approach as well as its associated model for solving the inverse kinematics problem of mobile robot manipulators. Unlike existing works based on the assumption that neural network models are free of external disturbances, four common forms of time-varying disturbances suppressed by the proposed RZND model are investigated in this paper. In addition, theoretical analyses on the antidisturbance performance are presented in detail to prove the effectiveness and robustness of the proposed RZND model with time-varying disturbances suppressed for solving the inverse kinematics problem of mobile robot manipulators. That is, the RZND model converges toward the exact solution of the inverse kinematics problem of mobile robot manipulators with bounded or zero-oriented steady-state position error. Moreover, simulation studies and comprehensive comparisons with existing neural network models, e.g., the conventional Zhang neural network model and the gradient-based recurrent neural network model, together with extensive tests with four common forms of time-varying disturbances substantiate the efficacy, robustness, and superiority of the proposed RZND approach as well as its time-varying disturbances suppression model for solving the inverse kinematics problem of mobile robot manipulators.
本文提出了一种新颖的鲁棒归零神经动力学（RZND）方法及其相关模型，用于解决移动机器人操纵器的逆运动学问题。与现有基于神经网络模型无外部干扰这一假设的工作不同，本文研究了所提出的RZND模型能够抑制的四种常见形式的时变干扰。此外，详细给出了抗干扰性能的理论分析，以证明所提出的RZND模型在抑制时变干扰的情况下解决移动机器人操纵器逆运动学问题的有效性和鲁棒性。也就是说，RZND模型收敛于具有有界或零定向稳态位置误差的移动机器人操纵器逆运动学问题的精确解。此外，与现有神经网络模型（如传统的张神经网络模型和基于梯度的递归神经网络模型）的仿真研究和综合比较，以及对四种常见形式时变干扰的广泛测试，证实了所提出的RZND方法及其时变干扰抑制模型在解决移动机器人操纵器逆运动学问题方面的有效性、鲁棒性和优越性。
