Xia Youshen S, Feng Gang, Wang Jun
Nanjing University of Posts and Telecommunications, Nanjing, China.
IEEE Trans Syst Man Cybern B Cybern. 2005 Feb;35(1):54-64. doi: 10.1109/tsmcb.2004.839913.
This paper proposes a primal-dual neural network with a one-layer structure for online resolution of constrained kinematic redundancy in robot motion control. Unlike the Lagrangian network, the proposed neural network can handle physical constraints, such as joint limits and joint velocity limits. Compared with the existing primal-dual neural network, the proposed neural network has a low complexity for implementation. Compared with the existing dual neural network, the proposed neural network has no computation of matrix inversion. More importantly, the proposed neural network is theoretically proved to have not only a finite time convergence, but also an exponential convergence rate without any additional assumption. Simulation results show that the proposed neural network has a faster convergence rate than the dual neural network in effectively tracking for the motion control of kinematically redundant manipulators.
本文提出了一种具有单层结构的原始对偶神经网络，用于在线解决机器人运动控制中的约束运动冗余问题。与拉格朗日网络不同，所提出的神经网络可以处理物理约束，如关节极限和关节速度极限。与现有的原始对偶神经网络相比，所提出的神经网络实现复杂度低。与现有的对偶神经网络相比，所提出的神经网络无需矩阵求逆运算。更重要的是，理论证明所提出的神经网络不仅具有有限时间收敛性，而且在没有任何额外假设的情况下具有指数收敛速度。仿真结果表明，在所提出的神经网络在对运动学冗余机械手的运动控制进行有效跟踪时，其收敛速度比对偶神经网络更快。
