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首页> 外文期刊>IEEE / ASME Transactions on Mechatronics >Neural Network-Based Kinematic Inversion of Industrial Redundant Robots Using Cooperative Fuzzy Hint for the Joint Limits Avoidance
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Neural Network-Based Kinematic Inversion of Industrial Redundant Robots Using Cooperative Fuzzy Hint for the Joint Limits Avoidance

机译：基于神经网络的工业冗余机器人运动学反演，使用协同模糊提示避免关节极限

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摘要

In this paper, a neural network (NN)-based inverse kinematics problem of redundant manipulators subject to joint limits is presented. The Widrow–Hoff NN with an adaptive learning algorithm derived by applying Lyapunov stability theory is introduced. Since the inverse kinematics has an infinite number of joint angle vectors, a fuzzy neural network (FNN) is designed to provide an approximate value for that vector. This vector is fed into the NN as a hint input vector to guide the output of the NN within the self motion. This FNN is designed on the basis of cooperatively controlling each joint angle in the sense that it stops the motion on the critical axis at its limit at the expense of greater compensation from the most relaxed joint to accomplish the task. Physical constraints such as the joint velocity limits as well as the joint angle limits are incorporated into the method. Experiments are conducted for the PA-10 redundant manipulator to show the effectiveness of the proposed control system. A comparative study is carried out with the gradient projection method.
机译：本文提出了一种基于神经网络的冗余机械臂逆运动学问题。介绍了通过应用Lyapunov稳定性理论导出的具有自适应学习算法的Widrow-Hoff NN。由于逆运动学具有无限数量的关节角度矢量，因此设计了模糊神经网络（FNN）为该矢量提供近似值。该向量作为提示输入向量输入到NN中，以指导自运动内NN的输出。该FNN是在协同控制每个关节角度的基础上设计的，从某种意义上说，它可以在临界轴上将运动停止在其极限位置，但代价是从最放松的关节获得更大的补偿来完成任务。诸如关节速度极限以及关节角度极限之类的物理约束条件被合并到该方法中。对PA-10冗余机械手进行了实验，以证明所提出的控制系统的有效性。用梯度投影法进行了比较研究。

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