SCARA机器人自组织模糊聚类神经网络控制器

A Controller Based on Self-Organizing Fuzzy Clustering Neural Networks for SCARA Robotic Manipulators

本文提出了用于SCARA机器人运动控制的自组织模糊聚类神经网络控制器。该控制器基于模糊聚类方法在学习模糊规则之前先优化训练数据,去除冗余数据并解决数据冲突问题,不但减少了神经网络的计算负担,而且生成的规则更加适合机器人运动控制。控制器主要特点是能够动态地自组织结构,学习速度快,鲁棒性强。仿真结果表明控制效果很好。

This paper presents a self-organizing fuzzy clustering neural network （SOFCNN） controller suitable for motion control of SCARA robotic manipulators. The SOFCNN is based on the fuzzy clustering method optimizing training data before learning fuzzy rules, in order to remove redundant data and resolve conflicts in data. The method reduced computation of neural network and made the control rules more reasonable for the robotic manipulators. The feature of the SOFCNN controller has dynamic self-organizing structure, fast learning speed and flexibility in learning. The simulation results show that is very fine.