Computer Engineering and Applications ›› 2010, Vol. 46 ›› Issue (26): 238-240.DOI: 10.3778/j.issn.1002-8331.2010.26.072

• 工程与应用 • Previous Articles     Next Articles

Research on CMAC study control strategy for robotic visual servo system

SHI Yu-qiu1,SUN Wei2   

  1. 1.Department of Electronic Information and Control Engineering,Guangxi University of Technology,Liuzhou,Guangxi 545006,China
    2.College of Electrical and Information Engineering,Hunan University,Changsha 410082,China
  • Received:2009-02-24 Revised:2009-04-24 Online:2010-09-11 Published:2010-09-11
  • Contact: SHI Yu-qiu


石玉秋1,孙 炜2   

  1. 1.广西工学院 电子信息与控制工程系,广西 柳州 545006
    2.湖南大学 电气与信息工程学院,长沙 410082
  • 通讯作者: 石玉秋

Abstract: Cerebella Model Articulation Controller(CMAC) neural network has fast learning speed and can be used very well for real time system.A study control strategy for robotic visual servo system is introduced.In this strategy,CMAC is used as feedforward visual servo controller to compensate a general feedback controller.The proposed CMAC controller approximates the image Jacobin matrix that maps the 2D/3D relationships between image features and robotic joints movements.The on-line learning ability of CMAC can make the proposed system not sensitive to the camera calibrated errors and has strong robustness.The results of experiments prove the validity of the presented control system.

Key words: robot, visual servo, study control strategy, Cerebella Model Articulation Controller(CMAC)

摘要: 根据小脑模型关联控制器(CMAC)收敛速度快,适于实时控制系统的特点,设计了一种基于CMAC学习控制方法的机器人视觉伺服系统。在该系统中,CMAC被用作前馈视觉控制器对常规反馈控制器进行补偿。所提出的CMAC控制器替代图像雅可比矩阵来获得目标图像特征和机器人关节运动之间2D/3D变换关系,通过其在线学习,可以使系统对摄像机标定误差不敏感,从而提高系统的鲁棒性。实验证明了所设计控制系统的有效性。

关键词: 机器人, 视觉伺服, 学习控制, 小脑模型关联控制器

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