A Sliding Mode Control-based on a RBF Neural Network for Deburring Industry Robotic Systems

作者：Tao, Yong^*; Zheng, Jiaqi; Lin, Yuanchang

来源：International Journal of Advanced Robotic Systems, 2016, 13(1): 8.

DOI：10.5772/62002

• Sliding Mode Control (SMC)
• Radial Basis Function Neural Network (RBFNN)
• Radial Basis Function Neural Network Sliding Mode Control (RBFNN-SMC)
• Deburring Robotic Control

A sliding mode control method based on radial basis RBF) neural network is proposed for the deburring of industry robotic systems. First, a dynamic model for deburring the robot system is established. Then, a conventional SMC scheme is introduced for the joint position tracking of robot manipulators. The RBF neural network based sliding mode control (RBFNN-SMC) has the ability to learn uncertain control actions. In the RBFNN-SMC scheme, the adaptive tuning algorithms for network parameters are derived by a Koski function algorithm to ensure the network convergences and enacts stable control. The simulations and experimental results of the deburring robot system are provided to illustrate the effectiveness of the proposed RBFNN-SMC control method. The advantages of the proposed RBFNN-SMC method are also evaluated by comparing it to existing control schemes.

• 出版日期2016-1-22
• 单位中国科学院重庆绿色智能技术研究院; 北京航空航天大学; 武汉科技大学