A Sliding Mode Control-Based on a RBF Neural Network for Deburring Industry Robotic Systems | Zendy

Yong Tao | Zendy; Jiaqi Zheng | Zendy; Yuanchang Lin | Zendy

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

Author(s) -

Yong Tao,

Jiaqi Zheng,

Yuanchang Lin

Publication year - 2016

Publication title -

international journal of advanced robotic systems

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.394

H-Index - 46

eISSN - 1729-8814

pISSN - 1729-8806

DOI - 10.5772/62002

Subject(s) - computer science , artificial neural network , control theory (sociology) , sliding mode control , scheme (mathematics) , control engineering , radial basis function , mode (computer interface) , robot , artificial intelligence , control (management) , engineering , nonlinear system , mathematics , mathematical analysis , physics , quantum mechanics , operating system

A sliding mode control method based on radial basis function (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

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