• ISSN 0258-2724
  • CN 51-1277/U
  • EI Compendex
  • Scopus
  • Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
  • Chinese S&T Journal Citation Reports
  • Chinese Science Citation Database
Volume 31 Issue 3
Jun.  2018
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Article Contents
MAO Run, GAO Hongli, SONG Xingguo. RBF Neural Network Robot Manipulator Control Based on Fuzzy Compensation[J]. Journal of Southwest Jiaotong University, 2018, 53(3): 638-645. doi: 10.3969/j.issn.0258-2724.2018.03.027
Citation: MAO Run, GAO Hongli, SONG Xingguo. RBF Neural Network Robot Manipulator Control Based on Fuzzy Compensation[J]. Journal of Southwest Jiaotong University, 2018, 53(3): 638-645. doi: 10.3969/j.issn.0258-2724.2018.03.027

RBF Neural Network Robot Manipulator Control Based on Fuzzy Compensation

doi: 10.3969/j.issn.0258-2724.2018.03.027
  • Received Date: 23 Mar 2016
  • Publish Date: 01 Jun 2018
  • For achieving high precision trajectory tracking control of robot manipulators, a control strategy based on fuzzy logic compensation for radial basis function (RBF) neural networks has been proposed. First, the output of a proportional-integral (PD) controller was used in conjunction with an RBF neural network for obtaining a dynamic model of the robot manipulator system. A fuzzy compensator was then introduced for addressing the modelling errors and external disturbances. Furthermore, the fuzzy compensator control scheme and the non-fuzzy compensator control scheme were applied to two-degrees-of-freedom robot manipulators through simulation with MATLAB and Simulink, and the root mean square deviation of tracking errors was thereby measured. The obtained results show that the control accuracies of the first and second joints of the robot manipulators can be improved by 60.8% and 71.4%, respectively. Furthermore, the proposed control scheme can be applied to such robot manipulators which cannot be precisely modelled by compensating the non-modelled part and external disturbances of the system.

     

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