Adaptive sliding mode control with radial basis function neural network for time dependent disturbances and uncertainties
A radial basis function neural network (RBFNN) based adaptive sliding mode controller is presented in this paper to cater for a 3-DOF robot manipulator with time-dependent uncertainties and disturbance. RBF is one of the most popular intelligent methods to approximate uncertainties due to its simple...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Asian Research Publishing Network (ARPN)
2016
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/51750/1/adaptive_sliding_mode_control.pdf http://irep.iium.edu.my/51750/4/51750_Adaptive%20sliding%20mode%20control.pdf http://irep.iium.edu.my/51750/ http://www.arpnjournals.org/jeas/research_papers/rp_2016/jeas_0316_3932.pdf |
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| Summary: | A radial basis function neural network (RBFNN) based adaptive sliding mode controller is presented in this paper to cater for a 3-DOF robot manipulator with time-dependent uncertainties and disturbance. RBF is one of the most popular intelligent methods to approximate uncertainties due to its simple structure and fast learning capacity. By choosing a proper Lyapunov function, the stability of the controller can be proven and the update laws of the RBFN can be derived easily. Simulation test has been conducted to verify the effectiveness of the controller. The result shows that the controller has successfully compensate the time-varying uncertainties and disturbances with error less than 0.001 rad. |
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