A neural network controller for trajectory control of industrial robot manipulators

Journal of Computers

Volumn 3, Issue 8, 2008, Pages 1-8

  Jiang, Zhao Hui ^a   Ishita, Taiki ^b  

^a HIROSHIMA INSTITUTE OF TECHNOLOGY   (Japan)

^b Yushin Corporation   (Japan)

Author keywords

Neural network controller; Robot manipulator; Trajectory control

Indexed keywords

CONTROL SYSTEMS; FLEXIBLE MANIPULATORS; INDUSTRIAL MANIPULATORS; INDUSTRIAL ROBOTS; LINEAR CONTROL SYSTEMS; MANIPULATORS; MODULAR ROBOTS; NAVIGATION; NEURAL NETWORKS; ROBOT APPLICATIONS; ROBOTIC ARMS; ROBOTS; TRAJECTORIES;

APPLICATION EXAMPLES; DYNAMIC TRAJECTORIES; NETWORK TECHNOLOGIES; NEURAL NETWORK CONTROLLERS; ROBOT MANIPULATOR; TRAJECTORY CONTROL; TRAJECTORY TRACKING CONTROL; TRAJECTORY TRACKING ERRORS;

CONTROLLERS;

EID: 78649298107     PISSN: 1796203X     EISSN: None     Source Type: Journal    
DOI: 10.4304/jcp.3.8.1-8     Document Type: Article

References (13)

1. T.C.S Hsia, A new technique for robust control of servo systems, IEEE Transactions on Industrial Electronics, Vol. 36, No.1, pp.1-7 1989
2. C.Y. Kou, S. P. Wang, Nonlinear robust industrial robot control, Transactions ASME, Journal of Dynamic Systems, Measurement and Control, Vol. 111, No. 1, pp 24-30, 1989.
3. J. J. E. Slotine, W. P. Li, Composite adaptive control of robot manipulators, Automatica, Vol. 25, No. 4, p 509-519, 1989
4. M. W. Spong, On the Robust Control of Robot Manipulators, IEEE Transactions on Automatic Control, Vol. 37, no. 11, pp. 1782-1786, 1992
5. C. C. Cheah, C. Liu, and J. J. E. Slotine, Adaptive Tracking Control for Robots with Uncertainties in Kinematic, Dynamic and Actuator Models, IEEE Transactions on Automatic Control, Vol. 51 No.6, pp.1024-1029, 2006
6. C. Kwan, F. L. Lewis, Robust backstepping control of nonlinear systems using neural networks, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans., Vol. 30, No. 6, pp.753-766, 2000
7. T. D. Sanger, Neural network learning control of robot manipulators using gradually increasing task difficulty, IEEE Transactions on Robotics and Automation, Vol. 10, No. pp. 323-333, 1994
8. S. Jung and S. B. Yim, Experimental Studies of Neural Network Impedance Control for Robot Manipulators, Proceedings of IEEE International Conference on Robotics and Automation, pp.3453-3458, 2001.
9. W. S. Yu, and G. C. Wang, Adaptive Control Design Using Delayed Dynamical Neural Network for a Class of Nonlinear Systems, Proceedings of IEEE International Conference on Robotics and Automation, pp. 3447-3452, 2001.
10. Y. H. Kim, F. L. Lewis, Neural network output feedback control of robot manipulators, IEEE Transactions on Robotics and Automation, Vol. 15, No. 2, pp.301-309, 1999
11. S. Jung and T.C. Hsia, Neural Network Reference Compensation Technique for Position Control of Robot Manipulator, IEEE Conference on Neural Network, pp 1735-1741, June, 1996
12. O. Barambones, V. Etxebarria, Robust Neural Control for Robotic Manipulators, Automatica, Vol.38, pp. 235-242, 2002
13. C. M. Clark, J. K. Mills, Robotic System Sensitivity to Neural Network Learning Rate: Theory, Simulation, and Experiments, The International Journal of Robotics Research, Vol.19, No.10, pp.955-968, 2000