Cited By
View all- Wu YSong QYang X(2007)Robust Recurrent Neural Network Control of Biped RobotJournal of Intelligent and Robotic Systems10.1007/s10846-007-9133-149:2(151-169)Online publication date: 1-Jun-2007
Design of Adaptive Robot Control System Using Recurrent Neural Network
Pages 247 - 261
Abstract
The use of a new Recurrent Neural Network (RNN) for controlling a robot manipulator is presented in this paper. The RNN is a modification of Elman network. In order to solve load uncertainties, a fast-load adaptive identification is also employed in a control system. The weight parameters of the network are updated using the standard Back-Propagation (BP) learning algorithm. The proposed control system is consisted of a NN controller, fast-load adaptation and PID-Robust controller. A general feedforward neural network (FNN) and a Diagonal Recurrent Network (DRN) are utilised for comparison with the proposed RNN. A two-link planar robot manipulator is used to evaluate and compare performance of the proposed NN and the control scheme. The convergence and accuracy of the proposed control scheme is proved.
References
[1]
1. Psaltis, D., Sideris, A., and Yamamura, A.: A multilayered neural network controller, IEEE Control Syst. Mag. (1989), 17-21.
[2]
2. Narendra, K. S. and Parthasarathy, K.: Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Netw. 1 (1990), 4-27
[3]
3. Pineda, F. J.: Dynamics and architecture for neural computation, J. Complex. 4 (1988), 216-245
[4]
4. Pearlmutter, B. A.: Learning state space trajectories in recurrent neural networks, Neural Comput. 1 (1989), 263-269.
[5]
5. Khemaissia, S. and Morris, A. S.: Neuro-adaptive control of robot manipulators, Robotica 11 (1993), 456-473.
[6]
6. Miyamoto, H., Kawato, M., Setoyama, T., and Suzuki, R.: Feedback-error-learning neural networks for trajectory control of a robot manipulator, Neural Netw. 1 (1988), 251-265.
[7]
7. Miller, W. T., Glanz, F. H., and Kraft, L. G.: CMAC: An associative neural network alternative to backpropagtion, Proc. IEEE 78 (1990), 1561-1567.
[8]
8. Kawato, M., Uno, Y., Isabe, M., and Suzuki, R.: Hierarchical neural network model for voluntary movement with application to robotics, IEEE Control Syst. Mag. (1988), 8-16.
[9]
9. Katic, D. M. and Vukobratovic, M. K.: Highly efficient robot dynamics learning by decomposed connectionist feedforward control structure, IEEE Trans. Syst. Man Cybern. 25(1) (1995), 145-158.
[10]
10. Ishiguro, A., Furuhashii, T., Okuma, S., and Uchikawa, Y.: A neural network compensator for uncertainties of robot manipulator, IEEE Trans. Ind. Electron. 39 (1992), 61-66.
[11]
11. Jung, S. and Hsia, T. C.: New neural network control technique for non-linear based robot manipulator control, IEEE Int. Conf. Syst., Man Cybernetics, Canada 3 (1995), 2928-2933.
[12]
12. Li, Q., Poo, A. N., and Ang, M.: An enhanced computed-torque control scheme for robot manipulators with a neuro-compensator, IEEE Int. Conf. Syst., Man Cybernetics, Canada 1 (1995), 56-60.
[13]
13. Elman, J. L.: Finding structure in time, Cogn. Sci. 14 (1990), 179-211.
[14]
14. Yildirim, S.: Neural network controller for cooperating robots, Electron. Lett. 37(22) (2001), 1351-1352.
[15]
15. Craig, J. J., Hsu P., and Sastry, S. S.: Adaptive control of mechanical manipulators, Int. J. Rob. Res. 6(6) (1987), 16-28.
[16]
16. Ku, C. C. and Lee, K. Y.: Diagonal recurrent neural networks for dynamic systems control, IEEE Trans. Neural Netw. 6 (1995), 144-156.
Index Terms
- Design of Adaptive Robot Control System Using Recurrent Neural Network
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Published: 01 November 2005
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View all- Wu YSong QYang X(2007)Robust Recurrent Neural Network Control of Biped RobotJournal of Intelligent and Robotic Systems10.1007/s10846-007-9133-149:2(151-169)Online publication date: 1-Jun-2007