Modelling, control, and stability analysis of non-linear systems using generalized fuzzy neural networks

International Journal of Systems Science

Volumn 34, Issue 6, 2003, Pages 427-438

  Gao, Yang ^a   Er, Meng Joo ^a  

^a NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

Author keywords

[No Author keywords available]

Indexed keywords

ASYMPTOTIC STABILITY; COMPUTER SIMULATION; FUZZY CONTROL; MANIPULATORS; NEURAL NETWORKS; SYSTEM STABILITY;

ADAPTIVE FUZZY NEURAL CONTROLLER (AFNC); STABILITY ANALYSIS;

NONLINEAR CONTROL SYSTEMS;

EID: 0344152811     PISSN: 00207721     EISSN: None     Source Type: Journal    
DOI: 10.1080/00207720310001612855     Document Type: Article

References (17)

1. CANNON, M. R., and SLOTINE, J. J. E., 1995, Space-frequency localized basis function networks for nonlinear system estimation and control. Neurocomputing, 9, 293-342.
2. CHAK, C. K., FENG, G., and MA, J., 1998, An adaptive fuzzy neural network for mimo system model approximation in high-dimensional spaces. IEEE Transactions on Systems, Man and Cybernetics, 28, 436-446.
3. CHEN, S., COWAN, C. F. N., and GRANT, P. M., 1991, Orthogonal least squares learning algorithm for radial basis function network. IEEE Transactions on Neural Networks, 2, 302-309.
4. ER, M. J., and CHIN, S. M., 2000, Hybrid adaptive fuzzy controllers of robot manipulators with bounds estimation. IEEE Transactions on Industrial Electronics, 47, 1151-1160.
5. ER, M. J., and GAO, Y., 2003, Robust adaptive control of robot manipulators using generalized fuzzy neural networks. IEEE Transactions on Industrial Electronics, 50, 620-628.
6. FENG, G., 1997, A new stable tracking control scheme for robotic manipulators. IEEE Transactions on System, Man and Cybernetics, 27, 510-516.
7. GAO, Y., and ER, M. J., 2001, Adaptive fuzzy neural control of multiple-link robot manipulators. International Journal of Robotics and Automation, 16, 172-182.
8. JIN, Y., 1998, Decentralized adaptive fuzzy control of robot manipulators. IEEE Transactions on System, Man, and Cybernetics, 28, 47-58.
9. JIN, Y., JIANG, J., and ZHU, J., 1995, Neural network based fuzzy identification with application to modelling and control of complex systems. IEEE Transactions on System, Man, and Cybernetics, 25, 990-997.
10. 3 fuzzy rule systems from data using evolution strategies. IEEE Transactions on System, Man, and Cybernetics, 29, 829-845.
11. LIN, F. J., HWANG, W. J., and WAI, R. J., 1999, A supervisory fuzzy neural network control system for tracking periodic input. IEEE Transactions on Fuzzy Systems, 7, 41-52.
12. MACKAY, D. J. C., 1995, Bayesian neural networks and density networks. Nuclear Instruments and Methods in Physics Research, 354, 73-80.
13. NIE, J., and LINKENS, D., 1995, Fuzzy-Neural Control: Principles, Algorithms and Applications (Englewood Cliffs: Prentice Hall).
14. OHTANI, Y., and YOSHIMURA, T., 1996, Fuzzy control of a manipulator using the concept of sliding mode. International Journal of Systems Science, 27, 1727-1731.
15. PARK, D., KANDEL, A., and LANGHOLZ, G., 1994, Genetic-based new fuzzy reasoning models with application to fuzzy control. IEEE Transactions on System, Man, and Cybernetics, 24, 39-47.
16. SLOTINE, J. J. E., and LI, W., 1991, Applied Nonlinear Control (Englewood Cliffs: Prentice Hall).
17. WANG, L. X., 1997, A Course in Fuzzy Systems and Control (Englewood Cliffs: Prentice Hall).