Identification and Decentralized Adaptive Control Using Dynamical Neural Networks with Application to Robotic Manipulators

IEEE Transactions on Neural Networks

Volumn 4, Issue 6, 1993, Pages 919-930

  Karakaşoğlu, Ahmet ^a   Sudharsanan, Subramania I ^a,b   Sundareshan, Malur K ^a  

^a UNIVERSITY OF ARIZONA   (United States)

^b IBM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; LEARNING SYSTEMS; MANIPULATORS; NEURAL NETWORKS;

FEEDBACK CONTROL; ROBOTIC MANIPULATORS; SELF-TUNING REGULATION;

ROBOTICS;

EID: 0027699924     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.286887     Document Type: Article

References (27)

1. A. Isidori, Nonlinear Control Systems. New York: Springer-Verlag, 1989.
2. S. S. Sastry and A. Isidori, “Adaptive control of linearizable systems,” IEEE Trans. Automat. Contr., vol. 34, pp. 1123–1131, 1989.
3. D. Psaltis, A. Sideris, and A. A. Yamamura, “A multilayered neural network controller,” IEEE Contr. Syst. Mag., vol. 8, pp. 17–21, 1988.
4. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. 1, pp. 4–27, 1990.
5. F. C. Chen, “Backpropagation neural networks for nonlinear self-tuning adaptive control,” IEEE Contr. Syst. Mag., vol. 10, pp. 44–48, 1990.
6. N. V. Bhat, P. A. Mindelman, T. McAvoy, and N. S. Wang, “Modeling chemical process systems via neural computation,” IEEE Contr. Syst. Mag., vol. 10, pp. 24–31, 1990.
7. C. P. Neuman and V. D. Tourassis, “Discrete dynamic robot models,” IEEE Trans. Syst., Man, Cybern., vol. SMC-15, pp. 193–204, 1985.
8. K. Homik, M. Strinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359–366, 1989.
9. G. Cybenko, “Continuous value neural networks with two hidden layers are sufficient,” Math. Contr., Signals, Syst., vol. 2, pp. 303–314, 1989.
10. K. Funahashi, “On the approximate realization of continuous mappings by neural networks,” Neural Networks, vol. 2, pp. 183–192, 1989.
11. L. B. Almeida, “A learning rule for asynchronous perceptrons with feedback in a combinatorial environment,” in Proc. IEEE First Int. Conf. Neural Networks, San Diego, 1987, pp. 609–618.
12. F. J. Pineda, “Dynamics and architecture for neural computation,” J. Complexity, vol. 4, pp. 216–245, 1988.
13. S. I. Sudharsanan and M. K. Sundareshan, “Training of a three-layer dynamical recurrent neural network for nonlinear input-output mapping,” in Proc. Int. Joint Conf. Neural Networks (UCNN-91), vol. II, Seattle, WA, 1991, pp. 111-116; also see Tech. Rep. ECE-NN-7-90, ECE Dep. Univ. of Arizona, June 1990.
14. B. Widrow, “Adaptive inverse control,” in Adaptive Systems in Control and Signal Processing 1986. International Federation of Automatic Control, July 1986.
15. M. W. Walker and D. E. Orin, “Efficient dynamic computer simulation of robotic mechanisms,” Trans. ASME, J. Dynamic Syst. Meas. Contr., vol. 104, pp. 205–211, 1982.
16. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning internal representations by error propagation,” in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, Eds. Cambridge, MA: M.I.T. Press, 1986, pp. 45–76.
17. R. P. Paul, Robot Manipulators: Mathematics, Programming, and Control. Cambridge, MA: M.I.T. Press, 1981.
18. M. K. Sundareshan and M. A. Koenig, “Decentralized model reference adaptive control of robotic manipulators,” in Proc. 1985 Amer. Contr. Conf, Boston, MA, June 1985.
19. M. K. Sundareshan and A. Karakasoglu, “A neural network-based approach for the intelligent adaptive control of robotic manipulators,” Syst. Sci., vol. 18, no. 1–2, pp. 153–163, 1992.
20. A. Karakasoglu, “Neural network-based approaches to controller design for robot manipulators,” Ph.D. dissertation, Dep. ECE, Univ. of Arizona, 1991.
21. A. Karakasoglu, S. I. Sudharsanan, and M. K. Sundareshan, “Neural network-based identification and adaptive control of nonlinear systems: A novel dynamical network architecture and training policy,” in Proc. 30th IEEE Conf. Decision Contr., Brighton, England, Dec. 1991.
22. A. Karakasoglu and M. K. Sundareshan, “A recurrent neural network-based adaptive variable structure model following control of multijointed robotic manipulators,” in Proc. 31st IEEE Conf. Decision Contr., Tucson, AZ, Dec. 1992.
23. S. I. Sudharsanan and M. K. Sundareshan, “Equilibrium characterization of dynamical neural networks and a systematic synthesis procedure for associative memories,” IEEE Trans. Neural Networks, vol. 2, pp. 509–522, 1991.
24. M. K. Sundareshan, “Exponential stabilization of large-scale systems: Decentralized and multilevel schemes,” IEEE Trans. Syst., Man, Cybern., vol. SMC-7, pp. 478–484, 1977.
25. M. K. Sundareshan, “Generation of multilevel control and estimation schemes for large-scale systems: A perturbational approach, IEEE Trans. Syst. Man, Cybern., vol. SMC-7, pp. 144–152, 1977.
26. M. K. Sundareshan and R. Elbanna, “Qualitative analysis and decentralized controller synthesis for a class of large-scale systems with symmetrically interconnected subsystems,” Automatica, vol. 27, pp. 383–388, Mar. 1991.
27. M. K. Sundareshan and R. M. Elbanna, “A constructive procedure for stabilization of large-scale systems by informationally decentralized controllers,” IEEE Trans. Automat. Contr., vol. 36, pp. 848–852, July 1991.