Reinforcement learning for process identification, control and optimisation

2004 2nd International IEEE Conference 'Intelligent Systems' - Proceedings

Volumn 1, Issue , 2004, Pages 316-321

  Govindhasamy, James J ^a   McLoone, Scán F ^b   Irwin, George W ^a  

^a QUEEN'S UNIVERSITY BELFAST   (United Kingdom)

^b NATIONAL UNIVERSITY OF IRELAND   (Ireland)

Author keywords

Action dependent adaptive critics; Neural network; Nonlinear PI control; Reinforcement learning

Indexed keywords

APPROXIMATION THEORY; BACKPROPAGATION; COMPUTER SIMULATION; INTEGRATED CONTROL; LEARNING SYSTEMS; MATHEMATICAL MODELS; NEURAL NETWORKS; OPTIMIZATION; PROPORTIONAL CONTROL SYSTEMS;

ACTION DEPENDENT ADAPTIVE CRITICS; NONLINEAR PI CONTROL; REINFORCEMENT LEARNING;

PROCESS CONTROL;

EID: 8844245617     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Werbos, P. J., "A Menu of Designs for Reinforcement Learning Over Time", in Miller, W. T., Sutton, R. S. and Werbos P. J. eds, Neural Networks for Control, MIT Press, Cambridge, MA, pp. 67 - 95, 1990.
2. P. J. Werbos, "Approximate dynamic programming control for real-time control and neural modelling", Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches (Chapter 13), Edited by D. A. White and D. A. Sofge, New York, NY: Van Nostrand Reinhold, 1992.
3. R. E. Bellman, "Dynamic Programming", Princeton, NJ, Princeton University Press, 1917.
4. J. Si and Y. T. Wang, "Online Learning Control by Association and Reinforcement", IEEE Transactions on Neural Networks, vol. 12, no. 2, pp. 264-276, March 2001.
5. S. McLoone, "Neural Network Identification: A survey of gradient based methods", IEE Colloquium Optimization in Control: Methods and applications, London, Digest 98/521, November 1998.
6. Ngia L.S.H. and Sjöberg J., "Efficient Training of Neural Nets for Nonlinear Adaptive Filtering using a Recursive Levenberg-Marquardt Algorithm", IEEE Transactions on Signal Processing, vol. 48, no. 7, pp. 1915-1926, July 2000.
7. Prokhorov, D., Santiago, R., and D. Wunsch, "Adaptive Critic Designs: A Case Study For Neurocontrol", Neural Networks, vol. 8, no. 9, pp. 1367-1372, 1995.
8. Prokhorov, D., and Wunsch, D., "Adaptive critic designs," IEEE Transactions on Neural networks, Vol. 8, pp. 997-1007, Sept. 1997.
9. Werbos, P. J., "Consistency of HDP Applied to a Simple Reinforcement Learning Problem", Neural Networks, vol. 3, no. 2, pp. 179-189, 1990.
10. Liu, D., Xiong, X., and Zhang, Y., "Action-Dependant Adaptive Critic Designs", Proc. of the INNS-IEEE International Joint Conference on Neural Networks, pp. 990-995, July, 2001.
11. Sutton R. S., "Learning to Predict by the Method of Temporal Differences", Machine Learning, Vol. 3, pp. 9-44, 1988.
12. Sutton R.S., "Implementation Details of the TD(1) Procedure for the Case of Vector Predictions and Backpropagation", GTE Laboratories Technical Note TN87-509.1, Aug., 1989.
13. Morningred, J.D., Paden, B.E., Seborg, D.E., and Mellichamp, D.A., "An Adaptive Nonlinear Predictive Controller", Chemical Engineering Science, vol. 47, No. 4, pp. 755-762, 1992.
14. Morningred J.D., Paden, B.E., Seborg, D.E., and Mellichamp, D.A., "An adaptive nonlinear predictive controller", Proceeding of the American Control Conference (ACC), vol.2, pp. 1614-1619, 1990.
15. Rovlak, J. A. and Corlis, R., "Dynamic matrix based control of fossil power plants", IEEE Transactions on Energy Conversion, vol. 6, no. 2, pp. 320-326, 1991.
16. Irwin, G. W., O'Reilly, P., Ligthbody, G., Brown, M., and Swidenbank, E., "Electrical power and chemical process applications", In Neural network applications in control, Irwin, G. W., Warwick, K. and Hunt, K. J., eds., IEE Control Engineering Series 53, The Institution of Electrical Engineers, London, UK, 1995.