×

A two-stage approach to self-learning direct fuzzy controllers. (English) Zbl 1005.93029

The paper proposes, using a two-stage algorithm, an approach to self-tuning a direct fuzzy controller with very limited information on the system to be controlled. No off-line pre-training of the system and its parameters is assumed. During the first stage, a coarse tuning is achieved based on the system output error. In the second stage, the fine tuning of the fuzzy rules is accomplished by using the controller output error as the information source and a gradient-based method as the optimization tool. Several examples are presented to validate the proposed approach.

MSC:

93C42 Fuzzy control/observation systems
68T05 Learning and adaptive systems in artificial intelligence
PDFBibTeX XMLCite
Full Text: DOI

References:

[1] Procyk, T.; Mamdani, E., A linguistic self-organizing process controller, Automatica, 15, 1, 15-30 (1979) · Zbl 0393.68082
[2] Maeda, M.; Murakami, S., A self-tuning fuzzy controller, Fuzzy Sets and Systems, 51, 29-40 (1992)
[3] Singh, Y. P., A modified self-organizing controller for real-time process control applications, Fuzzy Sets and Systems, 96, 147-159 (1998)
[4] Rojas, I.; Pomares, H.; Pelayo, F. J.; Anguita, M.; Ros, E.; Prieto, A., New methodology for the development of adaptive and self-learning fuzzy controllers in real time, Int. J. Approx. Reason., 21, 109-136 (1999) · Zbl 0995.68079
[5] Wang, L. X., Adaptive Fuzzy Systems and Control. Design and Stability Analysis (1994), Prentice-Hall: Prentice-Hall Englewoodcliffs, NJ
[6] Chen, F.-C.; Khalil, H. K., Adaptive control of a class of nonlinear discrete-time systems using neural networks, IEEE Trans. Automat. Control, 40, 5, 791-801 (1995) · Zbl 0925.93461
[7] Fong, K. F.; Loh, A. P., MRAC control of non-linear systems using neural networks with recursive least squares adaptation, (Proceedings of the IEEE International Conference on Neural Networks (1993)), 529-533
[8] Layne, J. R.; Passino, K. M., Fuzzy model reference learning control, J. Intell. Fuzzy Syst., 4, 33-47 (1996) · Zbl 0900.93165
[9] Narendra, K.; Annaswamy, A., Stable Adaptive Systems (1989), Prentice-Hall: Prentice-Hall EnglewoodCliffs, NJ · Zbl 0758.93039
[10] Andersen, H. C.; Lotfi, A.; Tsoi, A. C., A new approach to adaptive fuzzy control: The controller output error method, IEEE Trans. Systems Man Cybernet. - Part B, 27, 4, 686-691 (1997)
[11] Pomares, H.; Rojas, I.; Fernández, F. J.; Anguita, M.; Ros, E.; Prieto, A., New approach for the design of fuzzy controllers in real time, (Proceedings of the 8th International Conference on Fuzzy Systems, Seoul, Korea (1999)), 522-526
[12] Ordoñez, R.; Zumberge, J.; Spooner, J. T.; Passino, K. M., Adaptive fuzzy control: experiments and comparative analyses, IEEE Trans. Fuzzy Systems, 5, 2, 167-188 (1997)
[13] Lee, C. C., Fuzzy logic in control systems fuzzy logic controller - Part III, IEEE Trans. Systems Man Cybernet., 20, 2, 404-435 (1990)
[14] Ruspini, E. H., A new approach to clustering, Inform. Control, 15, 22-32 (1969) · Zbl 0192.57101
[15] Rojas, I.; Pomares, H.; Ortega, J.; Prieto, A., Self-organized fuzzy system generation from training examples, IEEE Trans. Fuzzy Systems, 8, 1, 23-36 (2000)
[16] Pomares, H.; Rojas, I.; Ortega, J.; Gonzalez, J.; Prieto, A., A systematic approach to a self-generating fuzzy rule-table for function approximation, IEEE Trans. Systems Man Cybernet., 30, 3, 431-447 (2000)
[17] Reay, D. S., Comments on “a new approach to adaptive fuzzy control the controller output error method”, IEEE Trans. Systems Man Cybernt. - Part B, 29, 4, 545-546 (1999)
[18] Slotine, J. E.; Li, W., Applied Nonlinear Control (1991), Prentice-Hall: Prentice-Hall Englewood Cliffs, NJ · Zbl 0753.93036
[19] Berenji, H. R.; Khedkar, P., Learning and tuning fuzzy logic controllers through reinforcements, IEEE Trans. Neural Networks, 3, 5, 724-739 (1992)
[20] Jang, J. S.R., Self-learning fuzzy controllers based on temporal back propagation, IEEE Trans. Neural Networks, 3, 5, 714-723 (1992)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.