Abstract
Recently, adaptive control systems utilizing artificial intelligent techniques are being actively investigated in many applications. Neural networks with their powerful learning capability are being sought as the basis for many adaptive control systems where on-line adaptation can be implemented. Fuzzy logic, on the other hand, has proved to be rather popular in many control system applications due to providing a rule-base like structure. In this paper, an adaptive neuro-fuzzy control system is proposed in which the Radial Basis Function neural network (RBF) is implemented as a neuro-fuzzy controller (NFC) and the General Regression neural network (GRNN) as a predictor. The adaptation of the system involves the following three procedures: (1) tuning of the control actions or rules, (2) trimming of the control actions, and (3) adjustment of the controller output gain. The tuning method is a non-gradient descent method based on the predicted system response which is able to self-organize the control actions from the initial stage. The trimming scheme can help to reduce the aggressiveness of the particular control rules such that the response is stabilized to the set-points more effectively, while the controller gain adjustment scheme can be applied in the cases where the appropriate controller output gain is difficult to determine heuristically. To show the effectiveness of this methodology, its performance is compared with the well known Generalized Predictive Control (GPC) technique which is a combination of both adaptive and predictive control schemes. Comparisons are made with respect to the transient response, disturbance rejection and changes in plant dynamics. The proposed control system is also applied in controlling a single link manipulator. The results show that it exhibits robustness and good adaptation capability which can be practically implemented.
Similar content being viewed by others
References
Yusof, R.: Theory and applications of self-tuning PID and generalized predictive controllers, PhD Dissertation, University of Tokusima, Japan, 1994.
Clarke, D. W. and Mohtadi, C.: Properties of generalized predictive control, Automatica 25(6) (1989), 859–875.
Cox, E.: Adaptive fuzzy system, IEEE Spectrum (February 1993), 27–31.
Nie, J. and Linkens, D. A.: Fuzzy-Neural Control: Principles, Algorithms and Applications, Prentice-Hall, UK, 1995.
Wang, L. X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis, Prentice-Hall, New Jersey, 1994.
Zhang, Y., Sen, P., and Hearn, G. E.: An on-line trained adaptive neural controller, IEEE Control Systems (October 1995), 67–75.
Lightbody, G. and Irwin, G. W.: Direct neural model reference adaptive control, IEE Proc. Control Theory Appl. 142(1) (1995), 31–43.
Jang, J. R. and Sun, C. T.: Neuro-fuzzy modeling and control, Proc. IEEE 83(3) (1995), 378–406.
Yin, T. K. and Lee, C. S. G.: Fuzzy model-reference adaptive control, in: Proc. of the 3rd Conf. on Decision and Control, USA, December 1994, pp. 4130–4135.
Jin, L., Nikiforuk, P. N., and Gupta, M. M.: Adaptive control of discrete-time nonlinear systems using recurrent neural networks, IEE Proc. Control Theory Appl. 141(3) (1994), 169–176.
Draeger, A., Engell, S., and Ranke, H.: Model predictive control using neural networks, IEEE Control Systems Magazine 15(5) (1995), 61–66.
Valente, J. and Lemos, J. M.: Long-range predictive adaptive fuzzy relational control, Fuzzy Sets Systems 70 (1995), 337–357.
Berenji, H. R. and Khedkar, P.: Learning and tuning fuzzy logic controllers through reinforcements, IEEE Trans. Neural Networks 3(5) (1992), 724–740.
Khalid, M., Omatu, S., and Yusof, R.: Adaptive fuzzy control of a water bath process with neural networks, Engrg. Appl. Artif. Intell. 7(1) (1994), 39–52.
Bogdan, S. and Kovacic, Z.: Fuzzy rule-based adaptive force control of a single Dof mechanisms, in: Proc. of the 1993 Int. Symp. on Intelligent Control, Chicago, USA, August 1993, pp. 469–474.
Skrjanc, I., Kavsek-Biasizzo, K., and Matko, D.: Real-time fuzzy adaptive control, Engrg. Appl. Artif. Intell. 10(1) (1997), 53–61.
Lin, C. T., Lin, C. J., and Lee, C. S.: Fuzzy adaptive learning control network with on-line neural learning, Fuzzy Sets Systems 71 (1995), 25–45.
Brown, M. and Harris, C. J.: A perspective and critique of adaptive neurofuzzy systems used for modeling and control applications, Internat. J. of Neural Systems 6(2) (1995), 197–220.
Harris, C. J., Brown, M., Bossley, K. M., Mills, D. J., and Ming, F.: Advances in neurofuzzy algorithms for real-time modeling and control, Engrg. Appl. Artif. Intell. 9(1) (1996), 1–16.
Watanabe, K., Tang, J., Nakamura, M., Koga, S., and Fukuda, T.: A fuzzy-Gaussian neural network and its applications to mobile robot control, IEEE Trans. Control Systems Techn. 4(2) (1996), 193–199.
Specht, D. F.: Probabilistic neural networks and general regression neural networks, in: Fuzzy Logic and Neural Network Handbook, Chapter 3, McGraw-Hill, New York, 1996.
Timothy, M.: Advanced Algorithms for Neural Networks: ACCC Sourcebook, Wiley, Canada, 1995.
Rubio, F. R., Berenguel, M., and Camacho, E. F.: Fuzzy logic control of a Solar power plant, IEEE Trans. on Fuzzy Systems 3(4) (1995), 459–468.
Wu, J. C. and Liu, T. S.: A sliding-mode approach to fuzzy control design, IEEE Trans. Control Systems Techn. 4(2) (1996), 141–151.
Lee, T. H., Nie, J. H., and Tan, W. K.: A self-organizing fuzzified basis function network control system applicable to nonlinear servo mechanisms, Mechatronics 5(6) (1995), 695–713.
Linkens, D. A. and Nie, J.: A unified real-time approximate reasoning approach for use in intelligent control, Part I: Theoretical development, Internat. J. Control 56 (1992), 347–364.
Linkens, D. A. and Nie, J.: Fuzzified RBF network-based learning control: Structure and self-construction, in: Proc. of 1993 IEEE Int. Conf. on Neural Networks, USA, 28 March—1 April, 1993, pp. 1016–1021.
Moody, J. and Darken, C.: Fast-learning in networks of locally-tuned processing units, Neural Comput. 1 (1994), 281–294.
Leonard, J. A. and Kramer, M. A.: Radial basis function networks for classifying process faults, IEEE Control System (April 1991), 31–38.
Specht, D. F.: A general regression neural network, IEEE Trans. Neural Network 2(6) (1991), 568–576.
Parzen, E.: On estimation of a probability density function and mode, Ann. Math. Statist. 33 (1962), 1065–1076.
Behmenburg, C.: Model reference adaptive systems with fuzzy logic controllers, in: Proc. 2 nd Conf. on Control Appl., Vancouver, September 1993, pp. 171–176.
Yan, J., Ryan, M., and Power, J.: Using Fuzzy Logic: Towards Intelligent Systems, Prentice-Hall, Englewood Cliffs, NJ, 1994.
Kristinsson, K. and Dumont, G. A.: System identification and control using genetic algorithms, IEEE Trans. Systems Man Cybernet. 22(5) (1992), 1033–1046.
Ljung, L. and Soderstrom: Theory and Practice of Recursive Identification, MIT Press, Cambridge, MA, 1983.
Astrom, K. J. and Wittenmark, B.: Computer Controlled Systems, Prentice-Hall, Englewood Cliffs, NJ, 1984.
Jin, L., Nikiforuk, P. N., and Gupta, M. M.: Direct adaptive output tracking control using multilayered neural networks, IEE Proc.-D 140(6), 392–398.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Seng, T.L., Khalid, M., Yusof, R. et al. Adaptive Neuro-fuzzy Control System by RBF and GRNN Neural Networks. Journal of Intelligent and Robotic Systems 23, 267–289 (1998). https://doi.org/10.1023/A:1008035716169
Issue Date:
DOI: https://doi.org/10.1023/A:1008035716169