IMPROVEMENTS IN THE MEAN ARTERIAL BLOOD PRESSURE REGULATION USING FUZZY-NEURAL MODEL-BASED PREDICTIVE CONTROL | ||||
| Menoufia Journal of Electronic Engineering Research | ||||
| Article 8, Volume 16, Issue 1, January 2006, Page 85-106 | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/mjeer.2006.64792 | ||||
 View on SCiNiTO | ||||
| Author | ||||
| Nabila. M. El-Rabaie | ||||
| Faculty of Electronic Engineering, Menoufia University Menoufia, Egypt | ||||
| Abstract | ||||
| The performance of a model-based control system depends strongly on the accuracy of the process model used. Many real-time processes have uncertain and non-linear dynamics and so it is difficult to model them mathematically. Because of the function approximation ability of neural networks, much research has been conducted on adapting them for modeling and controlling dynamic systems. The main objective of this paper is to develop fuzzy neural schemes in the field of process identification and control. The paper develops a systematic and transparent non-linear Fuzzy-Neural Model Predictive Control (FNMPC) scheme to inherit the advantages of both fuzzy logic systems and neural networks and to avoid their individual shortcomings. A reason for this scheme is that most of the optimization methods previously used in Model Predictive Control (MPC) were very computationally demanding. Consequently, the costing horizon of such MPC schemes is limited to a few time-steps and hence the actual control actions are erratic. For comparison reasons, both Fuzzy MPC (FMPC) and FNMPC have been simulated on the mean arterial blood pressure control problem. | ||||
| Keywords | ||||
| Fuzzy Set Theory; Neural Networks; Predictive Control | ||||
| References | ||||
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[1] L. A. Zadeh, “Fuzzy sets, ”Information and Control, vol. 8, 1965, pp. 338-353.
[2] E. H. Mamadani, “Application of fuzzy algorithms for control of simple dynamic plant, ”Proc. IEE, vol. 12, 1974, PP. 1485-1588.
[3] T. Takagi and M. Sugeno, “Fuzzy identification of systems and its applications to modeling and control, ”IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-15, 1985, pp. 116-132.
[4] H. A. Awad, Fuzzy neural networks for modeling and controlling dynamic systems. Ph.D. Thesis, Intelligent systems Int. School of Engineering, Cardiff university, Cardiff, U.K, 2001.
[5] S. I. Horikawa, T. Furnhashi and Y. Uchikawa, “On fuzzy modelling using fuzzy-neural networks with the back-propagation algorithm, ”IEEE Transactions on Neural Networks, vol. 3, 1992, pp. 801-806.
[6] C. Greco, G. Meng, E. Mosca and G. Zappa, “Performance Improvements of self-tuning controllers by multiple-step horizons: the MUSMAR approach” Automatica , vol. 20 pp 281-699, 1984.
[7] E. Mosca, G. Zappa, and J.M Lethos, “Robustness of multiprediction adaptive regulator: MUSMAR approach, ” Automatica , vol. 25, 1985, pp 521-529.
[8] D. W. Clarke, C. Mohtadi, and P. S. Tuffs, “Generalized predictive control- Part I: The basic algorithm, ”Automatica, vol. 23, 1987, pp. 137-148.
[9] D.W Nguyen and B. Widrow, “Neural networks for self learning control systems, ”IEEE Control Systems Magazine, 1990, pp 18-23.
[10] P.J. Werbos, “Generalization of back-propagation with application to a recurrent gas market model, ”Neural Networks, 1989.
[11] C. Georggesco, A.Alfshari, and G. Bornard, “Fuzzy predictive PID controllers: as heating control application” Procc. IEEE Internat. Conf. On Fuzzy Systems, S. Diago 1992.
[12] J. Valent de Olivveira, and J.M. Lemos, “Long-rang predictie adaptive fuzzy relational control, ” Fuzzy sets and Systems, vol.70, 1995, pp 337-357.
[13] N. M. El-Rabaia, M. A. El. Ghotmy, and T.A. Mahmoud, “Neuro-fuzzy control of mean arterial blood pressure, ”Electronic Engineering Bulletin, Menouf, Egypt, no. 20, July 2000.
[14] D. A. Linkens, and S. Kandiah, “Long-range predictive control using fuzzy process models, ”Trans. Icheme, vol 74, Part a, January 1996.
[15] Yaochu. Jin, Jingping Jiang, and Jing. Zhu, “Neural networks based fuzzy identification and its applications to modelling and control of complex systems” IEEE Trans. Syst., Man.,Cybern., vol. 25, no. 6, JUNE 1995.
[16] C.T Cho, Y.J Chen, and C.C.Teng, “Simplification of fuzzy- neural system using similarity analysis, ” IEEE Trans. Syst., Man.,Cybern., vol. 26, no., 2, APRIL 1996.
[17] E. F. Camacho and C. Bordons. Model predictive control in the process industry. Springer-Verlag London Ltd. UK, 1995.
[18] J.B Slate and L.C Sheppard, “Automatic control of blood pressure By using drug infusion,” Proc. IEE, vol. 129, Pt. A, No. 9, December 1982.
[19] L. C. Shappard, J. F. Shot, N. F. Roberson, F. D. Wallace, and N. T. Kouchoukos, “Computer controlled infusion of vasoactive drugs in post-cardiac surgical patients,” Proc. IEEE-EMBS Conf., 1979, PP. 280-284.
[20] G. A. Pajunen, M. Steinmetz, and R. Shankar, “Model reference adaptive control with constraints for postoperative blood pressure management, ” IEEE Trans. Biomed. Eng. vol. 37, 1990, PP. 679-686. | ||||
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