A new approach for fuzzy predictive adaptive controller design using particle swarm optimization algorithm

International Journal of Innovative Computing, Information and Control

Volumn 9, Issue 9, 2013, Pages 3741-3758

  Bououden, Sofiane ^a   Chadli, Mohammed ^a   Allouani, Fouad ^b   Filali, Salim ^c  

^a ABBES LAGHROUR UNIVERSITY   (Algeria)

^b UNIVERSITÉ DE PICARDIE JULES VERNE   (France)

^c Universite Mentouri Constantine   (Algeria)

Author keywords

Adaptive fuzzy logic systems; Particle swarm optimization (PSO); Predictive control

Indexed keywords

ADAPTIVE CONTROLLER DESIGN; ADAPTIVE FUZZY LOGIC SYSTEM; ADAPTIVE FUZZY MODELS; OPTIMIZATION PROBLEMS; PARTICLE SWARM OPTIMIZATION ALGORITHM; PREDICTIVE CONTROL; RECURSIVE LEAST SQUARE ALGORITHM; TAKAGI-SUGENO FUZZY INFERENCE SYSTEMS;

ALGORITHMS; FUZZY LOGIC; LINEAR SYSTEMS; MODEL PREDICTIVE CONTROL; PREDICTIVE CONTROL SYSTEMS;

PARTICLE SWARM OPTIMIZATION (PSO);

EID: 84880059317     PISSN: 13494198     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (35)

1. D. W. Clarke, C. Mohtadi and P. S. Tuffs, Generalized predictive control, Part I: The basic algorithm, Part II: Extensions and interpretation, Automatica, vol.23, no.2, pp.137-160, 1987.
2. Y. Zhang, T. Chai, H. Wang, J. Fu, L. Zhang and Y. Wang, An adaptive generalized predictive control method for nonlinear systems based on ANFIS and multiple models, IEEE Transactions on Fuzzy Systems, vol.18, no.6, pp.1070-1082, 2010.
3. S. A. Larrinaga, M. A. R. Vidal, E. Oyarbide and J. R. T. Apraiz, Predictive control strategy for DC/AC converters based on direct power control, IEEE Transactions on Industrial Electronic, vol.54, no.3, pp.1261-1271, 2007.
4. J. Licheng, R. Kumar and N. Elia, Model predictive control-based real-time power system protection schemes, IEEE Transactions on Power Systems, vol.25, no.2, pp.988-998, 2010.
5. T. Takagi and M. Sugeno, Fuzzy identification of systems and its application to modeling and control, IEEE Transactions on Systems, Man and Cybernetics, vol.15, no.1, pp.116-132, 1985.
6. J. Espinosa, J. Vandewalle and V. Wertz, Fuzzy Logic, Identification and Predictive Control, Springer, London, UK, 2005.
7. Y. J. Liu, W. Wang, S. C. Tong and Y. S. Liu, Robust adaptive tracking control for nonlinear systems based on bounds of fuzzy approximation parameters, IEEE Transactions on Systems, Man, Cybernetics, Part A: Systems and Humans, vol.40, no.1, pp.170-184, 2010.
8. M. Chadli, On the stability analysis of uncertain fuzzy models, International Journal of Fuzzy Systems, vol.8, no.4, pp.224-231, 2006.
9. M. Chadli and T.-M. Guerra, LMI solution for robust static output feedback control of Takagi-Sugeno fuzzy models, IEEE Transactions on Fuzzy Systems, vol.20, no.6, 2012.
10. P. P. Angelov and D. P. Filev, An approach to online identification of Takagi-Sugeno fuzzy models, IEEE Transactions on Systems, Man and Cybernetics, Part B: Cybernetics, vol.34, no.1, pp.484-498, 2004.
11. H. N. Nounou and K. M. Passino, Fuzzy model predictive control: Techniques, stability issues, and examples, Proc. of IEEE International Symposium Intelligent Control Intelligent Systems and Semiotics, pp.423-428, 1999.
12. A. Flores, D. Saez, J. Araya, M. Berenguel and A. Cipriano, Fuzzy predictive control of a solar power plant, IEEE Transactions on Fuzzy Systems, vol.13, no.1, pp.58-68, 2005.
13. S. Bououden, O. Benelmir, S. Ziani and S. Filali, A new adaptive fuzzy model and output terminal constraints in predictive control, International Journal of Information and Systems Sciences, vol.3, no.1, pp.25-35, 2007.
14. Y. L. Huang, H. H. Lou, J. P. Gong and T. F. Edgar, Fuzzy model predictive control, IEEE Trans- actions on Fuzzy Systems, vol.8, no.6, pp.665-678, 2000.
15. K. Belarbi and F. Megri, A stable model-based fuzzy predictive control based on fuzzy dynamic programming, IEEE Transactions on Fuzzy Systems, vol.15, no.4, pp.746-754, 2007.
16. H.-M. Lee, R. Jea and J.-S. Su, Fuzzy nonlinear programming for inventory based on confidence interval ICIC Express Letters, vol.6, no.5, pp.1387-1392, 2012.
17. W. Ma, J. Song and W. Wang, Dynamic filled algorithm for global optimization of nonlinear programming, International Conference on Information Networking and Automation, pp.V1-23-V1-27, 2010.
18. T. Niknam, H. D. Mojarrad and M. Nayeripour, A new hybrid fuzzy adaptive particle swarm opti- mization for non-convex economic dispatch, International Journal of Innovative Computing, Infor- mation and Control, vol.7, no.1, pp.189-202, 2011.
19. Y. Song, Z. Chen and Z. Yuan, New chaotic PSO-based neural network predictive control for non- linear process, IEEE Transactions on Neural Networks, vol.18, no.2, pp.595-600, 2007.
20. X. S. Yang, An Introduction with Metaheuristic Application, John Wiley & Sons, 2010.
21. A. Nunez, D. Saez, S. Oblak and I. Skrjanc, Fuzzy-model-based hybrid predictive control, Isa Trans- actions, vol.48, no.1, pp.24-31, 2009.
22. M. Rashid and A. R. Baig, PSOGP: A genetic programming based adaptable evolutionary hybrid particle swarm optimization, International Journal of Innovative Computing, Information and Control, vol.6, no.1, pp.287-296, 2010.
23. Y. Shi and R. C. Eberhart, Parameter selection in particle swarm optimization, Evolutionary Pro- gramming VII, LNCS, vol.1447, pp.591-600, 1998.
24. C. M. Lee and C. N. Ko, Time series prediction using RBF neural networks with a nonlinear time- varying evolution PSO algorithm, Neurocomputing, vol.73, pp.449-460, 2009.
25. K. D. Sharma, A. Chatterjee and A. Rakshit, A Hybrid approach for design of stable adaptive fuzzy controllers employing Lyapunov theory and particle swarm optimization, IEEE Transactions on Fuzzy Systems, vol.17, no.2, pp.329-342, 2009.
26. Z. Bingul and O. Karahan, A fuzzy logic controller tuned with PSO for 2 DOF robot trajectory control, Expert Systems with Applications, vol.38, pp.1017-1031, 2011.
27. J. Yu, S. Wang and L. Xi, Evolving artificial neural networks using an improved PSO and DPSO, Neurocomputing, vol.71, pp.1054-1060, 2008.
28. P. Boucher and D. Dumur, La Commande Predictive, Editions Technip, Collection Methodes et Pratiques de l'Ingenieur, Paris, 1996.
29. S. Bououden, S. Filali and K. Kemih, Adaptive fuzzy tracking control for unknown nonlinear systems, International Journal of Innovative Computing, Information and Control, vol.6, no.2, pp.541-549, 2010.
30. C.-P. Huang, Model based fuzzy control with affine T-S delayed models applied to nonlinear systems, International Journal of Innovative Computing, Information and Control, vol.8, no.5(A), pp.2979-2993, 2012.
31. L. J. Chenand and K. S. Narendra, Identification and control of a nonlinear discrete-time system based on its linearization: A unified framework, IEEE Transactions on Neural Networks, vol.15, no.3, pp.663-673, 2004.
32. D. W. Clarke and C. Mohtadi, Properties of generalized predictive control, The 10th World Congress IFAC, pp.63-74, 1987.
33. M. Henson and D. Seborg, Input-output linearization of general nonlinear processes, AIChE Journal, vol.36, no.11, pp.1753-1757, 1990.
34. G. Lightbody and G. Irwin, Nonlinear control structures based on embedded neural system models, IEEE Transactions on Neural Networks, vol.8, pp.553-567, 1997.
35. K. Astrom and B. Wittenmark, Adaptive Control, Addison-Wesley Editions, 1989.