Y. Shan, J. Hu, Z. Li, and J. M. Guerrero, A Model Predictive Control for Renewable Energy Based AC Microgrids Without Any PID Regulators, IEEE Transactions on Power Electronics, vol.33, issue.11, pp.9122-9126, 2018.

N. Rahimi and R. K. Moghaddam, Maximizing the Absorbed Power of a Point Absorber using an FA-based Optimized Model Predictive Control, China Ocean Engineering, vol.32, issue.6, pp.696-705, 2018.

G. Williams, P. Drews, B. Goldfain, J. M. Rehg, and E. A. Theodorou, Information-Theoretic Model Predictive Control: Theory and Applications to Autonomous Driving, IEEE Transactions on Robotics, vol.34, issue.6, pp.1603-1622, 2018.

K. Worthmann, M. W. Mehrez, M. Zanon, G. K. Mann, R. G. Gosine et al., Model Predictive Control of Nonholonomic Mobile Robots Without Stabilizing Constraints and Costs, IEEE Transactions on Control Systems Technology, vol.24, issue.4, pp.1394-1406, 2016.

D. Bao-cang, Modern Predictive Control, 1 st, 2010.

D. Q. Mayne, Model predictive control: Recent developments and future promise, Automatica, vol.50, issue.12, pp.2967-2986, 2014.

J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Design, 2013.

S. Bouallègue and R. Fessi, Rapid Control Prototyping and PIL Co-Simulation of a Quadrotor UAV Based on NI myRIO-1900 Board, International Journal of Advanced Computer Science and Applications, vol.7, issue.6, pp.26-35, 2016.

T. Baumeister, S. L. Brunton, and J. N. Kutz, Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers, Journal of the Optical Society of America B, vol.35, issue.3, pp.617-626, 2018.

A. S. Yamashita, A. C. Zanin, and D. Odloak, Tuning of Model Predictive Control with Multi-objective Optimization, Brazilian Journal of Chemical Engineering, vol.33, issue.02, pp.333-346, 2016.

Q. N. Tran, R. Octaviano, L. Ozkan, and A. C. Backx, Generalized Predictive Control tuning by controller matching, Proceedings of the 2014 American Control Conference, pp.4889-4894, 2014.

P. Bagheri and A. K. Sedigh, Analytical approach to tuning of model predictive control for first-order plus dead time models, IET Control Theory & Applications, vol.7, issue.14, pp.1806-1817, 2013.

R. Toro, C. Ocampo-martinez, F. Logist, J. V. Impe, and V. Puig, Tuning of Predictive Controllers for Drinking Water Networked Systems, Proceedings of the 18th World Congress of the International Federation of Automatic Control, pp.14507-14512, 2011.

G. Shah and S. Engell, Tuning MPC for Desired Closed-Loop Performance for MIMO Systems, Proceedings of the 2011 American Control Conference, pp.4404-4409, 2011.

X. Yang, Engineering optimization :an introduction with metaheuristic applications, 2010.

J. Dréo, A. Pétrowski, P. Siarry, and E. Taillard, Metaheuristics for Hard Optimization, 2006.

M. Gendreau and J. Potvin, Handbook of Metaheuristics, vol.146, 2010.

G. Sandou and S. Olaru, Particle Swarm Optimization Based NMPC: An Application to District Heating Networks, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00417255

G. Sandou and S. Olaru, Ant Colony and Genetic Algorithm for Constrained Predictive Control of Power Systems, Hybrid Systems: Computation and Control, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00257831

G. Sandou, Metaheuristic strategy for the hierarchical predictive control of large scale energy networks, Control Eng. Appl. Informatics, vol.11, issue.3, pp.32-40, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00424610

H. M. Albeahdili, T. Han, and N. E. Islam, Hybrid Algorithm for the Optimization of Training Convolutional Neural Network, Int. Jour.of Advanced Comp. Science and Applications, vol.6, issue.10, pp.79-85, 2015.

R. Suzuki, F. Kawai, H. Ito, C. Nakazawa, Y. Fukuyama et al., Automatic Tuning of Model Predictive Control Using Particle Swarm Optimization, Proceedings of the 2007 IEEE Swarm Intelligence Symp. Honolulu, pp.221-226, 2007.

M. L. Derouiche, S. Bouallègue, J. Haggège, and G. Sandou, LabVIEW Perturbed Particle Swarm Optimization Based Approach for Model Predictive Control Tuning, Proceedings of the 4th IFAC International Conference on Intelligent Control and Automation Sciences, vol.49, pp.353-358, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01347041

X. Zhao, A perturbed particle swarm algorithm for numerical optimization, Appl. Soft Comput, vol.10, issue.1, pp.119-124, 2010.

E. Rashedi, H. Nezamabadi-pour, and S. Saryazdi, GSA: A Gravitational Search Algorithm, Inf. Sci. (Ny), vol.179, issue.13, pp.2232-2248, 2009.

R. V. Rao, V. J. Savsani, and D. P. Vakharia, Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems, Comput. Des, vol.43, issue.3, pp.303-315, 2011.

S. Mirjalili, S. M. Mirjalili, and A. Lewis, Grey Wolf Optimizer, Adv. Eng. Softw, vol.69, pp.46-61, 2014.

H. Yaghoubi, The Most Important Maglev Applications, J. Eng, vol.2013, pp.1-19, 2013.

M. Santos, R. K. Galvao, and T. Yoneyama, Robust Model Predictive Control for a Magnetic Levitation System Employing Linear Matrix Inequalities, ABCM Symp. Ser. Mechatronics, vol.4, pp.147-155, 2010.

W. Golebiowski, Waptia-genetic optimization algorithm-General-LAVA, p.8, 2017.

J. Ivanka and P. Navratil, Multiestimation Scheme for Adaptive Control of Three Tank System DTS200, Proceedings of the 48th International Scientific Conference on Experimentalni Analyza Napeti, 2010.

, Experimental Stress Analysis. Velke Losiny, pp.123-130, 2010.