A. Alanqar, M. Ellis, and P. D. Christofides, Economic model predictive control of nonlinear process 461 systems using empirical models, AIChE Journal, issue.3, pp.61-816, 2015.

D. Angeli, R. Amrit, and J. B. Rawlings, On Average Performance and Stability of Economic Model Predictive Control, IEEE Transactions on Automatic Control, vol.57, issue.7, pp.1615-1626, 2012.
DOI : 10.1109/TAC.2011.2179349

T. Barz, D. C. Cárdenas, H. Arellano-garcia, and G. Wozny, Experimental evaluation of an ap- 465 proach to online redesign of experiments for parameter determination, AIChE Journal, issue.6, pp.59-1981, 2012.

T. Ba¸sarba¸sar and P. Bernhard, H ? -optimal control and related minimax design problems: a dynamic 467 game approach, 1995.

G. Besançon, Identification of parametric models from experimental data. Lecture Notes in Control 469 and Information Sciences, 2007.

G. Besançon, Nonlinear observers and application. Lecture Notes in Control and Information Sci- 471 ences, 2007.

G. Besançon and A. Ticlea, An Immersion-Based Observer Design for Rank-Observable Nonlinear Systems, IEEE Transactions on Automatic Control, vol.52, issue.1, pp.83-88, 2007.
DOI : 10.1109/TAC.2006.889867

D. Blanc, P. Dufour, Y. Toure, and P. Laurent, On nonlinear distributed parameter model predictive 475 control strategy: on-line calculation time reduction and application to an experimental drying process, 476 Computers and Chemical Engineering, pp.1533-1542, 2003.

N. Boizot, E. Busvelle, and J. Gauthier, An adaptive high-gain observer for nonlinear systems, Automatica, vol.46, issue.9, 2010.
DOI : 10.1016/j.automatica.2010.06.004

G. Bornard, N. Couenne, and F. Celle, Regularly persistent observers for bilinear systems, Lecture 480 Notes in Control and Information Sciences, pp.130-140, 1988.
DOI : 10.1007/BFb0043023

J. Calvet and Y. Arkun, Stabilization of feedback linearized nonlinear processes under bounded 482 perturbations, pp.747-752, 1989.

F. Castillo, E. Witrant, C. Prieur, and L. Dugard, Dynamic boundary stabilization of linear and 484 quasi-linear hyperbolic systems, IEEE Conference on Decision and Control CDC, pp.2952-485, 2012.

H. Cox, On the estimation of state variables and parameters for noisy dynamic systems, IEEE Transactions on Automatic Control, vol.9, issue.1, pp.5-12, 1964.
DOI : 10.1109/TAC.1964.1105635

P. Dufour, S. Flila, and H. Hammouri, Observer Design for MIMO Non-Uniformly Observable Systems, IEEE Transactions on Automatic Control, vol.57, issue.2, pp.511-516, 2012.
DOI : 10.1109/TAC.2011.2166667
URL : https://hal.archives-ouvertes.fr/hal-00648978

M. Ellis, H. Durand, and P. D. Christofides, A tutorial review of economic model predictive control methods, Journal of Process Control, vol.24, issue.8, pp.1156-1178, 2014.
DOI : 10.1016/j.jprocont.2014.03.010

S. Flila, P. Dufour, and H. Hammouri, Optimal input design for on-line identification: a coupled 493 observer-MPC approach, IFAC World Congress, pp.11457-11462, 2008.

U. Forssell and L. Ljung, Identification of unstable systems using output error and box-jenkins model 495 structures, Proceedings of the 29th IEEE CSS Chinese Control Conference, pp.3932-3937, 1998.

G. Franceschini and S. Macchietto, Model-based design of experiments for parameter precision: State of the art, Chemical Engineering Science, vol.63, issue.19, pp.4846-4872, 2008.
DOI : 10.1016/j.ces.2007.11.034

J. P. Gauthier, H. Hammouri, and S. Othman, A simple observer for nonlinear systems applications to bioreactors, IEEE Transactions on Automatic Control, vol.37, issue.6, pp.875-880, 1992.
DOI : 10.1109/9.256352

G. C. Goodwin and R. L. Payne, Dynamic system identification: experiment design and data analysis, 501 Mathematics in Science and Engineering, vol.136, 1977.

H. Hammouri and J. D. Morales, Observer synthesis for state-affine systems, 29th IEEE Conference on Decision and Control, pp.784-785, 1990.
DOI : 10.1109/CDC.1990.203695

R. Huang, E. Harinath, and L. T. Biegler, Lyapunov stability of economically oriented NMPC for cyclic processes, Journal of Process Control, vol.21, issue.4, pp.501-509, 2011.
DOI : 10.1016/j.jprocont.2011.01.012

H. Jain, V. Kaul, and N. Ananthkrishnan, Parameter estimation of unstable, limit cycling systems us- 507 ing adaptive feedback linearization: example of delta wing roll dynamics, Journal of Sound and Vibration, vol.508, pp.287-939, 2005.

B. Jayasankar, B. Huang, and A. Ben-zv, Receding horizon experiment design with application in 510 SOFC parameter estimation, Dynamics and Control of Process Systems, pp.527-532, 2010.

L. Keviczky, Design of experiments for the identification of linear dynamic systems, pp.303-308, 1975.

L. Ljung, Asymptotic behavior of the extended Kalman filter as a parameter estimator for linear systems, IEEE Transactions on Automatic Control, vol.24, issue.1, pp.36-50, 1979.
DOI : 10.1109/TAC.1979.1101943

L. Ljung, System identification: Theory for the user, 1999.

M. Nadri, H. Hammouri, and R. Grajales, Observer Design for Uniformly Observable Systems With Sampled Measurements, IEEE Transactions on Automatic Control, vol.58, issue.3, pp.757-762, 2013.
DOI : 10.1109/TAC.2012.2212517

A. H. Nayfeh, J. M. Elzeba, and D. T. Mook, Analytical study of the subsonic wing-rock phenomenon for slender delta wings, Journal of Aircraft, vol.26, issue.9, pp.805-809, 1989.
DOI : 10.2514/3.45844

L. Nelson and E. Stear, The simultaneous on-line estimation of parameters and states in linear systems, IEEE Transactions on Automatic Control, vol.21, issue.1, pp.94-98, 1976.
DOI : 10.1109/TAC.1976.1101148

T. S. Ng, G. C. Goodwin, and T. Söderström, Optimal experiment design for linear systems with input-output constraints, Automatica, vol.13, issue.6, pp.571-577, 1997.
DOI : 10.1016/0005-1098(77)90078-4

J. Qian, P. Dufour, and M. Nadri, Observer and model predictive control for on-line parameter 525 identification in nonlinear systems, IFAC Dynamics and Control of Process Systems, vol.526, pp.571-576, 2013.

J. Qian, M. Nadri, P. D. Moro¸sanmoro¸san, and P. Dufour, Closed loop optimal experiment design for on-line 528 parameter estimation, IFAC IEEE European Control Conference, pp.1813-1818, 2014.
DOI : 10.1109/ecc.2014.6862468

J. B. Rawlings, D. Angeli, and C. N. Bates, Fundamentals of economic model predictive control, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC), pp.3851-3861, 2012.
DOI : 10.1109/CDC.2012.6425822

E. Walter and L. Pronzato, Identification of parametric models from experimental data, p.532, 1994.

J. L. Willems, A general stability criterion for non-linear time-varying feedback systems???, International Journal of Control, vol.56, issue.4, pp.625-631, 1970.
DOI : 10.1109/TAC.1966.1098316

M. Zanon, S. Gros, and M. Diehl, Indefinite linear MPC and approximated economic MPC for nonlinear systems, Journal of Process Control, vol.24, issue.8, pp.1273-1281, 2014.
DOI : 10.1016/j.jprocont.2014.04.023
URL : https://hal.archives-ouvertes.fr/hal-01030531

V. M. Zavala and L. T. Biegler, The advanced-step NMPC controller: Optimality, stability and robustness, Automatica, vol.45, issue.1, pp.86-93, 2009.
DOI : 10.1016/j.automatica.2008.06.011

Y. Zhu and B. Huang, Constrained receding-horizon experiment design and parameter estimation in the presence of poor initial conditions, AIChE Journal, vol.53, issue.10, pp.2808-2820, 2011.
DOI : 10.1002/aic.12479