Moving Horizon State Estimation for Nonlinear Systems: Application to a Chemical Reactor CSTR


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Abstract


This paper considers the state estimation problem for the nonlinear systems. The approach, as it is based on the moving horizon technique, aims at studying the spirit of the estimator and its implementation simultaneously. This technique, in fact, allows for transposing the observation problem to an optimization one, it also consists in minimizing the gap between the measurement of the system and its prediction on a horizon of preset time. The optimization algorithm that is used is of Levenberg-Marquardt. The developed method, as a result, is validated on the nonlinear model of a continuous chemical reactor.
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Keywords


State Estimation; Moving Horizon; Nonlinear Systems; Optimization Algorithm

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References


J.P.Gauthier, and I.A.K.Kupka, Observability and observers for nonlinear systems, application to bioreactors, SIAM Journal on Control and Optimization, vol. 32(Issue 4):975-994, July 1994.

H. Michalska, and D. Q. Mayne, Moving Horizon Observers and Observers-Based Control. IEEE Transactions on Automatic Control, vol. 40(Issue 6):995-1006, June 1995.

H.Nijmeijer, A.J. Van Der Scjaft, Nonlinear dynamic control systems (Spring-verlag, 1990).

A. Alessandri, M. Baglietto, and G. Battistelli, Receding horizon estimation for switching discrete-time linear systems, IEEE Transactions on Automatic Control, vol. 50(Issue 11):1736-1748, November 2005.

B. Boulkroune, M. Darouach, and M. Zasadzinski, Optimal estimation for linear singular systems using moving horizon estimation, IFAK International Federation of Automatic Control, vol. 5(Issue 3):14528-14533, July 2008.

V. Rao, J. B. Rawlings, and D. Q. Mayne, Constrained state estimation for nonlinear discrete-time systems: stability and moving horizon approximations, IEEE Transactions on Automatic Control, vol. 48 (Issue 2):246-258, June 2003.

A. Bemporad, D. Mignone, M. Morari, Moving Horizon Estimation for Hybrid Systems and Fault Detection, American Control Conference, Vol. 3, pp. 2471-2475, SanDiego, CA, USA, June 1999.

H. Valdés-González, J. M. Flaus, and G Acuña, Moving Horizon State Estimation with Global Convergence Using Interval Techniques: Application to Biotechnological Processes. Journal of Process Control, Vol. 13(Issue 4):325-336, June 2003.

H. Berghuis, and H. Nijmeijer, Robust control of robots via linear estimated state feedback. IEEE Transactions on Automatic Control, Vol. 39(Issue 10):2159-2162, October 1994.

L.P.Russo, R.E.Young, Moving-Horizon State Estimation Applied to an Industrial Polymerization Process, American Control Conference, vol. 2, pp. 1129-1133, California, USA, 1999.

B. M. Wilamowski, and H. Yu, Improved Computation for Levenberg Marquardt Training, IEEE Transactions on Neural Networks, vol. 21(Issue 6):930-937, June 2010.

A. Djebabla, S. Bououden, S. Filali, Using LMI-Based Design to Resolve the Constrained Fuzzy Model Predictive Control Problem, (2008) International Review of Automatic Control (IREACO), 1 (4), pp. 435-440.

A. Alessandri, M. Baglietto, and G. Battistelli, Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes, IFAC International Federation of Automatic Control, Vol. 44(Issue 7):1753-1765, July 2008.

B. Boulkroune, M. Darouach, and M. Zasadzinski, Moving horizon estimation for linear discrete systems, IET Control Theory & Applications, Vol. 4(Issue 3):339-350, March 2010.

G. Ciccarella, M. Dalamora and A. Germani, A Luenberger-like observer for nonlinear systems, International Journal of Control, Vol. 57(Issue 3):537-556, March 1993.

M. T. Hagan, and M.B. Menhaj, Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, vol. 5(Issue 6):989-993, November 1994.

J. Madar, J. Abonyi, and F. Szeifert, Feedback Linearizing Control Using Hybrid Neural Networks Identified by Sensitivity Approach, Engineering Applications of Artificial Intelligence, vol. 10(Issue 3):355-363, September 2004.

R. Upadhyay, and R. Singla, Analysis of CSTR Temperature Control with Adaptive and PID Controller (A Comparative Study), IACSIT International Journal of Engineering and Technology, Vol.2(Issue 5):1793-8236, October 2010.


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