Nonlinear Observer Design for Mechatronic Systems via LMIs

Keywords: Nonlinear observer design, descriptor system, error factorization, linear matrix inequality, convex modeling

Abstract

This paper presents an observer design for discrete-time nonlinear descriptor systems via convex techniques. The approach is based on a recently appeared technique for exact factorization of the observation error, in order to overcome the well-known problem of unmeasurable scheduling variables within the convex observation; thus, the use of Lipschitz constants, differential mean value theorem, or robust techniques is avoided. As a result, the designing conditions are cast in terms of linear matrix inequalities and efficiently solved via convex optimization techniques. The effectiveness of the proposal is illustrated in the Furuta pendulum.

Downloads

Download data is not yet available.

References

Arceo, J., S´anchez, M., Estrada-Manzo, V., Bernal, M., 2018. Convex stability analysis of nonlinear singular systems via linear matrix inequalities. IEEE Transactions on Automatic Control 64 (4), 1740–1745.

Bergsten, P., Driankov, D., 2002. Observers for Takagi-Sugeno fuzzy systems. IEEE Trans. on Systems, Man and Cybernetics, Part B 32(1), 114–121.

Bernal, M., Guerra, T. M., 2010. Generalized non-quadratic stability of continuous-time Takagi-Sugeno models. IEEE Transactions on Fuzzy Systems

(4), 815–822.

Besanc¸on, G., 2003. High-gain observation with disturbance attenuation and application to robust fault detection. Automatica 39 (6), 1095–1102.

Blandeau, M., Estrada-Manzo, V., Guerra, T.-M., Pudlo, P., Gabrielli, F., 2018. Fuzzy unknown input observer for understanding sitting control of persons living with spinal cord injury. Engineering Applications of Artificial Intelligence 67, 381–389.

Boizot, N., Busvelle, E., Gauthier, J.-P., 2010. An adaptive high-gain observer for nonlinear systems. Automatica 46 (9), 1483–1488.

Boyd, S., Ghaoui, L. E., Feron, E., Belakrishnan, V., 1994. Linear Matrix Inequalities in System and Control Theory. Vol. 15. SIAM: Studies In Applied Mathematics, Philadelphia, USA.

Campos, V., Souza, F., Torres, L., Palhares, R., 2013. New stability conditions based on piecewise fuzzy Lyapunov functions and tensor product transformations. IEEE Transactions on Fuzzy Systems 21 (4), 748–760.

Daafouz, J., Bernussou, J., 2001. Parameter dependent Lyapunov functions for discrete time systems with time varying parametric uncertainties. Systems & control letters 43 (5), 355–359.

Dai, L., 1989. Singular control systems. Vol. 118. Springer.

Di Franco, P., Scarciotti, G., Astolfi, A., 2019. A globally stable algorithm for the integration of high-index di erential-algebraic systems. IEEE Transactions on Automatic Control.

Ding, B., 2010. Homogeneous polynomially nonquadratic stabilization of discrete-time Takagi-Sugeno systems via nonparallel distributed compensation law. IEEE Transaction on Automatic Control 18 (5), 994–1000.

Estrada-Manzo, V., Lendek, Z., Guerra, T. M., 2014. Discrete-time Takagi- Sugeno descriptor models: observer design. In: Proceedings of the IFAC 19th World Congress. pp. 7965–7969.

Estrada-Manzo, V., Lendek, Z., Guerra, T. M., 2016. Generalized lmi observer design for discrete-time nonlinear descriptor models. Neurocomputing 182, 210–220.

Frank, P. M., 1990. Fault diagnosis in dynamic systems using analytical and knowledge-based redundancy: A survey and some new results. automatica 26 (3), 459–474.

Furuta, K., Yamakita, M., Kobayashi, S., 1992. Swing-up control of inverted pendulum using pseudo-state feedback. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 206 (4), 263–269.

Gonz´alez, T., Bernal, M., Sala, A., Aguiar, B., 2016. Cancellation-based nonquadratic controller design for nonlinear systems via Takagi–Sugeno models. IEEE Transactions on cybernetics 47 (9), 2628–2638.

Guan, Y.and Saif, M., 1991. A novel approach to the design of unknown input observers. IEEE Transactions on Automatic Control 36 (5), 632–635.

Guerra, T., M´arquez, R., Kruszewski, A., Bernal, M., 2018. H1 LMI-based observer design for nonlinear systems via Takagi-Sugeno models with unmeasured premise variables. IEEE Transactions on Fuzzy Systems 26 (3), 1498–1509.

Guerra, T. M., Kerkeni, H., Lauber, J., Vermeiren, L., 2012. An ecient Lyapunov function for discrete T-S models: Observer design. IEEE Transactions on Fuzzy Systems 20 (1), 187–192.

Guerra, T. M., Vermeiren, L., 2004. LMI-based relaxed non-quadratic stabilization conditions for nonlinear systems in Takagi-Sugeno’s form. Automatica 40 (5), 823–829.

Ichalal, D., Marx, B., Mammar, S., Maquin, D., Ragot, J., 2018. How to cope with unmeasurable premise variables in Takagi–Sugeno observer design: dynamic extension approach. Engineering Applications of Artificial Intelligence 67, 430–435.

Ichalal, D., Marx, B., Ragot, J., Maquin, D., 2010. State estimation of Takagi– Sugeno systems with unmeasurable premise variables. IET Control Theory & Applications 4 (5), 897–908.

Khalil, H., 2002. Nonlinear Systems, 3rd Edition. Prentice Hall, New Jersey, USA.

Kruszewski, A.,Wang, R., Guerra, T. M., 2008. Nonquadratic stabilization conditions for a class of uncertain nonlinear discrete time TS fuzzy models: a new approach. IEEE Transactions on Automatic Control 53 (2), 606–611.

Lee, D. H., Kim, D. W., 2014. Relaxed LMI conditions for local stability and local stabilization of continuous-time Takagi-Sugeno fuzzy systems. IEEE Transactions on Cybernetics 44 (3), 394–405.

Lendek, Z., Guerra, T. M., Babuˇska, R., De-Schutter, B., 2010. Stability Analysis and Nonlinear Observer Design Using Takagi-Sugeno Fuzzy Models. Springer-Verlag, Netherlands.

Lewis, F., Dawson, D., Abdallah, C., 2003. Robot manipulator control: theory and practice. CRC Press.

L´opez-Estrada, F.-R., Astorga-Zaragoza, C.-M., Theilliol, D., Ponsart, J. C., Valencia-Palomo, G., Torres, L., 2017. Observer synthesis for a class of takagi–sugeno descriptor system with unmeasurable premise variable. application to fault diagnosis. International Journal of Systems Science 48 (16), 3419–3430.

Lor´ıa, A., Panteley, E., Zavala, A., 2009. Adaptive observers with persistency of excitation for synchronization of chaotic systems. IEEE Transactions on Circuits and Systems I: Regular Papers 56 (12), 2703–2716.

Luenberger, D., 1971. An introduction to observers. IEEE Trans. on Automatic Control 16, 596–602.

Mahmoud, M., 1982. Design of observer-based controllers for a class of discrete systems. Automatica 18 (3), 323–328.

Noh, D., Jo, N. H., Seo, J. H., 2004. Nonlinear observer design by dynamic observer error linearization. IEEE Transactions on Automatic Control 49 (10), 1746–1753.

Ogata, K., 2001. Modern control engineering. Prentice Hall PTR, NJ, USA.

Oh, S., Khalil, H. K., 1997. Nonlinear output-feedback tracking using high-gain observer and variable structure control. Automatica 33 (10), 1845–1856.

Ohtake, H., Tanaka, K., Wang, H. O., 2001. Fuzzy modeling via sector nonlinearity concept. In: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference. Vol. 1. pp. 127–132.

Oliveira, M., Skelton, R., 2001. Stability tests for constrained linear systems.

In: Perspectives in robust control. Vol. 268 of Lecture Notes in Control and Information Sciences. Springer-Verlag, Berlin, pp. 241–257.

Quintana, D., Estrada-Manzo, V., Bernal, M., 2020. An exact handling of the gradient for overcoming persistent problems in nonlinear observer design via convex optimization techniques. Fuzzy Sets and Systems (In press). DOI: https://doi.org/10.1016/j.fss.2020.04.012

Rajamani, R., 1998. Observers for Lipschitz nonlinear systems. IEEE Transactions on Automatic Control 43 (3), 397–401.

Sala, A., Pitarch, J. L., Bernal, M., Jaadari, A., Guerra, T. M., 2011. Fuzzy polynomial observers. In: Proceedings of the 18th IFAC World Congress. pp. 12772–12776.

Scherer, C., 2004. Linear Matrix Inequalities in Control Theory. Delf University, Delf, The Netherlands.

Spurgeon, S. K., 2008. Sliding mode observers: a survey. International Journal of Systems Science 39 (8), 751–764.

Takagi, T., Sugeno, M., 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics 15 (1), 116–132.

Tanaka, K., Hori, T., Wang, H., 2003. A multiple Lyapunov function approach to stabilization of fuzzy control systems. IEEE Transactions on Fuzzy Systems 11 (4), 582–589.

Tanaka, K., Wang, H., 2001. Fuzzy Control Systems Design and Analysis: A linear matrix inequality approach. John Wiley & Sons, New York.

Tuan, H., Apkarian, P., Narikiyo, T., Yamamoto, Y., 2001. Parameterized linear matrix inequality techniques in fuzzy control system design. IEEE Transactions on Fuzzy Systems 9 (2), 324–332.

Xie, X.-P., Yue, D., Peng, C., 2015. Observer design of discrete-timeT-S fuzzy systems via multi-instant augmented multi-indexed matrix approach. Journal of the Franklin Institute 352 (7), 2899–2919.

Published
2021-01-05
How to Cite
Martínez-Velázquez, F. J., Estrada-Manzo, V., & Bernal-Reza, M. (2021). Nonlinear Observer Design for Mechatronic Systems via LMIs. Pädi Boletín Científico De Ciencias Básicas E Ingenierías Del ICBI, 8(16), 75-81. https://doi.org/10.29057/icbi.v8i16.5973