Abstract
This chapter presents a variety of nonlinear models related to the Takagi–Sugeno form (TS): some of them are directly inspired from the fuzzy approach in which they first appeared; others are generalisations or derivative structures of the original one. Their common characteristic is the presence of convex structures of the state, time, or exogenous parameters. Since this book is focussed on model-based approaches, different methods are presented to construct the convex models from a given nonlinear one.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Existence and uniqueness of solutions for systems of the form \(E\dot{\boldsymbol{x}}(t)=A\boldsymbol{x}(t)\), with A and E square matrices of the same size, are guaranteed if \(\det (sE-A)\ne 0\) for some s; such systems are called regular [52]. Since this condition is only sufficient, the problem remains open for systems of the form (3.71).
- 2.
If \(T_1\) were not full rank, additional variable elimination and algebraic manipulations involving differentiation would be needed in order to set up a minimal representation (3.78) with invertible \(T_1\); these are the so-called high-index differential-algebraic equations. Alternatively, a descriptor system framework might be pursued, see Sect. 3.4; in fact, all LFT representations admit a descriptor form, see Sect. 3.5.3.
References
Kailath, T.: Linear Systems. Prentice Hall, Upper Saddle River, NJ (1980)
Franklin, G.F., Powell, J.D., Workman, M.L.: Digital Control of Dynamic Systems. Addison-Wesley, Boston, MA (1990)
Åström, K.J., Wittenmark, B.: Computer Controlled Systems. Prentice-Hall, Upper Saddle River, NJ (1990)
Boyd, S., El Ghaoui, L., Féron, E., Balakrishnan, V.: Linear Matrix Inequalities in System and Control Theory. Studies in Applied Mathematics, Society for Industrial and Applied Mathematics, Philadelphia, PA (1994)
Takagi, T., Sugeno, M.: Fuzzy identification of systems and its applications to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)
Passino, K., Yurkovich, S.: Fuzzy Control. Addisson-Wesley Longman, Menlo Park, CA (1998)
Tanaka, K., Wang, H.O.: Fuzzy Control System Design and Analysis: A Linear Matrix Inequality Approach. Wiley, New York (2001)
Tanaka, K., Wang, H.O.: Fuzzy Control Systems Design and Analysis. Wiley, New York (2001)
Sala, A.: Generalising quasi-lpv and cdi models to quasi-convex difference inclusions. IFAC-PapersOnLine 50(1), 7560–7565 (2017)
Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control. Springer, New York (1993)
Abonyi, J., Babuška, R., Szeifert, F.: Modified Gath-Geva fuzzy clustering for identification of Takagi–Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. B Cybern. 32(5), 612–621 (2002)
Babuška, R., van der Veen, P., Kaymak, U.: Improved covariance estimation for Gustafson-Kessel clustering. In: Proceedings of the IEEE International Conference on Fuzzy Systems, vol. 2. IEEE, New York, pp. 1081–1085 (2002)
Johansen, T., Babuška, R.: Multiobjective identification of Takagi–Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 11(6), 847–860 (2003)
Kukolj, D., Levi, E.: Identification of complex systems based on neural and Takagi–Sugeno fuzzy model. IEEE Trans. Syst. Man Cybern. B Cybern. 34(1), 272–282 (2004)
Kaymak, U., van den Berg, J.: On constructing probabilistic fuzzy classifiers from weighted fuzzy clustering. In: Proceedings of the International Conference on Fuzzy Systems, vol. 1, pp. 395–400 (2004)
Angelov, P., Filev, D.: An approach to online identification of Takagi–Sugeno fuzzy models. IEEE Trans. Syst. Man Cybern. B Cybern. 34(1), 484–498 (2004)
Ohtake, H., Tanaka, K., Wang, H.: Fuzzy modeling via sector nonlinearity concept. In: Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference, vol. 1, pp. 127–132 (2001)
Wang, L.-X., Mendel, J.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Networks 3(5), 807–814 (1992)
Kosko, B.: Fuzzy systems as universal approximators. IEEE Trans. Comput. 43(11), 1329–1333 (1994)
Ying, H.: Sufficient conditions on general fuzzy systems as function approximators. Automatica 30(3), 521–525 (1994)
Fantuzzi, C., Rovatti, R.: On the approximation capabilities of the homogeneous Takagi–Sugeno model. In: Proceedings of the Fifth IEEE International Conference on Fuzzy Systems, pp. 1067–1072. IEEE, New York (1996)
Johansen, T.A., Shorten, R., Murray-Smith, R.: On the interpretation and identification of dynamic Takagi–Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 8(3), 297–313 (2000)
Kiriakidis, K.: Nonlinear modeling by interpolation between linear dynamics and its application in control. J. Dyn. Syst. Meas. Contr. 129(6), 813–824 (2007)
Taniguchi, T., Tanaka, K., Ohtake, H., Wang, H.: Model construction, rule reduction, and robust compensation for generalized form of Takagi–Sugeno fuzzy systems. IEEE Trans. Fuzzy Syst. 9(4), 525–538 (2001)
Sanchez, M., Bernal, M.: A convex approach for reducing conservativeness of Kharitonovs-based robustness analysis. In: Proceedings of the 20th IFAC World Congress, pp. 855–860 (2017)
Ariño, C., Sala, A.: Relaxed LMI conditions for closed-loop fuzzy systems with tensor-product structure. Eng. Appl. Artif. Intell. 20(8), 1036–1046 (2007)
Guerra, T., Guerra, J., Bernal, M.: LMI-based dynamic control of non-affine nonlinear systems via Takagi–Sugeno models. In: 2018 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–6. IEEE, New York (2018)
Robles, R., Sala, A., Bernal, M., Gonzalez, T.: Choosing a Takagi–Sugeno model for improved performance. In: Proceedings of the 2015 IEEE International Conference on Fuzzy Systems, pp. 1–6. IEEE, New York (2015)
Robles, R., Sala, A., Bernal, M., González, T.: Subspace-based Takagi–Sugeno modeling for improved LMI performance. IEEE Trans. Fuzzy Syst. 25(4), 754–767 (2017)
Robles, R., Sala, A., Bernal, M., Gonzalez, T.: Optimal-performance Takagi–Sugeno models via the LMI null space. IFAC-PapersOnLine 49(5), 13–18 (2016)
Khalil, H.K.: Nonlinear Systems. Prentice-Hall, Upper Saddle River, NJ (2002)
Johansson, M., Rantzer, A., Arzen, K.: Piecewise quadratic stability of fuzzy systems. IEEE Trans. Fuzzy Syst. 7(6), 713–722 (1999)
Feng, M., Harris, C.J.: Piecewise Lyapunov stability conditions of fuzzy systems. IEEE Trans. Syst. Man Cybern. B Cybern. 31(2), 259–262 (2001)
Kwiatkowski, A., Werner, H.: PCA-based parameter set mappings for LPV models with fewer parameters and less overbounding. IEEE Trans. Control Syst. Technol. 16(4), 781–788 (2008)
Loke, M., Barker, R.: Rapid least-squares inversion of apparent resistivity pseudosections by a quasi-newton method 1. Geophys. Prospect. 44(1), 131–152 (1996)
Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Numerical Analysis, pp. 105–116. Springer, Berlin (1978)
Godefroid, P., Klarlund, N., Sen, K.: Dart: directed automated random testing. ACM Sigplan Notices 40(6), 213–223 (2005)
Krstic, M., Kanellakopoulos, I., Kokotovic, P.V., et al.: Nonlinear and adaptive control design, vol. 222. Wiley, New York (1995)
Polycarpou, M.: Stable adaptive neural control scheme for nonlinear systems. IEEE Trans. Autom. Control 41(3), 447–451 (1996)
Han, H., Su, C.-Y., Stepanenko, Y.: Adaptive control of a class of nonlinear systems with nonlinearly parameterized fuzzy approximators. IEEE Trans. Fuzzy Syst. 9(2), 315–323 (2001)
Rantzer, A., Johansson, M.: Piecewise linear quadratic optimal control. IEEE Trans. Autom. Control 45(4), 629–637 (2000)
Gonzalez, T., Sala, A., Bernal, M., Robles, R.: Invariant sets of nonlinear models via piecewise affine Takagi–Sugeno model. In: Proceedings of the 2015 IEEE International Conference on Fuzzy Systems. IEEE, New York (2015)
Gonzalez, T., Bernal, M.: Progressively better estimates of the domain of attraction for nonlinear systems via piecewise Takagi–Sugeno models: Stability and stabilization issues. Fuzzy Sets Syst. 297, 73–95 (2016)
Gonzalez, T., Sala, A., Bernal, M., Aguiar, B.: Piecewise-Takagi–Sugeno asymptotically exact estimation of the domain of attraction of nonlinear systems. J. Franklin Inst. 354(3), 1514–1541 (2017)
Gonzalez, T., Bernal, M., Marquez, R.: Stability analysis of nonlinear models via exact piecewise Takagi–Sugeno models. In: 19th World Congress of the International Federation of Automatic Control (IFAC) (2014)
Hedlund, S., Johansson, M.: A Toolbox for Computational Analysis of Piecewise Quadratic Linear Systems. Department of Automatic Control, Lund, Sweden (1999)
Sala, A., Ariño, C.: Polynomial fuzzy models for nonlinear control: A Taylor series approach. IEEE Trans. Fuzzy Syst. 17(6), 1284–1295 (2009)
Tanaka, K., Yoshida, H., Ohtake, H., Wang, H.: A sum of squares approach to modeling and control of nonlinear dynamical systems with polynomial fuzzy systems. IEEE Trans. Fuzzy Syst. 17(4), 911–922 (2009)
Prajna, S., Papachristodoulou, A., Seiler, P., Parrilo, P.A.: SOSTOOLS: Sum of Squares Optimization Toolbox for MATLAB. California Inst. Technol, Pasadena, CA (2004)
Campbell, S.: Singular Systems of Differential Equations II. Research Notes in Mathematics. Pitman Publishing, London (1982)
Louis, F.L.: A survey of linear singular systems. J. Circuits, Systems Signal Process. 5(1), 3–36 (1986)
Dai, L.: Singular control systems. Lecture Notes in Control and Information Sciences, Springer, Berlin (1989)
Luenberger, D.G.: Dynamic equations in descriptor form. IEEE Trans. Autom. Control 22(3), 312–321 (1977)
Luenberger, D.G.: Non-linear descriptor systems. J. Econ. Dyn. Control 1(3), 219–242 (1979)
Pantelides, C.C.: The consistent initialization of differential-algebraic systems. SIAM J. Sci. Stat. Comput. 9(2), 213–231 (1988)
Yang, C., Sun, J., Zhang, Q., Ma, X.: Lyapunov stability and strong passivity analysis for nonlinear descriptor systems. IEEE Trans. Circuits Syst. I: Regular Papers 60(4), 1003–1012 (2013)
Kumar, A., Daoutidis, P.: Control of nonlinear differential algebraic equation systems: an overview. In: Nonlinear Model Based Process Control, pp. 311–344. Springer, Berlin (1998)
Aliyu, M., Boukas, E.-K.: H \(\infty \) filtering for nonlinear singular systems. IEEE Trans. Circuits Syst. I: Regular Papers 59(10), 2395–2404 (2012)
Wu, L., Shi, P., Gao, H.: State estimation and sliding-mode control of Markovian jump singular systems. IEEE Trans. Autom. Control 55(5), 1213–1219 (2010)
Li, F., Du, C., Yang, C., Gui, W.: Passivity-based asynchronous sliding mode control for delayed singular Markovian jump systems. IEEE Trans. Autom. Control 63(8), 2715–2721 (2017)
Duan, G.R.: Analysis and Design of Descriptor Linear Systems. Springer, New York (2010)
Xu, S., Lam, J.: Robust Control and Filtering of Singular Systems. Springer, Berlin (2006)
Nedialkov, N., Pryce, J., Tan, G.: Algorithm 948 DAESA: A MATLAB tool for structural analysis of differential-algebraic equations software. ACM Trans. Math. Software (TOMS) 41(2), 12 (2015)
Tuan, H.D., Apkarian, P., Narikiyo, T., Kanota, M.: New fuzzy control model and dynamic output feedback parallel distributed compensation. IEEE Trans. Fuzzy Syst. 12(1), 13–21 (2004)
Estrada-Manzo, V., Lendek, Z., Guerra, T.M., Pudlo, P.: Controller design for discrete-time descriptor models: a systematic LMI approach. IEEE Trans. Fuzzy Syst. 23(5), 1608–1621 (2015)
Lewis, F., Dawson, D., Abdallah, C.: Robot Manipulator Control: Theory and Practice. Marcel Dekker Inc, New York (2004)
Guelton, K., Delprat, S., Guerra, T.-M.: An alternative to inverse dynamics joint torques estimation in human stance based on a Takagi–Sugeno unknown-inputs observer in the descriptor form. Control. Eng. Pract. 16(12), 1414–1426 (2008)
Schulte, H., Guelton, K.: Descriptor modelling towards control of a two-link pneumatic robot manipulator: a T–S multimodel approach. Nonlinear Anal. Hybrid Syst 3(2), 124–132 (2009)
Vermeiren, L., Dequidt, A., Afroun, M., Guerra, T.M.: Motion control of planar parallel robot using the fuzzy descriptor system approach. ISA Trans. 51, 596–608 (2012)
Verghese, G.C., Levy, B.C., Kailath, T.: A generalized state-space for singular systems. IEEE Trans. Autom. Control 26(4), 811–831 (1981)
Goldstein, H., Poole, C.P., Safko, J.L.: Classical Mechanics: Pearson New International Edition. Pearson Higher Ed, London (2014)
Kibble, T., Berkshire, F.H.: Classical Mechanics. Imperial College Press, London (2004)
Arceo, J.C., Sánchez, M., Estrada-Manzo, V., Bernal, M.: Convex stability analysis of nonlinear singular systems via linear matrix inequalities. IEEE Trans. Autom. Control 64(4), 1740–1745 (2018)
Apkarian, P., Gahinet, P.: A convex characterization of gain-scheduled h\(_\infty \) controllers. IEEE Trans. Autom. Control 40(5), 853–864 (1995)
Apkarian, P., Adams, R.J.: Advanced gain-scheduling techniques for uncertain systems. IEEE Trans. Control Syst. Technol. 6(1), 21–32 (1998)
Scherer, C.W.: LPV control and full block multipliers. Automatica 37(3), 361–375 (2001)
Wu, F., Dong, K.: Gain-scheduling control of LFT systems using parameter-dependent Lyapunov functions. Automatica 42(1), 39–50 (2006)
Rugh, W.J., Shamma, J.S.: Research on gain scheduling. Automatica 36(10), 1401–1425 (2000)
Zhou, K., Doyle, J.C., Glover, K., et al.: Robust and Optimal Control, vol. 272. Prentice Hall, Upper Saddle River, NJ (1996)
Gharibzahedi, S.M.T., Mousavi, S.M., Khodaiyan, F., Hamedi, M.: Optimization and characterization of walnut beverage emulsions in relation to their composition and structure. Int. J. Biol. Macromol. 50(2), 376–384 (2012)
Robles, R., Sala, A., Bernal, M.: Performance-oriented quasi-LPV modeling of nonlinear systems. Int. J. Robust Nonlinear Control 29(5), 1230–1248 (2019)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Bernal, M., Sala, A., Lendek, Z., Guerra, T.M. (2022). Modelling via Convex Structures. In: Analysis and Synthesis of Nonlinear Control Systems. Studies in Systems, Decision and Control, vol 408. Springer, Cham. https://doi.org/10.1007/978-3-030-90773-0_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-90773-0_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90772-3
Online ISBN: 978-3-030-90773-0
eBook Packages: Intelligent Technologies and Robotics