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Modelling via Convex Structures

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Analysis and Synthesis of Nonlinear Control Systems

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.

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Notes

  1. 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. 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

  1. Kailath, T.: Linear Systems. Prentice Hall, Upper Saddle River, NJ (1980)

    MATH  Google Scholar 

  2. Franklin, G.F., Powell, J.D., Workman, M.L.: Digital Control of Dynamic Systems. Addison-Wesley, Boston, MA (1990)

    MATH  Google Scholar 

  3. Åström, K.J., Wittenmark, B.: Computer Controlled Systems. Prentice-Hall, Upper Saddle River, NJ (1990)

    Google Scholar 

  4. 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)

    Book  MATH  Google Scholar 

  5. 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)

    Article  MATH  Google Scholar 

  6. Passino, K., Yurkovich, S.: Fuzzy Control. Addisson-Wesley Longman, Menlo Park, CA (1998)

    MATH  Google Scholar 

  7. Tanaka, K., Wang, H.O.: Fuzzy Control System Design and Analysis: A Linear Matrix Inequality Approach. Wiley, New York (2001)

    Book  Google Scholar 

  8. Tanaka, K., Wang, H.O.: Fuzzy Control Systems Design and Analysis. Wiley, New York (2001)

    Book  Google Scholar 

  9. Sala, A.: Generalising quasi-lpv and cdi models to quasi-convex difference inclusions. IFAC-PapersOnLine 50(1), 7560–7565 (2017)

    Article  Google Scholar 

  10. Driankov, D., Hellendoorn, H., Reinfrank, M.: An Introduction to Fuzzy Control. Springer, New York (1993)

    Book  MATH  Google Scholar 

  11. 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)

    Article  Google Scholar 

  12. 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)

    Google Scholar 

  13. Johansen, T., Babuška, R.: Multiobjective identification of Takagi–Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 11(6), 847–860 (2003)

    Article  Google Scholar 

  14. 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)

    Article  Google Scholar 

  15. 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)

    Google Scholar 

  16. 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)

    Article  Google Scholar 

  17. 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)

    Google Scholar 

  18. Wang, L.-X., Mendel, J.: Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE Trans. Neural Networks 3(5), 807–814 (1992)

    Article  Google Scholar 

  19. Kosko, B.: Fuzzy systems as universal approximators. IEEE Trans. Comput. 43(11), 1329–1333 (1994)

    Article  MATH  Google Scholar 

  20. Ying, H.: Sufficient conditions on general fuzzy systems as function approximators. Automatica 30(3), 521–525 (1994)

    Article  MATH  Google Scholar 

  21. 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)

    Google Scholar 

  22. 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)

    Article  Google Scholar 

  23. Kiriakidis, K.: Nonlinear modeling by interpolation between linear dynamics and its application in control. J. Dyn. Syst. Meas. Contr. 129(6), 813–824 (2007)

    Article  Google Scholar 

  24. 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)

    Article  Google Scholar 

  25. 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)

    Google Scholar 

  26. 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)

    Article  Google Scholar 

  27. 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)

    Google Scholar 

  28. 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)

    Google Scholar 

  29. 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)

    Article  Google Scholar 

  30. 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)

    Article  MathSciNet  Google Scholar 

  31. Khalil, H.K.: Nonlinear Systems. Prentice-Hall, Upper Saddle River, NJ (2002)

    MATH  Google Scholar 

  32. Johansson, M., Rantzer, A., Arzen, K.: Piecewise quadratic stability of fuzzy systems. IEEE Trans. Fuzzy Syst. 7(6), 713–722 (1999)

    Article  MATH  Google Scholar 

  33. Feng, M., Harris, C.J.: Piecewise Lyapunov stability conditions of fuzzy systems. IEEE Trans. Syst. Man Cybern. B Cybern. 31(2), 259–262 (2001)

    Article  Google Scholar 

  34. 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)

    Article  Google Scholar 

  35. 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)

    Article  Google Scholar 

  36. Moré, J.J.: The Levenberg-Marquardt algorithm: implementation and theory. In: Numerical Analysis, pp. 105–116. Springer, Berlin (1978)

    Chapter  Google Scholar 

  37. Godefroid, P., Klarlund, N., Sen, K.: Dart: directed automated random testing. ACM Sigplan Notices 40(6), 213–223 (2005)

    Article  Google Scholar 

  38. Krstic, M., Kanellakopoulos, I., Kokotovic, P.V., et al.: Nonlinear and adaptive control design, vol. 222. Wiley, New York (1995)

    MATH  Google Scholar 

  39. Polycarpou, M.: Stable adaptive neural control scheme for nonlinear systems. IEEE Trans. Autom. Control 41(3), 447–451 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  40. 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)

    Article  Google Scholar 

  41. Rantzer, A., Johansson, M.: Piecewise linear quadratic optimal control. IEEE Trans. Autom. Control 45(4), 629–637 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  42. 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)

    Google Scholar 

  43. 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)

    Article  MathSciNet  MATH  Google Scholar 

  44. 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)

    Article  MathSciNet  MATH  Google Scholar 

  45. 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)

    Google Scholar 

  46. Hedlund, S., Johansson, M.: A Toolbox for Computational Analysis of Piecewise Quadratic Linear Systems. Department of Automatic Control, Lund, Sweden (1999)

    Google Scholar 

  47. Sala, A., Ariño, C.: Polynomial fuzzy models for nonlinear control: A Taylor series approach. IEEE Trans. Fuzzy Syst. 17(6), 1284–1295 (2009)

    Article  Google Scholar 

  48. 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)

    Article  Google Scholar 

  49. Prajna, S., Papachristodoulou, A., Seiler, P., Parrilo, P.A.: SOSTOOLS: Sum of Squares Optimization Toolbox for MATLAB. California Inst. Technol, Pasadena, CA (2004)

    Google Scholar 

  50. Campbell, S.: Singular Systems of Differential Equations II. Research Notes in Mathematics. Pitman Publishing, London (1982)

    Google Scholar 

  51. Louis, F.L.: A survey of linear singular systems. J. Circuits, Systems Signal Process. 5(1), 3–36 (1986)

    Article  MathSciNet  Google Scholar 

  52. Dai, L.: Singular control systems. Lecture Notes in Control and Information Sciences, Springer, Berlin (1989)

    Book  MATH  Google Scholar 

  53. Luenberger, D.G.: Dynamic equations in descriptor form. IEEE Trans. Autom. Control 22(3), 312–321 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  54. Luenberger, D.G.: Non-linear descriptor systems. J. Econ. Dyn. Control 1(3), 219–242 (1979)

    Article  Google Scholar 

  55. Pantelides, C.C.: The consistent initialization of differential-algebraic systems. SIAM J. Sci. Stat. Comput. 9(2), 213–231 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  56. 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)

    Article  MathSciNet  MATH  Google Scholar 

  57. 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)

    Chapter  Google Scholar 

  58. Aliyu, M., Boukas, E.-K.: H \(\infty \) filtering for nonlinear singular systems. IEEE Trans. Circuits Syst. I: Regular Papers 59(10), 2395–2404 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  59. 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)

    Article  MathSciNet  MATH  Google Scholar 

  60. 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)

    Article  MathSciNet  MATH  Google Scholar 

  61. Duan, G.R.: Analysis and Design of Descriptor Linear Systems. Springer, New York (2010)

    Book  MATH  Google Scholar 

  62. Xu, S., Lam, J.: Robust Control and Filtering of Singular Systems. Springer, Berlin (2006)

    MATH  Google Scholar 

  63. 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)

    Article  MathSciNet  MATH  Google Scholar 

  64. 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)

    Article  MATH  Google Scholar 

  65. 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)

    Article  Google Scholar 

  66. Lewis, F., Dawson, D., Abdallah, C.: Robot Manipulator Control: Theory and Practice. Marcel Dekker Inc, New York (2004)

    Google Scholar 

  67. 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)

    Article  Google Scholar 

  68. 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)

    Article  MathSciNet  MATH  Google Scholar 

  69. 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)

    Article  Google Scholar 

  70. Verghese, G.C., Levy, B.C., Kailath, T.: A generalized state-space for singular systems. IEEE Trans. Autom. Control 26(4), 811–831 (1981)

    Article  MathSciNet  MATH  Google Scholar 

  71. Goldstein, H., Poole, C.P., Safko, J.L.: Classical Mechanics: Pearson New International Edition. Pearson Higher Ed, London (2014)

    Google Scholar 

  72. Kibble, T., Berkshire, F.H.: Classical Mechanics. Imperial College Press, London (2004)

    Book  MATH  Google Scholar 

  73. 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)

    Article  MathSciNet  MATH  Google Scholar 

  74. Apkarian, P., Gahinet, P.: A convex characterization of gain-scheduled h\(_\infty \) controllers. IEEE Trans. Autom. Control 40(5), 853–864 (1995)

    Article  MathSciNet  MATH  Google Scholar 

  75. Apkarian, P., Adams, R.J.: Advanced gain-scheduling techniques for uncertain systems. IEEE Trans. Control Syst. Technol. 6(1), 21–32 (1998)

    Article  MATH  Google Scholar 

  76. Scherer, C.W.: LPV control and full block multipliers. Automatica 37(3), 361–375 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  77. Wu, F., Dong, K.: Gain-scheduling control of LFT systems using parameter-dependent Lyapunov functions. Automatica 42(1), 39–50 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  78. Rugh, W.J., Shamma, J.S.: Research on gain scheduling. Automatica 36(10), 1401–1425 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  79. Zhou, K., Doyle, J.C., Glover, K., et al.: Robust and Optimal Control, vol. 272. Prentice Hall, Upper Saddle River, NJ (1996)

    MATH  Google Scholar 

  80. 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)

    Article  Google Scholar 

  81. Robles, R., Sala, A., Bernal, M.: Performance-oriented quasi-LPV modeling of nonlinear systems. Int. J. Robust Nonlinear Control 29(5), 1230–1248 (2019)

    Article  MathSciNet  MATH  Google Scholar 

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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

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