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Reseach Article

Solution of matrix Riccati differential equation for nonlinear singular system using neural networks

by J. Abdul Samath, N. Selvaraju
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 29
Year of Publication: 2010
Authors: J. Abdul Samath, N. Selvaraju
10.5120/575-181

J. Abdul Samath, N. Selvaraju . Solution of matrix Riccati differential equation for nonlinear singular system using neural networks. International Journal of Computer Applications. 1, 29 ( February 2010), 48-55. DOI=10.5120/575-181

@article{ 10.5120/575-181,
author = { J. Abdul Samath, N. Selvaraju },
title = { Solution of matrix Riccati differential equation for nonlinear singular system using neural networks },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 29 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 48-55 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number29/575-181/ },
doi = { 10.5120/575-181 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:01.731064+05:30
%A J. Abdul Samath
%A N. Selvaraju
%T Solution of matrix Riccati differential equation for nonlinear singular system using neural networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 29
%P 48-55
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, the solution of the matrix Riccati differential equation(MRDE) for nonlinear singular system is obtained using neural networks. The goal is to provide optimal control with reduced calculus effort by comparing the solutions of the MRDE obtained from well known traditional Runge Kutta(RK)method and nontraditional neural network method. Accuracy of the neural solution to the problem is qualitatively better. The advantage of the proposed approach is that, once the network is trained, it allows instantaneous evaluation of solution at any desired number of points spending negligible computing time and memory. The computation time of the proposed method is shorter than the traditional RK method. An illustrative numerical example is presented for the proposed method.

References
  1. S. P. Banks, Exact boundary controllability and optimal control for a generalized Korteweg de Vries equation,Internat. Nonlinear Anal. 47 2001) 5537-5546.
  2. P. Balasubramaniam, J. Abdul Samath, N. Kumaresan and A. Vincent Antony Kumar, Solution of matrix Riccati differential equation for the linear quadratic singular system using neural networks, Appl. Math. Comput. 182(2006) 1832–1839.
  3. P. Balasubramaniam, J. Abdul Samath and N. Kumaresan, Neuro Approach for solving Matrix Riccati Differential Equations, Neural, Parallel Sci. Comput., 2 (2007) 125–135.
  4. S. P. Banks and K. J. Mhana, Optimal control and stabilization for nonlinear systems, IMA J. Math. Control Inf.,9 (1992), 179–196.
  5. K. E. Brenan, S. L. Campbell and L.R. Petzold, Numerical Solution of Initial Value Problems in Differential-Algebraic Equations, Elsevier, New York, 1989.
  6. S. L. Campbell, Singular Systems of Differential Equations, Pitman, Marshfield, MA, 1980.
  7. S. L. Campbell, Singular Systems of Differential Equations II, Pitman, Marshfield, MA, 1982.
  8. T. Cimen and S. P. Banks, Nonlinear optimal tracking control with application to super-tankers for autopilot design, Automatica, 40 (2004), 1845–1863.
  9. S. L. Campbell and E. Griepentrog, Solvability of general differential- algebraic equations, SIAM J. Sci. Comput., 16 (1995), 257–270.
  10. F. C. Chen and C. C. Liu, Adaptively controlling nonlinear continuous-time systems using multilayer neural networks, IEEE Trans. Automat. Control, 39 (1994), 1306–1310.
  11. L. Dai, Singular control systems, Lecture Notes in Control and Information Sciences, Springer , New York, 1989.
  12. G. Da Prato and A. Ichikawa, Quadratic control for linear periodic systems, Appl. Math. Optim.,18 (1988), 39–66.
  13. S. W. Ellacott, Aspects of the numerical analysis of neural networks, Acta Numer., 5 (1994), 145–202.
  14. F. M. Ham and E. G. Collins, A neurocomputing approach for solving the algebraic matrix Riccati equation,Proceedings IEEE International Conference on Neural networks, 1 (1996), 617 – 622.
  15. M. Jamshidi, An overview on the solutions of the algebraic matrix Riccati equation and related problems, LargeScale Systems, 1 (1980), 167–192.
  16. L. Jodar and E. Navarro, Closed analytical solution of Riccati type matrix differential equations, Indian J. Pureand Appl. Math., 23 (1992), 185–187.
  17. A. Karakasoglu, S. L. Sudharsanan and M. K. Sundareshan, Identification and decentralized adaptive control usingneural networks with application to robatic manipulators, IEEE Trans. Neural Networks, 4 (1993), 919–930.
  18. I. E. Lagaris, A. Likas and D. I. Fotiadis, Artificial neural networks for solving ordinary and partial differentialequations, IEEE Trans. Neural Networks 9 (1998), 987–1000.
  19. F. L. Lewis, A Survey of Linear Singular Systems, Circ. Syst. Sig. Proc., 5(1)(1986), 3–36.
  20. N. Lovren and M. Tomic, Analytic solution of the Riccati equation for the homing missile linear quadratic control problem, J. Guidance. Cont. Dynamics , 17 (1994), 619–621.
  21. D. Mccaffrey and S. P. Banks, Lagrangian manifolds and asymptotically optimal stabilizing feedback control, Syst.Control Lett., 43 (2001), 219-224.
  22. N. H. McClamroch, Feedback stabilization of control systems described by a class of nonlinear differential algebraicequations, Systems Control Lett., 15 (1990), 53–60.
  23. W. T. Miller, R. Sutton and P. Werbos, Neural networks for control, Cambridge, MA, MIT Press, 1990.
  24. K. S. Narendra and F. L. Lewis, Special issue on neural network feedback control, Automatica, 37 (2001), 1147-1148.
  25. K. S. Narendra and K. Parathasarathy, Identification and control of dynamical systems using neural networks,IEEE Trans. Neural networks, 1 (1990), 4–27.
  26. A. P. Paplinski, Lecture notes on feedforward multilayer neural networks, NNet(L.5), 2004.
  27. M. M. Polycarpou, Stable adaptive neural control scheme for nonlinear systems, IEEE Trans. Automat. Control,41 (1996), 447– 451.
  28. E. Polak, Optimisation-Algorithms and consistent approximations, New York, Springer. 1997
  29. T. Parisini and R. Zoppoli, Neural approximations for infinitehorizon optimal control of nonlinear stochasticsystems, IEEE Trans. Neural Networks 9 (1998), 1388–1408.
  30. M. Razzaghi, A Schur method for the solution of the matrix Riccati equation, Int. J. Math. and Math. Sci., 20 (1997), 335–338.
  31. M. Razzaghi, Solution of the matrix Riccati equation in optimal control, Information Sci., 16 (1978), 61–73.
  32. M. Razzaghi, A computational solution for a matrix Riccati differential equation, Numerical Math., 32 (1979), 271–279.
  33. G. A. Rovithakis and M. A. Christodoulou, Adaptive control of unknown plants using dynamical neural networks,IEEE Trans. Systems, Man and Cybernetics, 24 (1994), 400–412.
  34. N. Sadegh, A perceptron network for functional identification and control of nonlinear systems, IEEE Trans. Neural Networks, 4 (1993), 982–988.
  35. D. R. Vaughu, A negative exponential solution for the matrix Riccati equation, IEEE Trans Automat. Control, 14 (1969), 72–75.
  36. P. De. Wilde, Neural Network Models, Second ed., Springer-Verlag, London, 1997. J. Wang and G. Wu, A multilayer recurrent neural network for solving continuous-time algebraic Riccati equations, Neural Networks, 11 (1998), 939–950.
  37. K. Zhou and Khargonekar, An algebraic Riccati equation approach to H optimization, Systems Control Lett., 11 (1998), 85–91.
Index Terms

Computer Science
Information Sciences

Keywords

Matrix Riccati differential equation Nonlinear Optimal control Singular system Runge Kutta method and Neural networks