Uncertain Fractional Order Chaotic Systems Tracking Design via Adaptive Hybrid Fuzzy Sliding Mode Control

Authors

  • Tsung-Chih Lin Feng-Chia University, 40724, Taichung, Taiwan
  • Chia-Hao Kuo Ph.D Program in Electrical and Communications Engineering Feng-Chia University, Taichung, Taiwan
  • Valentina E. Balas Aurel Vlaicu University of Arad, Romania B-dul Revolutiei 77, 310130 Arad, Romania

Keywords:

Fractional order chaotic systems, fuzzy logic control, adaptive hybrid control

Abstract

In this paper, in order to achieve tracking performance of uncertain fractional order chaotic systems an adaptive hybrid fuzzy controller is proposed. During the design procedure, a hybrid learning algorithm combining sliding mode control and Lyapunov stability criterion is adopted to tune the free parameters on line by output feedback control law and adaptive law. A weighting factor, which can be adjusted by the trade-off between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive fuzzy controller and direct adaptive fuzzy controller. To confirm effectiveness of the proposed control scheme, the fractional order chaotic response system is fully illustrated to track the trajectory generated from the fractional order chaotic drive system. The numerical results show that tracking error and control effort can be made smaller and the proposed hybrid intelligent control structure is more flexible during the design process.

References

M. S. Tavazoei, M. Haeri, Synchronization of chaotic fractional-order systems via active sliding mode controller, Physica A, Vol. 387, pp. 57-70, 2008. http://dx.doi.org/10.1016/j.physa.2007.08.039

A.A. Kilbas, H.M. Srivastava and J.J. Trujillo, Theory and applications of fractional differential equations, North-Holland Math. Studies 204, Elsevier, Amsterdam, 2006.

I. Petras, A note on the fractional-order Chua's system, Chaos, Solitons & Fractals, 38 (1), pp. 140-14,7 2008.

X. Gao and J. Yu, Chaos in the fractional order periodically forced complex Duffing's oscillators, Chaos, Solitons & Fractals 26, pp. 1125-1133, 2005. http://dx.doi.org/10.1016/j.chaos.2004.12.030

J.G. Lu and G. Chen, A note on the fractional-order Chen system, Chaos, Solitons & Fractals 27, pp. 685-688, 2006. http://dx.doi.org/10.1016/j.chaos.2005.04.037

P. Arena and R. Caponetto, Bifurcation and chaos in non-integer order cellular neural networks, Int J Bifurcat Chaos 8 (7), pp. 1527-1539, 1998. http://dx.doi.org/10.1142/S0218127498001170

Petras I., A note on the fractional-order cellular neural networks. In: Proceedings of the IEEE world congress on computational intelligence, international joint conference on neural networks, Vancouver, Canada; pp.16-21, 2006. http://dx.doi.org/10.1109/ijcnn.2006.246798

S. H. Hosseninnia, R. Ghaderi, A. Ranjbar N., M. Mahmoudian, S. Momani, Sliding mode synchronization of an uncertain fractional order chaotic system, Journal Computers & Mathematics with Applications, Vol. 59, pp. 1637-1643, 2010. http://dx.doi.org/10.1016/j.camwa.2009.08.021

L.A. Zadeh, Fuzzy logic, neural networks and soft computing. Commun. ACM 37 3, pp. 77-84, 1994. http://dx.doi.org/10.1145/175247.175255

X. Z. Zhang; Y.N. Wang; X. F. Yuan, H? Robust T-S Fuzzy Design for Uncertain Nonlinear Systems with State Delays Based on Sliding Mode Control, International Journal of Computers Communications & Control, 5(4):592-602, 2010.

Balas, M.M., Balas, V.E., World Knowledge for Control Applications by Fuzzy-Interpolative Systems, International Journal of Computers Communications and Control, Vol. 3, Supplement: Suppl. S, pp. 28-32, 2008.

L. X. Wang, and J. M. Mendel, Fuzzy basis function, universal approximation, and orthogonal least square learning, IEEE Trans. Neural Networks, vol. 3, no. 5, pp. 807-814, 1992. http://dx.doi.org/10.1109/72.159070

L. X. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Englewood Cliffs, NJ: Prentice-Hall, 1994.

K. Diethelm, An algorithm for the numerical solution of differential equations of fractional order, Elec. Trans. Numer. Anal. 5, pp. 1-6, 1997.

J. L. Castro, "Fuzzy logical controllers are universal approximators," IEEE Trans. Syst., Man, Cybern., 25, pp. 629-635, 1995. http://dx.doi.org/10.1109/21.370193

S. S. Ge, T. H. Lee, C. J. Harris, Adaptive Neural Network Control of Robotic Manipulators, World Scientific Publishing Co., Singapore, 1998.

C. H. Wang, T. C. Lin, T. T. Lee and H. L. Liu, "Adaptive Hybrid Intelligent Control for Unknown Nonlinear Dynamical Systems", IEEE Transaction on Systems, Man, and Cybernetics Part B, 32 (5), October, pp. 583-597, 2002. http://dx.doi.org/10.1109/TSMCB.2002.1033178

C. H. Wang, H. L. Liu and T. C. Lin, "Direct Adaptive Fuzzy-Neural Control with Observer and Supervisory Control for Unknown Nonlinear Systems", IEEE Transaction on Fuzzy Systems, 10(1), pp. 39-49, 2002. http://dx.doi.org/10.1109/91.983277

T. C. Lin, C. H. Wang and H. L. Liu, "Observer-based Indirect Adaptive Fuzzy-Neural Tracking Control for Nonlinear SISO Systems Using VSS and ", Fuzzy Sets and Systems, 143, pp.211-232, 2004. http://dx.doi.org/10.1016/S0165-0114(03)00167-2

L. A. Zadeh, Knowledge Representation in Fuzzy Logic, IEEE Trans. Knowledge and Data Engineering, 1 (1), pp. 89-100, 1989. http://dx.doi.org/10.1109/69.43406

Published

2011-09-10

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.