Robust Adaptive Self-Organizing Wavelet Fuzzy CMAC Tracking Control for De-icing Robot Manipulator

  • ThanhQuyen Ngo
  • TaVan Phuong Faculty of Electrical and Electronics Engineering, HCMC University of Technology And Education, Vietnam


In this paper, a robust adaptive self-organizing control system based on a novel wavelet fuzzy cerebellar model articulation controller (WFCMAC) is developed for an n-link robot manipulator to achieve the high-precision position tracking. This proposed controller consists of two parts: one is the WFCMAC approach which is implemented to cope with nonlinearities, due to the novel WFCMAC not only incorporates the wavelet decomposition property with fuzzy CMAC fast learning ability but also it will be self-organized; that is, the layers of WFCMAC will grow or prune systematically. Therefore, dimension of WFCMAC can be simplified. The second is the order which is the adaptive robust controller which is designed to achieve robust tracking performance of the system. The adaptive tuning laws of WFCMAC parameters and error estimation of adaptive robust controller are derived through the Lyapunov function so that the stability of the system can be guaranteed. Finally, the simulation and experimental results of novel three-link deicing robot manipulator are applied to verify the effectiveness of the proposed control methodology.


[1] M. Baban, C.F. Baban, C. Bungau, G. Dragomir, R.M. Pancu (2014); Estimation of the Technical State of Automotive Disc Brakes Using Fuzzy Logic, International Journal of Computers Communications & Control, 9(5): 531-538.

[2] C.R. Costea, H.M. Silaghi, D. Zmaranda, M.A. Silaghi (2015); Control System Architecture for a Cement Mill Based on Fuzzy Logic, International Journal of Computers Communications & Control, 10(2): 165-173.

[3] Y. Zou, Y. N. Wang, X. Z. Liu (2010); Neural network robust H∞ tracking control strategy for robot manipulators. Applied Mathematical Modelling, 34(7): 1823-1838.

[4] Y. Feng, W. Yao-nan, Y. Yi-min (2012); Inverse Kinematics Solution for Robot Manipulator based on Neural Network under Joint Subspace, International Journal of Computers Communications & Control, 7(3): 459-472.

[5] C. Zhu, Y. F. Fang (2007); Adaptive control of parallel manipulators via fuzzy-neural network algorithm. Journal Control Theory & Application, 5(3): 295-300.

[6] T. Ngo, Y. Wang, T.L. Mai, M.H. Nguyen, J. Chen (2012); Robust Adaptive Neural-Fuzzy Network Tracking Control for Robot Manipulator. International Journal of Computers Communications& Control, 7(2): 341-352.

[7] C. F. Hsu, C. M. Lin, T. T. Lee (2006); Wavelet adaptive backstepping control for a class of nonlinear Systems, IEEE Transcation Neural Network, 17(5): 1175-1183.

[8] C. H. Lu (2009); Design and application of stable predictive controller using recurrent wavelet neural networks, IEEE Transactions On Industrial Electronics, 56(9): 733 – 3742.

[9] J. S. Albus (1975); A new approach to manipulator control: The cerebellar model articulation controller (CMAC), J. Dyn. Syst. Meas. Control, 97(3): 220 – 227.

[10] S. Jagannathan, S. Commuri, F. L. Lewis (1998); Feedback linearization using CMAC neural networks, Automatica, 34(3): 547 – 557.

[11] Y. H. Kim, F. L. Lewis (2000); Optimal design of CMAC neural-network controller for robot manipulators, IEEE Transcation System Man Cybernation C, Application Revision, 30(1): 22– 31.

[12] C. T. Chiang, C. S. Lin (1996); CMAC with general basis functions, Journal of Neural Network, 9(7): 1199 – 1211.

[13] H. C. Lu, C. Y. Chuang, M. F. Yeh (2009); Design of hybrid adaptive CMAC with supervisory controller for a class of nonlinear system. Neurocomputing, 72(7-9): 1920 – 1933.

[14] Y. F. Peng, C. M. Lin (2004); Intelligent hybrid control for uncertain nonlinear systems using a recurrent cerebellar model articulation controller. IEEE Proceedings Control Theory Application, 151(5): 589 – 600.

[15] Y.C. Hsu, G. Chen, H.X. Li (2001); A Fuzzy adaptive Variable Structure Controller with Application to Robot Manipulators, IEEE Transcation System Man Cybernation, 31(3): 331-340.

[16] H. K. Khalil (1996); Nonlinear systems, Englewood Cliffs, NJ: Prentice-Hall, 1996.

[17] C. M. Lin, T. Y. Chen (2009); Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems, IEEE Neural Nets, 20(9): 1377 – 1384.

[18] C.T. Lin, C. S. George Lee (1996); Neural fuzzy systems, Englewood Cliffs, NJ: Prentice- Hall.
How to Cite
NGO, ThanhQuyen; PHUONG, TaVan. Robust Adaptive Self-Organizing Wavelet Fuzzy CMAC Tracking Control for De-icing Robot Manipulator. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 10, n. 4, p. 567-578, june 2015. ISSN 1841-9844. Available at: <>. Date accessed: 13 july 2020. doi:


Wavelet, CMAC, Deicing robot manipulator