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1494 JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 Figure 4. Norm of the weight vector θ Figure 4 shows the weights is always bounded in whole control process though the structure and parameters of neural network is adjusted on line. It can be seen that the actual trajectories converge rapidly to the desired ones. The computer simulation results show that the adaptive neural network controller can perform successful control and achieve desired performance. VII. CONCLUSIONS A new adaptive neural network tracking control algorithm is presented for a class of SISO nonaffine nonlinear systems with zero dynamics in this paper. The method does not assume boundedness on the time derivative of a control effectiveness term, and only need sign known and boundedness of the control effectiveness term. The update law of neural network adjustable parameters is obtained by the gradient descent algorithm. The overall adaptive scheme guarantees that all signals involved are uniformly ultimately bounded and the output of the closed-loop system tracks the desired output trajectory. Simulation results demonstrate the feasibility of the proposed control scheme. ACKNOWLEDGMENT It is a project supported by Provincial Natural Science Foundation of Hunan, China (Grant No.09JJ3094), the Research Foundation of Education Bureau of Hunan Province, China (Grant No.09B022), the Great Item of United Provinces Natural Science Foundation of Hunan, China (Grant No.09JJ8006), the Planned Science and Technology Project of Hunan Province, China (Grant No.2011FJ3126). Supported by the Construct Program of the Key Discipline in Hunan Province: Control Science and Engineering Science and Technology Innovation Team of Hunan Province: Complex Network Control. REFERENCES [1] Zheng C, Jagannathan S, “Geneeralized Hamilton-jacobibellman formulation based neural network control of affine nonlinear discrete-time systems”, IEEE Transactions on Neural Networks, vol. 19, pp. 90-106, 2008. [2] Pang H P, Chen X, “Global robust optimal sliding mode control for uncertain affine nonlinear systems”, Journal of Systems Engineering and Electronics, vol. 20, 838-843, 2009. [3] Hu H, Liu G R, Tang H Z, Guo Peng, “Robust output tracking control for mismatched uncertainties nonlinear systems”, Natural Science Journal of Xiangtan University, vol. 32, pp. 108-111, 2010. [4] Tie-Shan Li, D. Wang, G. Feng, S. C. Tong, “A DSC approach to robust adaptive NN tracking control for strictfeedback nonlinear systems”, IEEE Transactions on systems, man, and cybernetics-part B: cybernetics, vol. 40, pp. 915-927, 2010. [5] Salim L, Thierry M G, “Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems”, Fuzzy Sets and Systems, vol. 158, pp. 1126-1137, 2007. [6] Huang H X, Nuan T, “Application of adaptive fuzzy sliding mode control based on GA in positioning servo system of the permanent magnetic linear motors”, Natural Science Journal of Xiangtan University, vol. 32, pp. 94-98, 2010. [7] H. Du, S. S. Ge, J. K. Liu, “Adaptive neural network output feedback control for a class of non-affine nonlinear systems with unmodelled dynamic”, IET Control Theory & Application, vol. 5, pp. 465-477, 2010. [8] Bong-Jun Yang, Anthony J. Calise. Adaptive control of a class of nonaffine systems using neural networks. IEEE Transactions on Neural Network, vol. 18, pp. 1149-1159, 2007 [9] Hu H, Liu G R, Liu D B, Guo P, “Output feedback tracking control for a class of uncertain nonlinear MIMO systems using neural network”, Control Theory & Applications, vol. 27, pp. 382-386, 2010 [10] Ge S S, Zhang J, “Neural-network control of nonaffine nonlinear system with zero dynamics by state and output feedback”, IEEE Transaction on neural networks, vol. 14, pp. 900-918, 2003 [11] Qiu J B, Feng G, Gao H J, “Asynchronous Outputfeedback control of network nonlinear systems with multiple packet dropouts: T-S fuzzy affine model-based approach”, IEEE Transaction on Fuzzy Systems, vol. 19, pp. 1014-1030, 2011 [12] Lin C.M, Chen T.Y, “Self-organizing CMAC control for a class of MIMO uncertain nonlinear systems”,.IEEE Transaction on neural networks, vol. 20, pp. 1377-1384, 2009. [13] S. Blazic, I. Skrjanc, D. Matko, “Globally stable direct fuzzy model reference adaptive control”, Fuzzy Sets and Systems, vol. 139, pp. 3-33, 2003. [14] Wang W Y, Chien Y S, Lee T T., “Observer-based T-S fuzzy control for a class of general nonaffine nonlinear systems using generalized projecton-update laws”, IEEE Transactions on fuzzy systems, vol. 19, pp. 493-503, 2011. [15] Jianming Lian, Yonggon Lee, Stanislaw H. Zak, “Variable neural direct adaptive robust control of uncertain systems”, IEEE Transactions on Automatic Control, vol. 53, 11, pp. 2658-2664, 2008. [16] S.Labiod, M.S.Boucherit, “Direct stable fuzzy adaptive control of a class of MIMO nonlinear systems”, Fuzzy sets and systems, vol. 151, pp. 59-77, 2005. [17] N. Golea, A. Golea, K. benmahammed, “Stable indirect fuzzy adaptive control”, Fuzzy sets and systems, vol1.137, pp. 353-366, 2003. [18] Jinpeng Yu, Bing Chen, Haisheng Yu, “Position tracking control of induction motors via adaptive fuzzy backstepping” Energy Conversion and Management, vol51, pp. 2345-2352, 2010. [19] Karimi B. Menhaj M B, Karimi G M, Saboori I, “Decentralized adaptive control of large-scale affine and nonaffine nonlinear systems”, IEEE Transactions on © 2013 ACADEMY PUBLISHER
JOURNAL OF COMPUTERS, VOL. 8, NO. 6, JUNE 2013 1495 Instrumentation and Measurement, vol. 8, pp. 2459-2467, 2009. [20] Huang G B, Saratchandran, Sundararajan N, “A Generalized Growing and Pruning RBF (GGP-RBF) Neural Network for Function Approximation”, IEEE Transactions on Neural Networks, vol. 16, pp. 57-67, 2005 [21] J.-H. Park, G.-T. Park, S.-H. Kim, C.-J. Moon, “Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear systems”, Fuzzy sets and systems, vol. 153, pp. 429-445, 2005. [22] J.-H. Park, S.-H. Kim, C.-J. Moon, “Adaptive neural control for strict-feedback nonlinear systems without backstepping”, IEEE Transactions on Neural Networks, vol. 20, 7, pp. 1204-1209, 2009. [23] H.-X, Li, S.C. Tong, “A hybrid adaptive fuzzy control for a class of nonlinear MIMO systems”, IEEE Transactions on Fuzzy Systems, vol. 11, pp. 24-34, 2003. [24] G.Nurnberger, “Approximation by spline functions, “New York: Springer-Verlag, 1999, [25] Pepe P, “Input-to-state stabilization of stabilizable, timedelay, control-affine, nonlinear systems”, IEEE Transactions on automatic control, vol. 54, pp. 1688-1693, 2009. Hui HU is a lecturer of Department of electrical and information engineering, Hunan institute of engineering. Dr.Hu received the B.S. degree in electronics and information engineering from Hunan University of Science and Technology in 2001.And received the M.S. degree in power electronics and drives from Xiangtan University in 2004.And received the Ph. D degree in control theory and control engineering from Hunan University in 2010. Her research interests include nonlinear systems tracking control, MIMO systems control, uncertain nonlinear system control and intelligent control. Peng GUO is a lecturer of Department of Computer and Science, Hunan institute of engineering. He received the B.S. degree in electronics and information engineering from Hunan University of Science and Technology in 2000, and received the M.S. degree in computer science from Hunan University in 2006. His research interests include intelligent control and computing theory, multimedia computing and networking and agent technology. © 2013 ACADEMY PUBLISHER
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Journal of Computers ISSN 1796-203X
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Corn Moisture Measurement using a C
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Call for Papers and Special Issues
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(Contents Continued from Back Cover
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