Abstract
This study presents a control system design based on cerebellar-modelarticulation- controller (CMAC) for a class of multiple-input multiple-output (MIMO) uncertain nonlinear systems. The proposed control system merges a CMAC and sliding mode control, so the input space dimension of CMAC can be simplified. The control system consists of a CMAC-based principal controller (CMPC) and a robust controller. CMPC containing a CMAC uncertainty observer is used as the principal controller and the robust controller is designed to dispel the effect of approximation error. The gradient descent method is used to on-line tune the parameters of CMAC and the Lyapunov function is applied to guarantee the stability of the system. An experimental result of linear ultrasonic motor motion control and a simulation study of biped robot fault tolerance control show that favorable control performance can be achieved by using the proposed control system.
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References
Slotine, J.J.E., Li, W.P.: Applied Nonlinear Control. Prentice-Hall, NJ (1991)
Hung, J.Y., Gao, W., Hsu, J.C.: Variable Structure Control: A Survey. IEEE Trans. Ind. Electron. 40(1), 2–22 (1993)
Albus, J.S.: A New Approach to Manipulator Control: the Cerebellar Model Articulation Controller (CMAC). J. Dyn. Syst., Meas., Contr. 97(3), 220–227 (1975)
Lane, S.H., Handelman, D.A., Gelfand, J.J.: Theory and Development of Higher-Order CMAC Neural Network. IEEE Control Syst. Mag. 12(2), 23–30 (1992)
Shiraishi, H., Ipri, S.L., Cho, D.D.: CMAC Neural Network Controller for Fuel-Injection Systems. IEEE Trans. Contr. Syst. Technol. 3(1), 32–38 (1995)
Jagannathan, S., Commuri, S., Lewis, F.L.: Feedback Linearization Using CMAC Neural Networks. Automatica 34(3), 547–557 (1998)
Chiang, C.T., Lin, C.S.: CMAC with General Basis Functions. J. Neural Netw. 9(7), 1199–1211 (1996)
Kim, Y.H., Lewis, F.L.: Optimal Design of CMAC Neural-Network Controller for Robot Manipulators. IEEE Trans. Syst., Man, Cybern. C 30(1), 22–31 (2000)
Su, S.F., Tao, T., Hung, T.H.: Credit-assigned CMAC and its Application to Online Learning Robust Controllers. IEEE Trans. Syst., Man and Cybern. B 33(2), 202–213 (2003)
Lin, C.M., Peng, Y.F.: Adaptive CMAC-based Supervisory Control for Uncertain Nonlinear Systems. IEEE Trans. Syst., Man, and Cybern. B 34(2), 1248–1260 (2004)
Commuri, S., Jagannathan, S., Lewis, F.L.: CMAC Neural Network Control of Robot Manipulators. Journal of Robotics Syst. 14(6), 465–482 (1997)
Wu, T.F., Tsai, P.S., Chang, F.R., Wang, L.S.: Adaptive Fuzzy CMAC Control for a Class of Nonlinear System with Smooth Compensation. IEE Proc. Cont. Theory Appl. 153(6), 647–657 (2006)
Lin, C.M., Peng, Y.F.: Missile Guidance Law Design Using Adaptive Cerebellar Model Articulation Controller. IEEE Trans. Neural Netw. 16(3), 636–644 (2005)
Wang, L.X.: Adaptive Fuzzy Systems and Control: Design and Stability Analysis. Prentice-Hall, Englewood Cliffs (1994)
Hagood, N.W., Mcfarland, A.J.: Modeling of a Piezoelectric Rotary Ultrasonic Motor. IEEE Trans. Ultrason., Ferroelect., Freq. Contr. 42, 210–224 (1995)
Vemuri, A.T., Polycarpou, M.M.: Neural-Network-based Robust Fault Diagnosis in Robotic Systems. IEEE Trans. Neural Netw. 8(6), 1410–1420 (1997)
Liu, Z., Li, C.: Fuzzy Neural Networks Quadratic Stabilization Output Feedback Control for Biped Robots via H∞ Approach. IEEE Trans. Syst., Man, Cybern. B 33(1), 67–84 (2003)
Lin, C.M., Chen, L.Y., Chen, C.H.: RCMAC Hybrid Control for MIMO Uncertain Nonlinear Systems Using Sliding-Mode Technology. IEEE Trans. Neural Netw. 18(3), 708–720 (2007)
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Lin, CM. (2009). Design and Applications of Cerebellar Model Articulation Controller. In: Rudas, I.J., Fodor, J., Kacprzyk, J. (eds) Towards Intelligent Engineering and Information Technology. Studies in Computational Intelligence, vol 243. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03737-5_9
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DOI: https://doi.org/10.1007/978-3-642-03737-5_9
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