Abstract
An adaptive controller is proposed for a class of nonlinear systems with unknown time-varying delays and a dead-zone input. Taking the dead-zone as a part of the system dynamics, the construction of the dead-zone inverse model is not needed and thus the characteristic parameters of the dead-zone are not necessarily known. Unknown time delays are handled by introducing improved Lyapunov-Krasovskii functions, where the requirements on the delayed functions/control coefficients are further relaxed without the singularity problem. A novel high-order neural network with only a scalar weight parameter is developed to approximate unknown nonlinearities. The closed-loop system is proved to be semi-globally uniformly ultimately bounded (SGUUB). Experiments on a robotic servo system are provided to verify the reliability of the presented method.
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References
Tao, G., Kokotovic, P.V.: Adaptive Control of Systems with Actuator and Sensor Nonlinearities. Wiley, New York (1996)
Tao, G., Lewis, F.L.: Adaptive Control of Nonsmooth Dynamic Systems. Springer, London (2003)
Ibrir, S., Xie, W.F., Su, C.Y.: Adaptive tracking of nonlinear systems with non-symmetric dead-zone input. Automatica 43, 522–530 (2007)
Selmic, R.R., Lewis, F.L.: Deadzone compensation in motion control systems using neural networks. IEEE. Trans. Automat. Contr. 45, 602–613 (2000)
Zhang, T.P., Ge, S.S.: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44, 1895–1903 (2008)
Zhou, J.: Decentralized adaptive control for large-scale time-delay systems with dead-zone input. Automatica 44, 1790–1799 (2008)
Huang, J.Q., Lewis, F.L.: Neural-network predictive control for nonlinear dynamic systems with time delay. IEEE Trans. Neural Netw. 14, 377–389 (2003)
Na, J., Ren, X.M., Huang, H.: Time-delay positive feedback control for nonlinear time-delay systems with neural network compensation. ACTA Automat. Sin. 34, 1196–1203 (2008)
Ge, S.S., Hong, F., Lee, T.H.: Adaptive neural network control of nonlinear systems with unknown time delays. IEEE Trans. Automat. Contr. 48, 2004–2010 (2003)
Hong, F., Ge, S.S., Lee, T.H.: Practical adaptive neural control of nonlinear systems with unknown time delays. IEEE Trans. Syst., Man, Cybern. B, Cybern. 35, 849–854 (2005)
Ge, S.S., Hong, F., Lee, T.H.: Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. IEEE Trans. Syst., Man, Cybern. B, Cybern. 34, 499–516 (2004)
Wang, M., Chen, B., Shi, P.: Adaptive neural control for a class of perturbed strict-feedback nonlinear time-delay systems. IEEE Trans. Syst., Man, Cybern. B, Cybern. 38, 721–730 (2008)
Zhang, T.P., Ge, S.S.: Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs. Automatica 43, 1021–1033 (2007)
Kosmatopoulos, E.B., Polycarpou, M.M., Christodoulou, M.A., Ioannou, P.A.: High-order neural network structures for identification of dynamical systems. IEEE Trans. Neural Netw. 6, 422–431 (1995)
Ge, S.S., Tee, K.P.: Approximation-based control of nonlinear MIMO time-delay systems. Automatica 43, 31–43 (2007)
The World’s Largest Antenna Measuring Range: Astrium and EurasSpace Deliver Compact Range to China, http://classic.eads.net/1024/en/pressdb/archiv/2003/2003/en_20030321_euras_e.html
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Na, J., Herrmann, G., Ren, X. (2010). Neural Network Control of Nonlinear Time-Delay System with Unknown Dead-Zone and Its Application to a Robotic Servo System. In: Vadakkepat, P., et al. Trends in Intelligent Robotics. FIRA 2010. Communications in Computer and Information Science, vol 103. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15810-0_43
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DOI: https://doi.org/10.1007/978-3-642-15810-0_43
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