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Chapter 1:Introduction
Ying Qu, Yu-Feng Li, Ru Bai, Chunying Chen and Zhifang Chai
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03 Jun 2010
Copyright Year:2010
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The terms “-ome” and “-omics” have been widely adopted by scientists. The “-omics” informally refers to the studies in biology, while the related “-omes” addresses the objects of study in such fields. The suffix “-ome” is thought to derive from the Latin prefix “omni-”, meaning total or complete. Th...
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Adaptive neural control for MIMO nonlinear systems with state time-varying delayRuliang WangKunbo MeiChaoyang ChenYanbo LiHebo MeiZhifang YuRegular PapersDOI:
10.-012-0281-xCite this article as: Wang, R., Mei, K., Chen, C. et al. J. Control Theory Appl. (9. doi:10.-012-0281-xIn this paper, adaptive neural control is proposed for a class of multi-input multi-output (MIMO) nonlinear unknown state time-varying delay systems in block-triangular control structure. Radial basis function (RBF) neural networks (NNs) are utilized to estimate the unknown continuous functions. The unknown time-varying delays are compensated for using integral-type Lyapunov-Krasovskii functionals in the design. The main advantage of our result not only efficiently avoids the controller singularity, but also relaxes the restriction on unknown virtual control coefficients. Boundedness of all the signals in the closed-loop of MIMO nonlinear systems is achieved, while The outputs of the systems are proven to converge to a small neighborhood of the desired trajectories. The feasibility is investigated by two simulation examples.Adaptive controlBackstepping techniqueTime-varying delayMIMONeural network[1]P. Liu, T. Su. Robust stability of interval time-delay systems with delay-dependence. Systems & Control Letters, ): 231–239.[2]V. B. Kolmanovskii, J. Richard. Stability of some linear systems with delays. IEEE Transactions on Automatic Control, ): 984–989.[3]S. K. Nguang. Robust stabilization of a class of time-delay nonlinear systems. IEEE Transactions on Automatic Control, ): 756–762.[4]S. S. Ge, F. Hong, T. H. Lee. Adaptive neural control of nonlinear time-delay systems with unknown virtual control coefficients. IEEE Transactions on Systems, Man, and Cybernetics, ): 499–516.[5]T. Zhang, S. S. Ge. Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs. Automatica, ): .[6]Z. Lin, H. Fang. On asymptotic stabilizability of linear systems with delayed input. IEEE Transactions on Automatic Control, ): 998–1013.[7]Y. Roh, J. Oh. Robust stabilization of uncertain input-delay systems by sliding mode control with delay compensation. Automatica, ): .[8]F. Mazenc, S. Mondie, S. Niculescu. Global asymptotic stabilization for chains of integrators with a delay in the input. IEEE Transactions on Automatic Control, ): 57–63.[9]F. Hong, S. S. Ge, T. H. Lee. Delay-independent sliding mode control of nonlinear time-delay systems. Proceedings of the American Control Conference. New York: IEEE, –4073.[10]S. S. Ge, G. Li, T. H. Lee. Adaptive NN control for a class of strictfeedback discrete time nonlinear systems. Automatica, ): 807–819.[11]J. Stoev, J. Y. Choi, J. Farrell. Adaptive control for output feedback nonlinear systems in the presence of modeling errors. Automatica, ): .[12]Y. Fu, Z. Tian. Output feedback stabilization for time-delay nonlinear systems. Acta Automatica Sinica, ): 802–805.[13]P. Pepe. Some results on adaptive tracking for a class of nonlinear time delay systems. Proceedings of the 40th IEEE Conference on Decision and Control. New York: IEEE, –1002.[14]D. W. C. Ho, J. Li, Y. Niu. Adaptive neural control for a class of nonlinearly parametric time-delay systems. IEEE Transactions on Neural Networks, ): 625–635.[15]Y. Niu, J. Lam, X. Wang, et al. Sliding mode control for nonlinear state-delayed systems using neural network approximation. IEE Proceedings — Control Theory and Applications, ): 233–239.[16]Y. Niu, J. Lam, X. Wang, et al. Adaptive H∞ control using backstepping design and neural networks. Transactions of the ASME. Journal of Dynamic Systems Measurement and Control, ): 478–485.[17]W. Chen, J. Li. Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems. Journal of Systems Engineering and Electronics, ): 611–618.[18]M. M. Polycarpus. Stable adaptive neural control scheme for nonlinear systems. IEEE Transactions on Automatic Control, ): 447–451.[19]M. M. Polycarpus, M. J. Mears. Stable adaptive tracking of uncertain systems using nonlinearly parametrized on-line approximators. International Journal of Control, ): 363–384.[20]C. M. Kwan, F. L. Lewis. Robust backstepping control of induction motors using neural networks. IEEE Transactions on Neural Networks, ): .[21]R. J. Wai, F. J. Lin, S. P. Hsu. Intelligent backstepping control for linear induction motor drive. IEE Proceedings — Control Theory and Applications, ): 193–202.[22]Q. Zhu, S. Fei, T. Zhang, et al. Adaptive RBF neural-networks control for a class of time-delay nonlinear systems. Neurocomputing, /18): .[23]H. Wu. Adaptive stabilizing state feedback controllers of uncertain dynamical systems with multiple time delays. IEEE Transactions on Automatic Control, ): .[24]S. Zhou, G. Feng, S. K. Nguang. Comments on robust stabilization of a class of time-delay nonlinear systems. IEEE Transactions on Automatic Control, ): .[25]S. S. Ge, F. Hong, T. H. Lee. Adaptive neural network control of nonlinear systems with unknown time delays. IEEE Transactions on Automatic Control, ): .[26]D. W. C. Ho, J. Li, Y. Niu. Adaptive neural control for a class of nonlinearly parametric time-delay systems. IEEE Transactions on Neural Networks, ): 625–635.[27]S. S. Ge, C. Wang. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, ): 674–692.[28]C. Chou, C. Cheng. A decentralized model reference adaptive variable structure controller for large-scale time-varying delay systems. IEEE Transactions on Automatic Control, ): .[29]Z. J. Palmor, Y. Halevi. On the design and properties of multivariable dead time compensators. Automatica, ): 255–264.[30]W. Wang, L. Mau. Stabilization and estimation for perturbed discrete time-delay large-scale systems. IEEE Transactions on Automatic Control, ): .[31]H. Wu. Decentralized adaptive robust control for a class of largescale systems including delayed state perturbations in the interconnections. IEEE Transactions on Automatic Control, ): .[32]S. S. Ge, K. P. Tee. Approximation-based control of nonlinear MIMO time-delay systems. Automatica, ): 31–43.[33]R. M. Sanner, J. E. Slotine. Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, ): 837–863.[34]Y. Yang, G. Feng, J. Ren. A combined backstepping and smallgain approach to robust adaptive fuzzy control for strict-feedback nonlinear systems. IEEE Transactions on Systems, Man and Cybernetics, ): 406–420.[35]S. Zhou, J. Lam, G. Feng, et al. Exponential ?-regulation for multiinput nonlinear systems using neural networks. IEEE Transactions on Neural Networks, ): .Ruliang Wang1Kunbo Mei2Chaoyang Chen3Yanbo Li2Hebo Mei4Zhifang Yu51.Computer and Information Engineering CollegeGuangxi Teachers Education UniversityNanning GuangxiChina2.School of Mathematical SciencesGuangxi Teachers Education UniversityNanning GuangxiChina3.Department of Control Science and EngineeringHuazhong University of Science and TechnologyWuhan HubeiChina4.Information Engineering CollegeCapital Normal UniversityBeijingChina5.School of Education SciencesGuangxi Teachers Education UniversityNanning GuangxiChina
We use cookies to improve your experience with our site.Discussion of the Motive and Equilibrium Mechanism of the Social Sports Instructors Development in China--《Journal of Tianjin University of Sport》2008年06期
Discussion of the Motive and Equilibrium Mechanism of the Social Sports Instructors Development in China
LI Xiangru1, LI Zhifang2, ZHANG Jianye3(1.Dept.of Leisure and Social Sport, Capital Institute of Physical Education, Beijing, 10088, C2.Institute of Disaster Prevention Science and Technology, Hebei 101601, C3.Hebei Institute of Physical Education, Shijiazhuang 050041, China)
To build an appropriate motive and equilibrium mechanism of the social sports instructors and to deal with their relation in an effective way are the crucial future development factors.This paper discussed the motive mechanism and concluded that the economy is the core motive, the politics regulates the basic development direction, and the culture gives the development sustaining power and discussed the equilibrium mechanism also.
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(C)2006 Tsinghua Tongfang Knowledge Network Technology Co., Ltd.(Beijing)(TTKN) All rights reservedProgress in Low Elastic Modulus Titanium Alloy--《Materials Review》2011年S1期
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Titanium alloys have been practically used as biomedical metallic materials because of good biocompatibility, corrosion resistance and mechanical properties.Developing new Ti alloy with low elastic modulus is one of the research hotspots.The recent research status of Ti alloy with low elastic modulus from both theoretical and experimental aspects is reviewed.The d-electron alloy design and the first principle calculation have been the effective methods to design Ti alloy with low elastic modulus.The multi-alloying-element design and the optimized thermomechanical processing play important roles in the development of new biomedical Ti alloys with multifunction and high properties.
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