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IEEE Trans. Automat. Contr. 01/2011; 56:625-629.
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IEEE Trans. Contr. Sys. Techn. 01/2011; 19:256-268.
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IJISTA. 01/2010; 8:114-129.
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Advances in Neural Networks - ISNN 2007, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part I; 01/2007
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Advances in Neural Networks - ISNN 2007, 4th International Symposium on Neural Networks, ISNN 2007, Nanjing, China, June 3-7, 2007, Proceedings, Part II; 01/2007
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Int. J. Systems Science. 01/2007; 38:709-724.
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Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part III; 01/2006
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Signal and Image Processing (SIP 2005), Proceedings of the IASTED International Conference, August 15-17, 2005, Honolulu, HI, USA; 01/2005
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Signal and Image Processing (SIP 2005), Proceedings of the IASTED International Conference, August 15-17, 2005, Honolulu, HI, USA; 01/2005
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ABSTRACT: This paper considers the position tracking problem of a popular magnetic levitation system in the presence of modeling errors due to uncertainties of physical parameters. The recently developed dynamic surface control (DSC) technique is modified and applied to the system under study, to overcome the problem of “explosion of terms” associated with the backstepping design procedure. The input-to-state stability (ISS) property is ensured by the robust nonlinear damping terms, and the ultimate control error bounds are made sufficiently small by the adaptive laws. Experimental results are included to show the excellent position tracking performance of the designed control system. © 2004 Wiley Periodicals, Inc. Electr Eng Jpn, 149(4): 42–51, 2004; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20092
Electrical Engineering in Japan 08/2004; 149(4):42 - 51. · 0.09 Impact Factor
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Advances in Neural Networks - ISNN 2004, International Symposium on Neural Networks, Dalian, China, August 19-21, 2004, Proceedings, Part II; 01/2004
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ABSTRACT: It is well known that least-squares (LS) method gives biased parameter estimates when the input and output measurements are corrupted by noise. One possible approach for solving this bias problem is the bias-compensation based method such as the bias-compensated least-squares (BCLS) method. In this paper, a new bias-compnesation based method is proposed for identification of noisy input-output system. The proposed method is based on compensation of asymptotic bias on the instrumental variables type (IV-type) estimates by making use of noise covariances estimates. In order to obtain the noise covariances estimates, an overdetermined system of equations is introduced, and the noise covariances estimation algorithm is derived by solving this overdetermined system of equations. From the combination of the parameter estimation algorithm and the noise covariances estimation algorithm, the proposed bias-compensated instrumental variables type (BCIV-type) method can be established. The results of a simulated example indicate that the proposed algorithm provides good estimates.
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ABSTRACT: In this paper, we consider the identification problem for a dual-rate system in which the input sampling period may differ from that of the output. Based on the lifting oper-ators, a lifted system which is equivalent to the original dual-rate system can be derived so that a lifted state-space model can be obtained which maps the relations between the dual-rate input-output data. Then the numerical sub-space state-space identification (N4SID) algorithm is modi-fied and used to identify the lifted state-space model, taking the causality constraints of the lifted system into account. Finally, numerical studies are included to show the excel-lent numerical performance of the proposed algorithm.
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ABSTRACT: This paper considers the identification problem of multiple input single output (MISO) continuous-time systems with unknown time delays of the inputs, from sampled input–output data. An iterative global separable nonlinear least-squares (GSEPNLS) method which estimates the time delays and transfer function parameters separably is derived to significantly reduce the possibility of convergence to a local minimum, by using the stochastic global-optimization techniques. Furthermore, the GSEPNLS method is modified to a novel global separable nonlinear instrumental variable (GSEPNIV) method to remove the biases of the estimates in the presence of high measurement noise. Simulation results show that the proposed algorithms work quite well.
Automatica.
