作者：
Prof. Jian-Xin XuProf. Leonid FridmanDepartment of Electrical and Computer Eng. National University of Singapore 4 Engineering Drive 3 Singapore 117576 Tel +65 6874-2566
Fax +65 6779-1103 Dr Jian-Xin Xu received his Bachelor degree from Zhejiang University
China in 1982. He attended the University of Tokyo Japan where he received his Master's and Ph.D. degrees in 1986 and 1989 respectively. All his degrees are in Electrical Engineering. He worked for one year in the Hitachi research Laboratory Japan and for more than one year in Ohio State University U.S.A. as a Visiting Scholar. In 1991 he joined the National University of Singapore and is currently an associate professor in the Department of Electrical Engineering. His research interests lie in the fields of learning control variable structure control fuzzy logic control discontinuous signal processing and applications to motion control and process control problems. He is the associate editor of Asian Journal of Control member of TC on variable structure systems and sliding mode control of IEEE Control Systems Society and a senior member of IEEE. He has produced more than 90 peer-refereed journal papers near 160 technical papers in conference proceedings and authored/edited 4 books. Division de Estudios de Posgrado Facultad de Ingenieria National Autonomous University of Mexico DEP-FI
UNAM Edificio “A” Circuito Exterior Ciudad Universitaria A. P. 70–256 C.P.04510 Mexico D.F. Mexico Tel +52 55 56223014 Fax +52 55 56161719 Dr. Leonid M. Fridman received his M.S in mathematics from Kuibyshev (Samara) State University
Russia Ph.D. in Applied Mathematics from Institute of Control Science (Moscow) and Dr. of Science degrees in Control Science from Moscow State University of Mathematics and Electronics in 1976 1988 and 1998 respectively. In 1976–1999 Dr. Fridman was with the Department of Mathematics at the Samara State Architecture and Civil Engineering Academy Samara Russia. In 2000–2002 he was with the Department of Postgraduate Study and Investigations at the Chihuahu
A human listener has the ability to follow a speaker's voice while others are speaking simultaneously;in particular, the listener can organize the time-frequency energy of the same speaker across time into a singl...
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The nonlinearity and high dimension of computational fluid dynamics (CFD) models (O(10 4 ) at the low end) reflect fluid dynamics' intrinsic complexity. It is a formidable challenge, setting fluid flow control apa...
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The nonlinearity and high dimension of computational fluid dynamics (CFD) models (O(10 4 ) at the low end) reflect fluid dynamics' intrinsic complexity. It is a formidable challenge, setting fluid flow control apart from conventional applications. Its implications include restrictions on model based control design, reliable state estimation, and thus, on feedback implementation. Seeking low order, design accessible models, the issue of an ample dynamic envelope, covering targeted free and actuated transients, is in the essence. We review some enablers for very low order, Galerkin models (GMs). Those include the combination of empirical proper orthogonal decomposition (POD) and physics based modes, estimation of turbulence and pressure effects, actuation models, interpolated models that cover an enhanced dynamic range, and auxiliary, phasor models, focused on sensor readings. The dynamic manifold of model validity must be respected for a meaningful use of the model, but can also be exploited, such as by a restriction to slow drift in the system's periodic behavior, enabling the use of simplifying dynamic phasor models. Finally, we shall highlight some intrinsic performance limitations in GM based feedback flow control
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