Minimal partial realization from orthonormal basis function expansions

2003 ◽  
Author(s):  
P.S.C. Heuberger ◽  
T.J. de Hoog ◽  
P.M.J. Van den Hof ◽  
Z. Szabo ◽  
J. Bokor
Keyword(s):  
Basis Function ◽  
Orthonormal Basis ◽  
Minimal Partial Realization

Minimal partial realization from generalized orthonormal basis function expansions

Automatica ◽  
2002 ◽  
Vol 38 (4) ◽  
pp. 655-669 ◽  
Author(s):  
Thomas J. de Hoog ◽  
Zoltán Szabó ◽  
Peter S.C. Heuberger ◽  
Paul M.J. Van den Hof ◽  
József Bokor
Keyword(s):  
Basis Function ◽  
Orthonormal Basis ◽  
Minimal Partial Realization

Investigation of the supercell based orthonormal basis function method for different kinds of fibers

2004 ◽  
Vol 10 (4) ◽  
pp. 296-311 ◽  
Author(s):  
Zhi Wang ◽  
Guobin Ren ◽  
Shuqin Lou ◽  
Weijun Liang
Keyword(s):  
Basis Function ◽  
Function Method ◽  
Orthonormal Basis

scholarly journals Development of FOPDT and SOPDT model from arbitrary process identification data using the properties of orthonormal basis function

2018 ◽  
Vol 7 (2.21) ◽  
pp. 77 ◽  
Author(s):  
Lalu Seban ◽  
Namita Boruah ◽  
Binoy K. Roy
Keyword(s):  
Basis Function ◽  
Delay Time ◽  
Orthonormal Basis ◽  
Control Point ◽  
Second Order ◽  
Step Test ◽  
Step Response ◽  
Order System ◽  
Model Parameters ◽  
First Order

Most of industrial process can be approximately represented as first-order plus delay time (FOPDT) model or second-order plus delay time (FOPDT) model. From a control point of view, it is important to estimate the FOPDT or SOPDT model parameters from arbitrary process input as groomed test like step test is not always feasible. Orthonormal basis function (OBF) are class of model structure having many advantages, and its parameters can be estimated from arbitrary input data. The OBF model filters are functions of poles and hence accuracy of the model depends on the accuracy of the poles. In this paper, a simple and standard particle swarm optimisation technique is first employed to estimate the dominant discrete poles from arbitrary input and corresponding process output. Time constant of first order system or period of oscillation and damping ratio of second order system is calculated from the dominant poles. From the step response of the developed OBF model, time delay and steady state gain are estimated. The parameter accuracy is improved by employing an iterative scheme. Numerical examples are provided to show the accuracy of the proposed method. 

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