Open AccessDevelopment of FOPDT and SOPDT model from arbitrary process identification data using the properties of orthonormal basis function
Author(s) -
Lalu Seban,
Namita Boruah,
Binoy Krishna Roy
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.21.11840
Subject(s) - control theory (sociology) , mathematics , orthonormal basis , transfer function , basis function , basis (linear algebra) , constant (computer programming) , algorithm , computer science , mathematical analysis , engineering , physics , geometry , quantum mechanics , control (management) , artificial intelligence , electrical engineering , programming language
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.