This paper aims to design an algorithm to identify Transfer Function of First Order Plus Dead Time (FOPDT) process by using Process Reaction Curve(PRC) identification method, auto selection of tuning methods using Ziegler- Nichols(Z-N), Astrom-Hagglund (A-H), Tsang-Rad(T-R), Fruehauf(FF) and Internal Model Controller (IMC), ITAE-1 tuning rules ITAE -1 for set point tracking(ITAE-1 SP) and disturbance rejection (ITAE-1 DR) and updating PI Controller parameters calculated for selected tuning method to meet optimized controller parameters. The performance of different tuning methods for various case studies of FOPDT processes were simulated and analyzed first and then real time plant- pH neutralization was controlled with proposed algorithm implemented in LabVIEW software. © 2017 The Authors. Published by Elsevier Ltd.

Subbulekshmi D

Electrical

School of Electrical Engineering

Chennai Campus

Akshay N

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Energy Procedia

Objective: To obtain mathematical model and parameters of poorly understood and imprecisely known plant/process. Methods: One solution to this problem is to obtain these using identification techniques. Process identification is a technique where a mathematical model of the process under study is build from process input-output data. Several autoregressive models are used to estimate the present value of the model using its past values of the process. Initially, the data set is generated for the given system and the auto regressive model is fitted to it, for the estimation of the model parameters. Residual error for a system is calculated using auto regression model parameters. Findings: A mathematical model for plant under study can be formulated with different system identification using Linear Regression methods like Auto Regressive eXogenous variable (ARX), Auto Regressive Moving Average with an eXogenous variable (ARMAX), Output Error (OE) and Box-Jenkins (BJ). For high model order ARX model is preferred and takes low computations but only suitable for white noise. The ARMAX model considers disturbance affecting process and provides higher performance index i.e. fitness which reveals percentage variation in output estimated by respective model. The Output-Error (OE) model estimates process model but cannot model disturbance features. The residual analysis of Box- Jenkins model shows that the prediction error is not auto-correlated, correlated and is uncorrelated with the input applied to process, thus showing Box- Jenkins model ability to capture noise dynamics of process. Applications/Improvements: The process model can be identified for the unknown, poorly known or partially known system and formulated model can be used in model Predictive controller design and Adaptive control techniques.

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Online Auto Selection of Tuning Methods and Auto Tuning PI Controller in FOPDT Real Time Process-pH Neutralization

Journal	Data powered by TypesetEnergy Procedia
Publisher	Data powered by TypesetElsevier BV
ISSN	1876-6102
Open Access	Yes