Open AccessARX, ARMAX, Box-Jenkins, Output-Error, and Hammerstein Models for Modeling Intelligent Pneumatic Actuator (IPA) System
Author(s) -
Mohamed Naji Muftah,
Wong Liang Xuan,
Ahmad Athif Mohd Faudzi
Publication year - 2021
Publication title -
journal of integrated and advanced engineering
Language(s) - English
Resource type - Journals
eISSN - 2774-6038
pISSN - 2774-602X
DOI - 10.51662/jiae.v1i2.18
Subject(s) - control theory (sociology) , system identification , actuator , parametric statistics , nonlinear system , black box , box–jenkins , position (finance) , parametric model , engineering , control system , control engineering , systems modeling , computer science , control (management) , artificial intelligence , data modeling , mathematics , time series , statistics , physics , software engineering , finance , electrical engineering , quantum mechanics , machine learning , economics , autoregressive integrated moving average
A pneumatic actuator is highly nonlinear, which makes the precise position control of this actuator difficult to achieve. In order to achieve precise control, selecting a suitable model structure is a prerequisite before control estimation. This selection of the model structure is based upon an understanding of the physical systems. In this paper, the black-box model is chosen as a system identification model for modeling position control of an Intelligent Pneumatic Actuator (IPA) system and a variety of parametric model structures. The parametric model structure, such as ARX, ARMAX, Box-Jenkins, output-error structures, and Hammerstein available in the black-box model, is used to assist in modeling the IPA system. The results indicate that Hammerstein had the best performance for modeling position control of the IPA system with the best fit 94.95. Also, the results show that ARX, ARMAX, Box-Jenkins, and output-error structures had best fit more than 90%.