Universiti Teknologi Malaysia Institutional Repository

Parametric and non-parametric identification for an automotive air conditioning system

Al-Awad, M. A. and Ghani, Z. A. and Mat Darus, I. Z. (2019) Parametric and non-parametric identification for an automotive air conditioning system. In: 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019, 17-19 Oct 2019, Dublin, Ireland.


Official URL: http://www.dx.doi.org/10.1145/3358331.3358384


This research aims to develop the dynamic model of an Automotive Air Conditioning system using conventional and intelligent techniques. The research focused to achieve the optimal model that can effectively capture the behavior of the system. Linear and Non-Linear Autoregressive with Exogenous input (ARX and NARX) and Linear Autoregressive Moving Average with Exogenous inputs (ARMAX) models were used to capture the dynamics behavior of the system using system identification technique utilizing experimentally acquired input-output data. The system identifications were conducted using parametric and conventional method namely Recursive Least Squares (RLS) and Recursive Extended Least Squares (RELS), and nonparametric method using Intelligent algorithm of Multilayer Perceptron Neural Network. The comparative investigations have proven the superiority of the ARMAX model over the ARX and NARX model in term of prediction performance, whiting the disturbance as well as computational load for training. The mean square error are 2.7341×10-4, 1.9017×10-5 and 5.0257×10-6, for ARX, NARX, and ARMAX model respectively.

Item Type:Conference or Workshop Item (Paper)
Uncontrolled Keywords:automotive air conditioning system, recursice least square, system identifications
Subjects:T Technology > TJ Mechanical engineering and machinery
Divisions:Mechanical Engineering
ID Code:91169
Deposited By: Narimah Nawil
Deposited On:21 Jun 2021 16:40
Last Modified:21 Jun 2021 16:40

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