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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI)
Noor Asyikin Sulaiman
Universiti Teknikal Malaysia Melaka
Computer Science & IT Electrical & Electronics Engineering Engineering

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Pneumatic positioning control system using constrained model predictive controller: Experimental repeatability test Siti Fatimah Sulaiman; M. F. Rahmat; Ahmad Athif Faudzi; Khairuddin Osman; S. I. Samsudin; A. F. Z. Abidin; Noor Asyikin Sulaiman
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i5.pp3913-3923

Abstract

Most of the controllers that were proposed to control the pneumatic positioning system did not consider the limitations or constraints of the system in their algorithms. Non-compliance with the prescribed system constraints may damage the pneumatic components and adversely affect its positioning accuracy, especially when the system is controlled in real-time environment. Model predictive controller (MPC) is one of the predictive controllers that is able to consider the constraint of the system in its algorithm. Therefore, constrained MPC (CMPC) was proposed in this study to improve the accuracy of pneumatic positioning system while considering the constraints of the system. The mathematical model of pneumatic system was determined by system identification technique and the control signal to the valves were considered as the constraints of the pneumatic system when developing the controller. In order to verify the accuracy and reliability of CMPC, repetitive experiments on the CMPC strategy was implemented. The existing predictive controller, that was used to control the pneumatic system such as predictive functional control (PFC), was also compared. The experimental results revealed that CMPC effectively improved the position accuracy of the pneumatic system compared to PFC strategy. However, CMPC not capable to provide a fast response as PFC.
Fault detection for air conditioning system using machine learning Noor Asyikin Sulaiman; Md Pauzi Abdullah; Hayati Abdullah; Muhammad Noorazlan Shah Zainudin; Azdiana Md Yusop
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 9, No 1: March 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (568.712 KB) | DOI: 10.11591/ijai.v9.i1.pp109-116

Abstract

Air conditioning system is a complex system and consumes the most energy in a building. Any fault in the system operation such as cooling tower fan faulty, compressor failure, damper stuck, etc. could lead to energy wastage and reduction in the system’s coefficient of performance (COP). Due to the complexity of the air conditioning system, detecting those faults is hard as it requires exhaustive inspections. This paper consists of two parts; i) to investigate the impact of different faults related to the air conditioning system on COP and ii) to analyse the performances of machine learning algorithms to classify those faults. Three supervised learning classifier models were developed, which were deep learning, support vector machine (SVM) and multi-layer perceptron (MLP). The performances of each classifier were investigated in terms of six different classes of faults. Results showed that different faults give different negative impacts on the COP. Also, the three supervised learning classifier models able to classify all faults for more than 94%, and MLP produced the highest accuracy and precision among all.
Co-Authors A. F. Z. Abidin Ahmad Athif Faudzi Azdiana Md Yusop Hayati Abdullah Khairuddin Osman M. F. Rahmat Md Pauzi Abdullah Muhammad Noorazlan Shah Zainudin S. I. Samsudin Siti Fatimah Sulaiman