dc.contributor.author	Ng, Boon Chiang.
dc.date.accessioned	2024-12-18T01:39:02Z
dc.date.available	2024-12-18T01:39:02Z
dc.date.issued	2015
dc.description	Thesis (PhD. (Mechanical Engineering))
dc.description.abstract	The inefficient On/Off control for the compressor operation has long been regarded as the major factor contributing to energy loss and poor cabin temperature control of an automotive air conditioning (AAC) system. In this study, two soft computing based controllers, namely the proportional-integral-derivative (PID) based controllers tuned using differential evolution (DE) algorithm and an adaptive neural network based model predictive controller (A-NNMPC), are proposed to be used in the regulation of cabin temperature through proper compressor speed modulation. The implementation of the control schemes in conjunction with DE and neural network aims to improve the AAC performance in terms of reference tracking and power efficiency in comparison to the conventional On/Off operation. An AAC experimental rig equipped with variable speed compressor has been developed for the implementation of the proposed controllers. The dynamics of the AAC system is modelled using a nonlinear autoregressive with exogenous inputs (NARX) neural network. Based on the plant model, the PID gains are offline optimized using the DE algorithm. Experimental results show that the DE tuned PID based controller gives better tracking performance than the Ziegler-Nichols tuning method. For A-NNMPC, the identified NARX model is incorporated as a predictive model in the control system. It is trained in real time throughout the control process and therefore able to adaptively capture the time varying dynamics of the AAC system. Consequently, optimal performance can be achieved even when the operating point is drifted away from the nominal condition. Finally, the comparative assessment indicates clearly that A-NNMPC outperforms its counterparts, followed by DE tuned PID based controller and the On/Off controller. Both proposed control schemes achieve up to 47% power saving over the On/Off operation, indicating that the proposed control schemes can be potential alternatives to replace the On/Off operation in an AAC system.
dc.description.sponsorship	Faculty of Mechanical Engineering.
dc.identifier.uri	https://openscience.utm.my/handle/123456789/1518
dc.language.iso	en
dc.publisher	Universiti Teknologi Malaysia
dc.subject	Thermodynamics and heat engines
dc.subject	Soft computing
dc.subject	Motor vehicles, accessories, and supplies
dc.title	Soft computing based controllers for automotive air conditioning system with variable speed compressor.
dc.type	Thesis
dc.type	Dataset

Soft computing based controllers for automotive air conditioning system with variable speed compressor.

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