Title page for etd-0711112-150002

URN	etd-0711112-150002	Statistics	This thesis had been viewed 644 times. Download 14 times.
Author	Tien-Huai Ma
Author's Email Address	timddc@yahoo.com.tw
Department	Institute Of Mechanical Engineering
Year	2011	Semester	2
Degree	Master	Type of Document	Master's Thesis
Language	zh-TW.Big5 Chinese	Page Count	95
Title	The Acrtifucial Neural Networks Applied to Temperature Control for A Variable Refrigerant Flow Air-conditioning System
Keyword	Artificial neural networks Compressor speed control Variable refrigerant flow (VRF) Energy-saving air conditioning systems Energy-saving air conditioning systems Variable refrigerant flow (VRF) Compressor speed control Artificial neural networks
Abstract	The variable refrigerant flow (VRF) air-conditioning systems are developed for energy saving applications. A variable speed compressor droved by an electrical inverter has been used to VRF air-conditioning systems for energy saving, but the AC driver will generate high heat dissipation and induce high operating temperature especially compressor in low speed operation conditions.This will decrease the energy saving efficiency. Recent years, the air conditioner compressors droved by a Brushless DC motor (BLDC motor) will improve the high heat generation problem in wide operation speed. The DC drivers have better energy saving efficiency than the conventional AC drivers. This study proposes the direct neural control with specified reference model applied to control the room temperature. The conventional PID control is applied to control the speed of compressor, and the back propagation error (BPE) is approximated by a linear combination of error and error’s differential, so that it is not necessary to build the neural emulator to estimate the Jacobin of plant, and the convergent speed can be increased by proposed methods.The dynamic simulations will analyze the time response of room temperature and energy saving efficiency.The simulation resuits show the dynamic response of room temperatnre will follow the response of the reference model with the energy-saving performance.
Advisor Committee	Ming-Huei Chu - advisor Chih-Sheng Yang - co-chair Jeng-Kuang Hwang - co-chair
Files	etd-0711112-150002.pdf indicate accessible at a year
Date of Defense	2012-06-30	Date of Submission	2012-07-11

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