An Intelligent Aircraft Window System for Visual Comfort Control

Abstract

The purpose of this thesis is to develop an intelligent aircraft window system for visual comfort control based on electrochromic windows that can change the transparency depending on passenger’s needs and visual comfort. In the current window system, window transparency is controlled manually. The system developed in this thesis is an automatic system. Under this development, a mock-up is set up to mimic a section of cabin with an electrochromic window along with a simulated sunlight source and several light sensors. To measure visual comfort, the daylight glare index (DGI) is adopted. Based on DGI, a fuzzy logic classifier is developed to evaluate visual comfort/discomfort. This is followed by developing a fuzzy logic controller that can automatically adjust the window transparency based on the measured DGI level and the rate of change of DGI. DGI set point for different individuals, the effect of illuminance and color temperature are also discussed

Reinforcement Learning Fuzzy Algorithm for Adaptive Cabin Management System With Application for Adaptive Interior Lighting

“…The fuzzy logic controller fails to perfectly differentiate between inputs the exact way a human brain could. This was also demonstrated in Lius work [16]. To overcome this drawback, supervised learning, specifically reinforcement learning can be utilized.…”

“…For more accurate experiments and applications documented, Liu published work on creating an intelligent electrochromic window system for visual comfort control inside an aircraft cabin [16]. This smart window essentially adjusted its transparency depending on the passengers needs along with the current glare inside the cabin for visual comfort.…”

“…Figure 2.5: Lius experiment setup in our lab [16] To measure visual comfort, the daylight glare index was adopted as its proven to be the most accurate and most consistent measurement method. Furthermore, A fuzzy logic controller was utilized to automatically adjust the electrochromic window transparency based off the DGI level and the rate of change of DGI.…”

“…Although not pursued in this research, DGI N can be utilized as a feedback in order to ensure consistently maintaining a DGI value of 22 which is considered the comfortable input. This was performed by Liu [16] who utilized a smart window to adjust the DGI to a setpoint of 22. This fuzzy inference system however, will utilize these inputs mentioned and a passenger override as the feedback which will be covered in the next chapter.…”

“…Table 3.2 displays the activities being incorporated and tested within this model, along with their respective rankings. values for various activities [16]. We will utilize this ranking along with their corresponding setting values in the testing and implementation testing in later chapters.…”