# A Distributed Intelligent Lighting Control System Based on Deep Reinforcement Learning
## 2 Proposed Methodologies
### Fuzzy Control for Shading Subsystem  
Fuzzy control is a method of intelligent rule-based control based on fuzzy mathematical theory. It does not require exact values but, like humans, is able to discriminate and make decisions based on its own experience with the imprecise quantities represented by natural language variables. Fuzzy control has several substantial advantages over other intelligent control methods [30; 36; 37]:  
* Its variables are in natural language rather than precise values; the control logic is simple to understand and simple to construct;
* The control chain of the fuzzy controller is based on rules derived from experience so that the system can maintain excellent control effects despite large external disturbances;
* Because its controller model is not a mathematical model and can be described with very little data, it does not require a large amount of training data. As the first dimming control in this system, it is not necessary for the blinds to control the room's illuminance to an exact value. Rather, they must control the room's temperature and illuminance within a range adequate for human perception. This paper  
Figure 1: Topology and network connections for a single-story building. adopts a fuzzy control approach to the control of blinds on a developed Raspberry-Pi-based distributed system. The fuzzy controller comprises four primary modules, as depicted in Figure 2: Fuzzification, which converts numerical representations of input quantities into fuzzy quantities that the fuzzy controller can recognize. Base Rule, which consists of a database and a rule base. The rule base establishes a collection of control rules described in language from the control purpose and control strategy, reflecting the experience of control experts. The fuzzy inference engine, which is the essence of fuzzy control, and finally, Defuzzification, also known as exactification, which is the use of databases and rule bases to simulate the process of human reasoning decisions and to derive the output quantities of fuzzy inference. It functions to convert the output quantity of a fuzzy logic conclusion into a precise value. This paper selects a triangular membership function with high sensitivity to transform numerical data into natural language variables. In the whole fuzzy control system, the output quantity is the angle of the blind, which ranges from 30\({}^{\circ}\) to 150\({}^{\circ}\), as shown in Figure 3a. The inputs are room temperature and work surface illumination, respectively. A number of studies have been conducted in relation to the issue of office temperature and work efficiency [38; 39; 40]. Their results show that the temperature range for optimal productivity is from 20 \({}^{\circ}\)C to 26 \({}^{\circ}\)C, especially from 22 \({}^{\circ}\)C to 24 \({}^{\circ}\)C, which almost matches optimal thermal satisfaction. But when the temperature starts to increase from the range of 22 \({}^{\circ}\)C to 23 \({}^{\circ}\)C, it leads to a decrease in cognitive tasks [41]. Therefore, this paper takes 23 \({}^{\circ}\)C as the optimal indoor temperature and sets the range of temperature membership function as 17 \({}^{\circ}\)C to 29 \({}^{\circ}\)C, as shown in Figure 3b. One study [42] showed that when office light levels were increased from 500 lx to 1500 lx, it could increase the productivity of office workers by 9%; so, keeping a room within that range of natural light is very helpful in improving productivity in the office. In this paper, the range of illuminance membership function is set to 0 lx 1500 lx, as shown in Figure 3c. In particular, a dedicated membership function is created for each input/output value, i.e., indoor illuminance, indoor temperature, and the blinds' angle, for a total of three membership functions. Table 1 shows the membership function for indoor temperature, divided into seven categories, and Table 2 shows the membership function for indoor illuminance, divided into seven categories.