Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

From ancient bonfires and oil lamps to candles and incandescent lamps, to the fluorescent lamps of thousands of households today, human beings have basically adapted to the indoor lighting environment of artificial light sources. However, due to thousands of years of environmental impact, natural light is still the most comfortable and comfortable light source for human beings, and natural lighting has always been highly valued by architects and lighting designers. The energy-saving and environmental protection requirements of today’s social buildings put forward further requirements for natural lighting and lighting.

1 Introduction

From ancient bonfires and oil lamps to candles and incandescent lamps, to the fluorescent lamps of thousands of households today, human beings have basically adapted to the indoor lighting environment of artificial light sources. However, due to thousands of years of environmental impact, natural light is still the most comfortable and comfortable light source for human beings, and natural lighting has always been highly valued by architects and lighting designers. The energy-saving and environmental protection requirements of today’s social buildings put forward further requirements for natural lighting and lighting.

The national technical and economic policy of China states: Architectural design should make full use of natural light and create a good light environment.

The research on natural lighting in indoor lighting is of great significance:

(1) The data shows that lighting electricity accounts for 25-40% of the energy consumption of the entire commercial building, while natural lighting can save 52% of lighting electricity under certain circumstances, which greatly saves energy.

(2) Related research shows that people working under natural light conditions can increase satisfaction and improve work efficiency. Ensure normal circadian rhythm and prevent symptoms such as seasonal affective disorder.

(3) The working illuminance level and uniformity of natural lighting have an important influence on visual fatigue.

The current research on natural lighting mainly focuses on the CIE standard sky model, building and lighting automatic control. Due to the randomness and variability of natural light, and the diversity of the patterns and orientations of application places, this paper uses fuzzy control methods, combined with ergonomics, to detect the illuminance values ​​of key points inside and outside the window sill, and perform fuzzy logic inferences on the changes of natural light. Judgment, making the adjustment of the shading device more reasonable and more humane. At the same time, it is different from ordinary curtains that only control in height dimensions. It increases the control of the blind angle, controls the intensity and angle of natural light entering the room, and rationally uses natural lighting to improve office efficiency and comfort of the office environment, and increase residential work The satisfaction of the personnel with the indoor environment.

2. Fuzzy control theory

Fuzzy control is a kind of computer digital control based on fuzzy theory, fuzzy language variables and fuzzy logic inference. It can avoid the mathematical model of the object, and convert the determined value of the fuzzy controller input into the corresponding fuzzy language variable value. The corresponding language variable value is defined by the corresponding degree of membership. It is suitable for the controlled object that is not easy to obtain an accurate mathematical model. , Its structural parameters are not very clear, or difficult to obtain, only the experience or knowledge of operators or domain experts is required. As a language variable controller, its control rules are only qualitatively expressed in the form of language variables, which constitutes a fuzzy model of the controlled object.

The basic principle of fuzzy control is shown in Figure 1. Its core part is the fuzzy controller, which is the process of fuzzification of input variables and defuzzification of output variables, as shown in the dashed box. The computer obtains the accurate value of the control quantity through sampling, and then compares this quantity with the given value to obtain the error signal E. Generally, the error signal E is selected as the input of the fuzzy controller. Fuzzy the precise amount of the error signal and express it in the corresponding fuzzy language. Obtain a subset e of the fuzzy language* of the error E. When there is e and the fuzzy rule R (fuzzy relation), the fuzzy decision is made according to the synthetic rules of the inference, and the fuzzy control quantity u is obtained as:

 Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

Figure 1 Schematic diagram of fuzzy control system

3. Fuzzy control strategy for indoor natural lighting

3. 1 Control strategy

This system adopts a side window lighting system, that is, light windows are exploited on one or both sides of the room, adjustable blinds are used, and the Delphi method (expert method) is adopted to control the natural lighting of the shading blinds to solve the randomness of natural light and interference factors. Many questions.

The influencing factors of natural light are more complicated, and many interferences are difficult to express uniformly with mathematical formulas. When the sun is too strong in summer, excessive natural light is likely to cause discomfort; clouds drifting and tree shadow floating will have a greater impact on natural illuminance. If you respond in a timely manner, the shading shutters need to be rotated frequently, and sometimes the noise is relatively large, which affects the work of indoor personnel. And rest. Therefore, this system introduces the idea of ​​ergonomics, combined with fuzzy logic reasoning, and reasonably controls the adjustment of shading louvers.

We first measure the indoor illuminance distribution of the side window daylighting system, analyze the indoor illuminance distribution and illuminance changes in a day under typical sunny, cloudy and other weather conditions, and summarize the rules, and find that it can reflect the current indoor illuminance and external weather conditions. One or several points of the window sill illuminance relationship are defined as key points, and the illuminance value of the key point and the change of the illuminance value and the speed of the change are detected, so as to adjust the louver angle βb (with 5 degrees as the minimum adjustment amount).

For direct light, we set a threshold. When the light intensity exceeds the threshold, natural lighting is rejected. When the natural light is strong (horizontal shading louvers), direct sunlight is reflected to the ceiling area to form diffuse reflection, which not only ensures uniform illumination and visual communication between personnel and the outside world, but also avoids glare. The electric louver responds if and only when the change of sunlight reaches the adjustment range we need. If the illuminance changes and the change is slow, adjust the louver. When the illuminance value increases, the louver angle becomes smaller, and when the illuminance value decreases, the louver angle becomes larger to increase lighting; if the illuminance changes quickly, it is judged as a cloud or tree Shadow interference, no curtain adjustment.

3. 2 Fuzzy controller structure

We use fuzzy statistical method and expert experience method to determine the membership function. The establishment of control rules and the assignment of parameters are completed on the basis of the indoor illuminance distribution measurement data of the standard daylighting room we selected and the lighting research experience of Fudan University, Tianjin University, Chongqing University and other architectural departments.

This controller is a two-dimensional fuzzy controller: the input variable is the error △E and the variation of the error is CE; the output variable is the control variable μ.

For the error △E, the fuzzy set of the error change amount CE and the control amount μ is defined as follows:

The fuzzy sets of error △E, error change amount CE and control amount μ are:

{Big negative, medium negative, small negative, zero, positive small, positive middle, positive big}

Use the English initials to be abbreviated as {NB, NM, NS, 0, PS, PM, PB}.

Based on this, the fuzzy rule table is established as shown in Table 1.

Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

Note: In the table, the CE from 0 to PB indicates that the illuminance increases from small to large, and the change from 0 to NB indicates that the illuminance decreases from large to small.

In the table, the row △E from 0 to PB indicates that the illuminance changes in the direction of increasing illuminance at a certain speed, and the change from 0 to NB indicates that the illuminance changes in the direction of decreasing illuminance at a certain speed.

The control amount μ represents the specific adjustment scale (NB, NM, NS, 0, PS, PM, PB).

0 means no adjustment, and the system is in idle state, which means monitoring and sampling.

In view of the fact that the accuracy of curtain control does not need to be too high, this article does not distinguish the difference between N0 (positive zero) and P0 (negative zero) in the error change.

3. 3 Fuzzy rule determination

For the error △E, the domain of the error change CE and the control quantity μ is defined as follows:

The domains of error △E and error change CE are:

{- 5, - 4, - 3, - 2, - 1, 0, 1, 2, 3, 4, 5}

The domain of μ is:

{0, 1}

According to the research results of indoor illuminance distribution, we discretized the indoor illuminance value, using 50lx as the gradient, divided into 11 levels {-300, -200, -150, -100, -50,0,50,100,150,200, 300} and correspondingly simplified to E = {-5,-4,-3,-2,-1, 0, 1, 2, 3, 4, 5}. When the value does not belong to the *, it is rounded to the nearest integer, for example, 4.5 -> 5.0, 2.7 -> 3.0. This rough fuzzification method is in line with the human brain’s habit of processing fuzzy information.

Among them, when the illuminance changes between -50 and 50, people’s perception of indoor illuminance changes is not obvious, so the changes are negative and positive.

When the illuminance changes to about 150, due to individual differences, some people think that there is an impact, some people think that the impact is not obvious, but when the change reaches more than 200, most people think that the impact is obvious, so-200,-150,- 100 and 100, 150, and 200 are medium change categories, and then judge the membership degree of each value based on experience, that is, the specific degree of its influence on indoor illuminance, see Table 2 for details.

Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

Table 2 Assignment table of fuzzy variable E

*{ - 5, - 4, - 3, - 2, - 1, 0, 1, 2, 3, 4, 5} corresponding to CE respectively represent the changes in the illuminance unit within the sampling time. The degree of illuminance decrease from large to small corresponds to-5 to-4; the degree of illuminance increase corresponds to 1 to 5 from large to small. It is used to reflect the change trend and degree of the error, so as to judge whether the change of illuminance is interference or irreversible change. According to the change of the illuminance difference and the experience of people in the ergonomics of the illuminance perception value, the CE assignment table of the illuminance change value can be determined. The change in error CE is obtained by dE/dt, and the introduction principle is shown in Figure 2:

Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

ωmax is the upper limit angular frequency of the sampling signal. In this range, the smaller the sampling period, the closer to continuous control. However, considering the response time of the electric curtain, this article sets the sampling frequency as 5 times per minute, and T=12s.

3.4 Synthesis of fuzzy rules

According to fuzzy mathematics calculation rules and composition rules, the membership function of the control rules after composition is μ (u).

Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

Since the *U of the control quantity μ is the classical * domain {0, 1} in this design, it is synthesized by formula 4 and adjusted according to the actual situation and empirical values, as shown in Table 3.

The output defuzzification adopts the maximum degree of subordination method, combined with the specific control method fuzzy* to be precise, from which the execution result can be obtained, and the choice of whether to adjust the curtain.

3. 5 System fuzzy control process

According to the setting of the above fuzzy rules, we have designed the fuzzy control program flow chart shown in Figure 3 below.

Design of indoor natural lighting system using two-dimensional fuzzy controller and C8051 single-chip microcomputer

The system first monitors the illuminance of key points, and when the illuminance changes, it is fuzzy to judge whether the change is interference or a variable that needs to be adjusted. First, with one minute as the time node, the main control unit obtains the illuminance value of five samples through the sensor. By successively calculating the difference between two adjacent data, the magnitude of the illuminance change is judged. A positive difference indicates that the illuminance becomes larger, and a negative difference indicates that the illuminance becomes smaller. Only when the five changes are both positive or negative, that is, when the illuminance change trend within one minute is consistent, the difference between the value of the fifth sampling and the illuminance value of the first sampling is used as the value of CE. Use the average of the five differences as the ΔE in the fuzzy rule. According to the fuzzy control rules specified above, the program can complete the corresponding adjustment actions by calling the corresponding curtain adjustment subroutine.

According to the above procedure, in each fuzzy judgment subroutine, the system judges whether the current outdoor illumination change belongs to external interference by judging the illuminance error △E and the value of the error change CE, and what adjustment method should be adopted if it is not the interference . The defuzzification of the output adopts the maximum subordination method, which is embodied in that it is adjusted at the time, and combined with the specific control method, the fuzzy* is refined. The execution results are shown in Table 4.

The experimental results show that when the change in external illuminance is less than the set value, the system does not adjust. When the external illuminance changes more than the set value, but the speed of change is relatively fast, and the illuminance returns to close to the original value within the preset time, the system determines that it is external interference and does not adjust, which can avoid meaningless operations in many cases. Greatly reduce the influence of external interference on natural lighting. Only when the external illuminance changes continuously and changes unidirectionally, the system makes corresponding adjustments. This control method greatly improves the readiness of the system and reduces a lot of interference fluctuations, making users feel more comfortable and humane.

4 Conclusion

In this paper, the fuzzy control strategy of natural lighting is determined according to relevant standards. According to the change of indoor illuminance, the membership function of fuzzy variables is determined by Delphi expert method. The illuminance change of key indoor points is used as the control variable, and the fuzzy variable is carried out. Chemical processing and fuzzy control.

After principle experiments, we designed a natural lighting controller based on the C8051 single-chip microcomputer chip, and combined shutter control with intelligent lighting control, and achieved good experimental results. Fuzzy control has strong robustness, can well avoid the frequent movement of curtains when interference occurs, and the combination of fuzzy algorithm and ergonomics can better conform to the human body’s perception of natural lighting and control behavior.

Natural lighting can make full use of sunlight resources, in addition to energy saving and environmental protection, but also more in line with the comfort of the human body. In summary, the combination of natural lighting and fuzzy control is of great application value.

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