Introduction

The smart window blind system is used for controlling the level of the sunlight which is required for lighting up the room. There are two ways of operating this system. They are : operating it manually or operating it automatically. If it is operated manually, then push buttons can be used for controlling the movement. If it is controlled automatically, the openings and closing of the blinds is done by an automated system which works on the basis of the level of light outside the room. The sensors are used for determining the levels of the light. A threshold level can be set for the sensor below which it has to perform certain action and above which it has to perform another action. The actuator used for controlling the movement of the blinds is a servo motor.

The system can be used in the homes as well as offices. The amount of daylight which enters a room can be controlled by partial or total filtering of the daylight by the blind system. This helps to avoid any type of glare inside the room which causes an obstacle in the normal working. The two factors which need to be controlled in the blind is the upward and downward movement and the angle ( AsadUllah, 2018 ). The blind system helps to ensure a healthy and safe living for the users. It also makes sure that the privacy of the user is maintained. A good smart blind system must have a long life and require very less repair and maintaining costs. If a repair is required then the sub parts of the system must be easily available in the market.

Revolutionization of Industry

There are several companies in the market which design the smart blind systems. SOMA smart is a company which designs the smart blind systems which can be controlled by the user using an application in the smart phone. But the system does not possess a controlling button. Ikea Fyrtur and Kadrilj is a company which designs the smart blind systems which consists of a battery to provide power to the blinds. There are a few companies which are trying to interface the smart blind system to a voice assistant such as Google assistant , Alexa etc. When a company designs a smart blind system, then it has to offer the system with a variety of colours, sizes, features etc ( Mehta, 2017 ). The major parts required by the system consists of the sensors , controller and motors.

Designing for smart blind system 

Te angle by which the blind is rotated is determined by the rotation of the servo motor. The rotation of the motor can be done in the clockwise direction and the anti clockwise direction. Another feature which can be added to the system is the LCD display. It can display the value of any parameters, on off status etc.

The design which is developed can be tested by creating the various scenarios which are practically possible for the room. In a home or office, the daylight needs to be used optimally to obtain proper lighting level in the room and to prevent any type of glares. The smart blind system helps to maintain the light levels as well as the thermal comfort of the room. It helps to protect the user and the furniture from direct sunlight ( Wei, 2013 ). A mobile application can be used for controlling the opening or closing of the blinds.    

Figure 1

Figure 2

The Figure 1 shows the controller design without use of the fuzzy logic. The output waveform can be seen in Figure 2. 

A timer can also be used in the smart blind systems. This timer must open the blinds in the early morning and late afternoon hours. It must close the blinds to allow a little light to enter the room when mid day occurs. The blinds must be totally closed at night. An efficient way for designing the smart blind system is the fuzzy neural network controller. Another method for controlling the movements of the blinds is by using a microcontroller. This can reduce the cost to a great extent as the microcontroller such as Arduino and raspberry Pi are available at very low prices in the market ( Martirano, 2014 ). Also, the software is open source and very easy to learn. The type of communication used in a smart blind system is the wireless communication. It allows the design of the system without using wires. In case of the automatically operated system the sensor measures the light intensity and moves the blinds to an appropriate position on the basis of the value of the light intensity. In the manually operated system a remote control can be used by the user.

Fuzzy logic design for smart blind system 

In the smart blind system the major requirement is an automatic decision making. Hence, a fuzzy based controller has been used here. The design is made using the software Matlab Simulink. This system is easy to design and show a good performance. The fuzzy PID controller is used here. This makes the overall system linearity as well as increases its speed. The amount of energy consumed by a building can be decreased by 80 percent by the use of smart blind systems. A fuzzy system offers a good flexibility and helps to generate the required outputs. A fuzzy logic controller consists of some input parameters and some output parameters. The fuzzy logic decides how the output values will vary as per the input values based on a decision making process ( Wei, 2014 ). Here the inputs can be considered the level of light, temperature position of blinds etc. The output decides how the blinds will move in response to the changes in the inputs. The major aim is to maintain a comfortable level of light inside the room by making maximum use of the natural light available.

Here, 3 input parameters have been employed. They are – luminance indoor ( IL1 ), luminance outdoor ( OL1 ) and expected light ( LE1 ).

Several cases can be designed here. If IL1 < LE1 , and OL1 > IL1 , then the blind is opened to get maximum natural light. If IL1 < LE1 and OL1 <= IL1, then natural light cannot be used. If IL1 > LE1, then there are 2 conditions: OL1 < IL1, then the blind is moved to minimum level to reach the EL level. If OL1 > IL1, then the blind is opened. In the case when OL1 < IL1, then it is ideal state and no action needs to be taken. The conditions which have been mentioned here can be simulated using the Matlab Simulink software. The fuzzy logic can be employed to design the controller.

Testing the system

Figure 3

Figure 4

The Figure 3 shows the design of a controller by using the fuzzy logic. The output waveform can be observed in Figure 4. A steep step can be observed which shows how the fuzzy logic is implemented.

An example can be considered here :

IL1 = 100 lux

OL1 = 150 lux

LE1 = 250 lux

Here, IL1 < LE1 and OL1 > IL1. Hence, IL1 must be increased and the system will adjust the blind to get the maximum possible luminance from the natural light.

Fuzzy logic parts 

Here, the fuzzy logic components like inference method, membership functions etc have been described.

The inputs considered are : IL1 – LE1 and OL1. The membership function for OL1 can be defined in the range 0 to 1100 lux and ( IL1 – LE1 ) in the range – 850 to + 850 lux.  

OL1 à { SS , S , SM , M , ML , L , LL }

IL1 – LE1 à { LN , MN , SN, Z , PS , PM , PL }

LE1 à { C , SC , M , SM , SO , O }

S = Small , M = Medium, L = Large, N = Negative, Z = Zero, P = Positive, C = Close, O = Open

The fuzzy rules have been designed for the smart blind system controller.

Advantages and disadvantages of the designed blind system :

The system is capable of saving the electrical energy . Any use of batteries can also be replaced by the use of solar energy . A smart blind system moves the blinds downwards on a hot summer day. This reduces the energy consumed by the air conditioners for lowering the temperature of the room. The blinds are also moved in the downward direction on a very cold day. This helps to reduce the energy consumed by the heating devices used to raise the temperature of the room. The natural light which is provided by moving the blinds in the upward direction when it is darker inside also helps to reduce the electrical energy consumed by the lighting devices (Ghadi, 2016). Hence on a total a large amount of energy consumption is reduced. This can be done by addition of temperature sensors to the system. By employing a smart bulb the intensity of the bulb can be changed as per the requirement. The fuzzy logic controller used here makes the system compatible for any future up gradation and expansion for bigger buildings.

Latest Techniques

In the future, the smart blind systems can be made even more advanced with the addition of certain features. The technology upgrades itself everyday and this provides an opportunity to make the systems more efficient. One such technology is the internet of things ( IoT). It is a technique by the help of which a large number of devices can be connected for the transmission and reception of data by the help of internet. It provides a wireless connection between the devices which allows fast data transfer between the devices. The 5 G technique is also going to change the communication scenario in the future. This will help to make the system very fast and it will also help to support large data rates. A problem faced in the 5 G system is that the size of the cells is really small and there is a drawback called the building penetration problem. Also 5G requires a very large bandwidth.

The number of wireless devices being used is growing at an alarming rate everyday. The traffic is increasing which leads to the congestion of the radio frequency spectrum. The efficiency of the system can decrease due to this congestion. The milli meter waves can be used to solve this problem because there frequency range is around 30 GHz . A very good speed can also be obtained by using the tera hertz waves.

Another concept which is very helpful in communication when large computations need to be done is the visible light communication ( VLC ) . This type of communication uses the visible light having a frequency range of 400 to 800 THz. The massive MIMO technology can be used for focusing a beam in a region for diminishing the energy transmitted. The NOMA technology can also be used in the orthogonal beam forming for the system.

References :

AsadUllah, M., Khan, M.A., Abbas, S., Athar, A., Raza, S.S. and Ahmad, G., 2018. Blind channel and data estimation using fuzzy logic-empowered opposite learning-based mutant particle swarm optimization. Computational intelligence and neuroscience, 2018.

Mehta, U.V., Alim, M.Z. and Kumar, S., 2017. Smart path guidance mobile aid for visually disabled persons. Procedia Computer Science, 105, pp.52-56.

Wei, Y., Kou, X. and Lee, M., 2013, October. Development of a guide-dog robot system for the visually impaired by using fuzzy logic based human-robot interaction approach. In 2013 13th International Conference on Control, Automation and Systems (ICCAS 2013) (pp. 136-141). IEEE.

Martirano, L., Parise, G., Parise, L. and Manganelli, M., 2014, October. Simulation and sensitivity analysis of a fuzzy-based building automation control system. In 2014 IEEE Industry Application Society Annual Meeting (pp. 1-7). IEEE.

Martirano, L., Manganelli, M., Parise, L. and Sbordone, D.A., 2014, May. Design of a fuzzy-based control system for energy saving and users comfort. In 2014 14th International Conference on Environment and Electrical Engineering (pp. 142-147). IEEE.

Martirano, L., Parise, G., Parise, L. and Manganelli, M., 2016. A fuzzy-based building automation control system: Optimizing the level of energy performance and comfort in an office space by taking advantage of building automation systems and solar energy. IEEE Industry Applications Magazine, 22(2), pp.10-17.

Lootsma, F.A., 2013. Fuzzy logic for planning and decision making (Vol. 8). Springer Science & Business Media.

Wei, Y., Kou, X. and Lee, M.C., 2014, July. A new vision and navigation research for a guide-dog robot system in urban system. In 2014 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (pp. 1290-1295). IEEE.

Ghadi, Y.Y., Rasul, M.G. and Khan, M.M.K., 2016. Design and development of advanced fuzzy logic controllers in smart buildings for institutional buildings in subtropical Queensland. Renewable and Sustainable Energy Reviews, 54, pp.738-744.

Prasath, V., Lakshmi, N., Nathiya, M., Bharathan, N. and Neetha, P., 2013. A survey on the applications of fuzzy logic in medical diagnosis. International Journal of Scientific & Engineering Research, 4(4), pp.1199-1203.