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The tracking sensor consists of four similar CdS sensors, that are found at the east, west, south, and north to identify the source of light intensity within the four orientations. The CdS sensor forms a 45deg position using the source of light. In the CdS sensor positions, brackets isolate the sunshine using their company orientations to attain a large-position search and rapidly determine the sun’s position (see Figure 4). The 4 sensors are split into two groups, east/west and north/south. Within the east/west group, the east and west CdS sensors compare the concentration of received light within the east and west. When the source of light intensity received through the sensors differs, the machine obtains signals in the sensors’ output current within the two orientations.

The machine then determines which sensor received more intensive light in line with the sensor output current value construed by current type A/D ripper tools (ADC).

The machine drives the step motor for the orientation of the sensor. When the output values of these two sensors are equal, the output difference is zero and also the motor’s drive current is zero, meaning the machine has tracked the present position from the sun. Its northern borderOrsouthern sensors track the positioning of the sun similarly.

IV. DESIGN ARCHITECTURE OF AVR MIRCROCONTROLLER

Fig 5.System Architecture

As proven in Figure 5 processor may be the control center and integrates our two-axis control nick. The machine determines which information is given to the utilizing a photography sensor. It conducts the tracking control rule operation to calculate the position needed through the motor and adjusts motor’s current position. Additionally, it moves the solar power to attain optimal power.

We developed a tracking sensor to look for the orientation from the solar source of light. The signals given back through the sensor make up the foundation of the controller input. The control design outputs the signals to manage the 2 axis step motor and also the solar tracking control system.

V. FUZZY CONTROLLER DESIGN

The fuzzy sets concept was suggested by Zadeh in 1965. The fuzzy formula could make human understanding in to the rule base to manage a plant with linguistic descriptions.

It depends on expert experience rather of mathematical models. The benefits of fuzzy control include good popularization, high problems tolerance, and appropriate for nonlinear control systems.

A fuzzy controller design has four parts, fuzzification, control rule base, fuzzy inference, and defuzzification. The block diagram from the fuzzy control product is proven in Fig. 7.

Initially, the sun’s rays light illuminates on the light sensitive resistor from the solar tracking device. A feedback analog signal is going to be created and changed into an electronic signal with an analog/digital ripper tools. Once the current around the eastward-westward direction or even the southward-northward direction differs, the variations is going to be delivered in to the fuzzy controller. Then, the fuzzy controller produces pulses to motor motorists and also the motor motorists produce PWM signals to manage step motors for tuning preferred angles. Observe that when the variations of sensors are zero, i.e. the sun’s rays is vertical towards the solar power, therefore the fuzzy controller doesn’t work. Because the sun moves very slow, the short rotating speed from the solar tacking system is rich in speed rotation not essential. By fuzzy control, some advantages for example necessary. By fuzzy control, some advantages for example reducing consumption power step motors and fast and reducing consumption power step motors and fast and smooth fixed position is possible. Therefore, the fuzzy control formula has enough capability to complete this goal.

Because the corresponding light sensitive resistors can operate individually, it may be viewed as independent control. For just one motor control, the mistake of output voltages of corresponding sensors could be set as input variables. The rotation duration of the walking motors for clockwise and counterclockwise are output variables. The membership functions are proven in Fig. 7 and eight. Five fuzzy control rules are utilized, as proven within the following.

Within this paper, product inference is used for fuzzy inference. The middle of gravity technique is adopted for

defuzzification to attain an operating operation value. The defuzzification is proven in (3).

This defuzzification technique is implemented by digital circuits.

Mire. CONCLUSION