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Fig. 2. Connection diagram after OPC

Figure 2 Connection diagram after OPC

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1. INTRODUCTION 1 Department of Electrical ad Electronics Engineering, Akdeniz University, Antalya, Turkey Generally, industrial controllers that can perform simple operations in a factory environment are used. However, these controllers cannot evaluate complex processes at the desired performance. For this reason, complex processes can be calculated by using a PC and the result can be transferred to the controller. Complex processes can be calculated in Matlab through an artificial intelligence algorithm.
1. INTRODUCTION 1 Department of Electrical ad Electronics Engineering, Akdeniz University, Antalya, Turkey Generally, industrial controllers that can perform simple operations in a factory environment are used. However, these controllers cannot evaluate complex processes at the desired performance. For this reason, complex processes can be calculated by using a PC and the result can be transferred to the controller. Complex processes can be calculated in Matlab through an artificial intelligence algorithm.
Figure 1 shows the structure where the systems using different communication protocols offered product-specific driver before the OPC. Different devices included in the system with different software applications enable communication with a single standard as long as they have the OPC driver. The communication structure after OPC is shown in Figure 2 [3] [7].
Figure 1 shows the structure where the systems using different communication protocols offered product-specific driver before the OPC. Different devices included in the system with different software applications enable communication with a single standard as long as they have the OPC driver. The communication structure after OPC is shown in Figure 2 [3] [7].
n the OPC structure where the server and the client reside, the server carries out data transfer in line with the requests he client. OPC client is supposed to be used on the application side to communicate with an appropriate OPC server. Als t functions as the data receiver as clients control the communication with the servers. Figure 3 shows the structure of O} server.
n the OPC structure where the server and the client reside, the server carries out data transfer in line with the requests he client. OPC client is supposed to be used on the application side to communicate with an appropriate OPC server. Als t functions as the data receiver as clients control the communication with the servers. Figure 3 shows the structure of O} server.
from Matlab environment and to read/write data through the server. The connection between Matlab/Simulink, PLC and OPC is shown in Figure 4.
from Matlab environment and to read/write data through the server. The connection between Matlab/Simulink, PLC and OPC is shown in Figure 4.
According to the classical logic, the weather is either hot or cold. In Fuzzy Logic, it is possible to have classifications such the weather is "slightly warm", "warm", "very hot", "somewhat cold", and this gives flexibility to the fuzzy logic. The fu: logic stages are shown in Figure 5. The first and second phases refer to cluster formation and identification processes. Fuzzification is the generalization of fuzzy clusters, and at this stage, we get the correct degree of the line in question. The membership functions stored at this stage are used to obtain fuzzy input values. The information is converted into fuzzy sets and defuzzification refers to a process that transforms a fuzzy number into an integer [2].
According to the classical logic, the weather is either hot or cold. In Fuzzy Logic, it is possible to have classifications such the weather is "slightly warm", "warm", "very hot", "somewhat cold", and this gives flexibility to the fuzzy logic. The fu: logic stages are shown in Figure 5. The first and second phases refer to cluster formation and identification processes. Fuzzification is the generalization of fuzzy clusters, and at this stage, we get the correct degree of the line in question. The membership functions stored at this stage are used to obtain fuzzy input values. The information is converted into fuzzy sets and defuzzification refers to a process that transforms a fuzzy number into an integer [2].
It is ensured that the server is ready with the channel setting, system settings, PLC model, Ethernet address and tag entri: to be used for OPC server configuration. The addresses and data types of the data to be transferred from Matlab to PL and from PLC to Matlab are shown in Figure-6. S7-1200 was programmed via Tia Portal editor. Analog temperature information was scaled between 0-100 (Figure-7) The scaled 'temperature' data was transferred to Matlab via OPC.
It is ensured that the server is ready with the channel setting, system settings, PLC model, Ethernet address and tag entri: to be used for OPC server configuration. The addresses and data types of the data to be transferred from Matlab to PL and from PLC to Matlab are shown in Figure-6. S7-1200 was programmed via Tia Portal editor. Analog temperature information was scaled between 0-100 (Figure-7) The scaled 'temperature' data was transferred to Matlab via OPC.
The temperature and relative humidity information obtained from DHT22 connected to PLC were inserted into the fuzzy logic block in Matlab / Simulink through PLC, and the resulting fan speed analog value were transferred from Matlab to the PLC as a percentage. For the physical representation of the results, it was ensured that the physical outputs were activated/deactivated by using the speed information comparison commands from Matlab in TiaPortal as shown Figure 8.
The temperature and relative humidity information obtained from DHT22 connected to PLC were inserted into the fuzzy logic block in Matlab / Simulink through PLC, and the resulting fan speed analog value were transferred from Matlab to the PLC as a percentage. For the physical representation of the results, it was ensured that the physical outputs were activated/deactivated by using the speed information comparison commands from Matlab in TiaPortal as shown Figure 8.
Fuzzy logic clusters were created by considering the apparent temperature table depending on the temperature and relative humidity as shown Figure 9 [4].
Fuzzy logic clusters were created by considering the apparent temperature table depending on the temperature and relative humidity as shown Figure 9 [4].
Figure 10 shows the parameters of the fuzzy model consisting of two input and one output parameters, and the system’s modeling in MATLAB program.
Figure 10 shows the parameters of the fuzzy model consisting of two input and one output parameters, and the system’s modeling in MATLAB program.
Fig. 10. (input1=temperature , input2=humidity, output=speed)
Fig. 10. (input1=temperature , input2=humidity, output=speed)
rhe graphical representation of the Input 2 “humidity” parameter in Matlab environment is given in Figure 12.
rhe graphical representation of the Input 2 “humidity” parameter in Matlab environment is given in Figure 12.
The graphical representation of fan speed variable in Matlab environment is given in Figure 13.
The graphical representation of fan speed variable in Matlab environment is given in Figure 13.
Rules were set to understand the relation between input and output parameters. The resulting database contains all the possibilities and some rules of the fuzzy model are shown in Figure 14.
Rules were set to understand the relation between input and output parameters. The resulting database contains all the possibilities and some rules of the fuzzy model are shown in Figure 14.
The impact of input variables on the output variables are given in Figure 15 based on the fuzzy rule base.
The impact of input variables on the output variables are given in Figure 15 based on the fuzzy rule base.
Fig. 15. The impact of input variable on output variable
Fig. 15. The impact of input variable on output variable
Fig. 20. System input MATLAB view Fig. 21. MATLAB output view System input is given in Figure 20 and system output is given in Figure 21.
Fig. 20. System input MATLAB view Fig. 21. MATLAB output view System input is given in Figure 20 and system output is given in Figure 21.
The system gives the fan speed as percentage after the Fuzzy process on analog temperature value from PLC and relative humidity ratio entered through Matlab, and sends it to PLC. The real-time communication and control of the system are shown below. TIA PORTAL part of the system input is shown in Figure 18. OPC part of the system input is given in Figure 19.
The system gives the fan speed as percentage after the Fuzzy process on analog temperature value from PLC and relative humidity ratio entered through Matlab, and sends it to PLC. The real-time communication and control of the system are shown below. TIA PORTAL part of the system input is shown in Figure 18. OPC part of the system input is given in Figure 19.
Fig. 17. Matlab/Simulink OPC Configuration
Fig. 17. Matlab/Simulink OPC Configuration
Fig. 24. Snapshot of the inputs/outputs in Fuzzy controller 4. CONCLUSIONS Real-time data exchange between S7-1200 and Matlab / Simulink was ensured through OPC Server. System inputs were processed in in Fuzzy controller in Matlab / Simulink and then system output is obtained. The resulting system output wa: transferred to S7-1200 and the desired physical output is set. This study shows that a complex system is resolved in an equally strong platform in real time. OPC part of the system output is given in Figure 22. TIA PORTAL portion of the system output is given in Figure 23. Snapshot of the system’s Fuzzy input/output is given in Figure 24.
Fig. 24. Snapshot of the inputs/outputs in Fuzzy controller 4. CONCLUSIONS Real-time data exchange between S7-1200 and Matlab / Simulink was ensured through OPC Server. System inputs were processed in in Fuzzy controller in Matlab / Simulink and then system output is obtained. The resulting system output wa: transferred to S7-1200 and the desired physical output is set. This study shows that a complex system is resolved in an equally strong platform in real time. OPC part of the system output is given in Figure 22. TIA PORTAL portion of the system output is given in Figure 23. Snapshot of the system’s Fuzzy input/output is given in Figure 24.
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Computer ScienceProgrammable Logic Controller
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