PID (Proportional Integral Derivative) is a control algorithm that mostly used in industry. However, users have never known what the PID model that used inside the PLC. By knowing the PID model that used in PLC, users will have more choice in determining the more appropriate tuning algorithm. Also, users can use MATLAB to perform analysis and can implement it to PLC. Through OPC Server (Object Linking and Embedding for Process Control Server) as a software interface, programs on a windows operating system can communicate with industry devices universally. PID model prediction method is done by comparing the output of the plant controlled by PID model in PLC and PID model in SIMULINK MATLAB using OPC Server intermediaries. Based on comparison result in graph and analysis using integral error method, PLC M221 using Parallel PID model and PLC S7-1200 using Ideal PID model.

Graphical abstract AbstractGreen house is a building that can modify the climate in accordance with that required by the plant. The green house system works best if it is equipped with an automation system that operates without the need for human assistance. This system consists of light sensor, soil moisture sensor, air humidity sensor, and temperature sensor. Soil moisture sensors, air humidity sensors, and temperature sensors have minimum value limits and maximum limit values. In this study, a webbased SCADA application is designed on a greenhouse simulator. PLC is used to monitor every sensor condition and control the green house simulator plant to keep it in the desired condition. PLC is connected to SCADA to monitor its controlling process and can be accessed through web browser using smartphone. From the results of observation data, output and input in SCADA happened delay time approximately for 3.195 second because happened process update time from SCADA to PLC. In addition, the first time to access SCADA via web browser will take 47.86 -52.7 seconds because there is a process of uploading SCADA images to the folder cache for display in the web browser.

Modular Production System (MPS) is a mini industrial automation process that simulates product processing. MPS uses a Programmable Logic Controller (PLC) to observe and control system processes. The MPS used is the Festo Processing Station which is composed of several components that are numerous and varied so that the control is quite difficult, especially when using a basic PLC program. Therefore, this paper proposes that MPS is classified as a Discrete Event System (DES). The method used to control DES is the Supervisory Control Theory (SCT). MPS is used to process two types of materials (metal and non-metal). The drilling process is carried out only for metal. PLC-based MPS is successfully controlled using the SCT method. MPS with the SCT method can process the material with 100% success. The PLC program using the SCT method can save data memory by 2.3% compared to without SCT.