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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Control Systems: Applications01:25

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Electrical engineering plays a pivotal role in our daily lives, with control systems at the heart of many applications, from home appliances to sophisticated space shuttles. Control systems manage and regulate the behavior of devices and processes, ensuring they function safely, correctly, and efficiently.
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Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
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Data Communication Based on MQTT in a Polymer Extrusion Process
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Using OPC technology to support the study of advanced process control.

Magdi S Mahmoud1, Muhammad Sabih1, Moustafa Elshafei1

  • 1Systems Engineering Department, King Fahd University of Petroleum and Minerals, P. O. Box 5067, Dhahran 31261, Saudi Arabia.

ISA Transactions
|February 23, 2015
PubMed
Summary
This summary is machine-generated.

This study demonstrates Object Linking and Embedding (OLE) for Process Control (OPC) technology as a flexible tool for advanced process control (APC) research. OPC facilitates co-simulation for developing and testing controllers in distributed control systems (DCS).

Keywords:
Distributed control systemsLabVIEWMATLABOPC (OLE for Process Control/Open Process Control)OPC clientOPC server

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Area of Science:

  • Automation and Control Engineering
  • Process Systems Engineering
  • Computer Science

Background:

  • Object Linking and Embedding (OLE) for Process Control (OPC) enables communication between diverse control systems.
  • University research centers require cost-effective and flexible tools for advanced process control (APC) development.
  • Distributed control systems (DCS) present complex challenges for control strategy implementation and testing.

Purpose of the Study:

  • To investigate OPC technology as a research tool for studying distributed control systems (DCS).
  • To develop and test advanced process control (APC) techniques within a co-simulation environment.
  • To demonstrate the application of OPC in model identification and controller design for a nonlinear boiler system.

Main Methods:

  • A co-simulation environment was established using Matlab, LabVIEW, and TCP/IP networking.
  • A nonlinear boiler model was implemented as an OPC server.
  • An OPC client was utilized for closed-loop model identification and Model Predictive Controller (MPC) design.

Main Results:

  • The OPC-based co-simulation environment proved effective for APC research in DCS.
  • Model predictive control (MPC) was successfully designed and implemented using the OPC framework.
  • The developed MPC effectively controlled NOx emissions, drum water level, and steam pressure.

Conclusions:

  • OPC technology offers a viable and economical solution for advanced process control research in academic settings.
  • Co-simulation using OPC facilitates the development and validation of sophisticated control strategies.
  • The study successfully applied OPC for integrated control of multiple process variables in a simulated boiler system.