Stepper Motor Position Control Using PD and MPC Algorithms Embedded in Programmable Logic Controller
This research studies the implementation of Proportional-Derivative (PD) and Model Predictive Control (MPC) approaches embedded in an industrial Programmable Logic Controller (PLC) to achieve the precise position control of a stepper motor. The MPC algorithm is widely used in industrial plants, part...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10897998/ |
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| Summary: | This research studies the implementation of Proportional-Derivative (PD) and Model Predictive Control (MPC) approaches embedded in an industrial Programmable Logic Controller (PLC) to achieve the precise position control of a stepper motor. The MPC algorithm is widely used in industrial plants, particularly in slower processes, such as press machines and heat treatment. However, their application in faster processes, such as servomotors and robotics, is often require faster optimization algorithms or more powerful processors. In this project, MATLAB Simulink® was used to model the motor and test the controller in advance to reduce the field commissioning time. Once validated, the model was exported to PLC-compatible code for real-time system integration. In addition, this study compares the result of MPC algorithm with traditional control strategies, such as PD. To generate pulses and read the motor encoder signal, the project used special function modules and implemented IEC 61131 programming code in ladder logic, function blocks, and structured text. The integration of Simulink with PLCs proved successful, demonstrating the adaptability and responsiveness of the system under varying conditions. Detailed analyses of motor position and speed further highlight the performance of the system. The PD controller exhibited more effective results in controlling position than the PI and MPC controllers. Implementing MPC on PLCs present challenges such as limited computational power, memory, and programming flexibility. PLCs often lack the processing capacity for real-time optimization and fast sampling rates required for accurate predictions. |
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| ISSN: | 2169-3536 |