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Implementation of PID Autotuning Procedure Based on Doublet-Pulse Method in PLC Controller

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Języki publikacji
EN
Abstrakty
EN
The paper describes the implementation of a PI controller autotuning procedure based on the doublet-pulse method. The doublet-pulse method is used as a tool to identify the control object. Its main advantage is its simplicity, requiring only two parameters to be declared, i.e., the amplitude and duration of the two step signals. We combined the Doublet-Pulse method with the Approximate M-constrained Integral Gain Optimisation tuning rules and implemented this combination as a stand-alone autotuning procedure in Siemens S7-1200 PLC controller. The procedure was tested for three types of simulated plant models. The simulated models were diverse in terms of dynamics, as we used lag-dominated, balanced, and delay-dominated models. We compared the doublet-pulse method with the classical identification method in the form of step response, i.e. method of moments. We conducted tests for three scenarios, i.e., a step change in the set point, set point trajectory tracking and load disturbances. To assess the control quality, we used integral index IAE. The described method is universal and can also be implemented in controllers from other manufacturers.
Twórcy
  • Institute of Mechanical Engineering, Warsaw University of Life Sciences
  • Faculty of Electrical and Computer Engineering, Cracow University of Technology
  • Institute of Mechanical Engineering, Warsaw University of Life Sciences
  • Institute of Mechanical Engineering, Warsaw University of Life Sciences
Bibliografia
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  • 3. Berner J., Hägglund T., Åström K. J. Asymmetric relay autotuning–Practical features for industrial use. Control Engineering Practice 2016; 54: 231–245.
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  • 5. Hägglund, T. The one-third rule for PI controller tuning. Computers & Chemical Engineering 2019; 127(11): 25–30.
  • 6. Salat R., Awtoniuk M. Black-Box modeling of PIDs implemented in PLCs without structural information: A support vector regression approach. Neural Computing and Applications 2015; 26(3): 723-734.
  • 7. Slavíček L., Balda P., Schlegel M. Comparison of Siemens and REX controls PI(D) autotuners. In: 23rd International Conference on Process Control (PC) 2021, 212–218.
  • 8. Shen S.-H., Wu J.-S., Yu C.-C. Use of biased-relay feedback for system identification. AIChE Journal 1996; 42(4): 1174–1180.
  • 9. Yu C.-C. Autotuning of PID controllers: A relay feedback approach. Springer, 2006.
  • 10. Luyben W.L. Derivation of transfer functions for highly nonlinear distillation columns. Industrial and Engineering Chemistry Research 1987; 26(12): 2490–2495.
  • 11. Ziegler J.G., Nichols, N.B. Optimum settings for automatic controllers. ASME Transactions 1942; 64(8): 759–765.
  • 12. Luyben W.L., Eskinat E. Nonlinear auto-tune identification. International Journal of Control 1994; 59(3): 595-626.
  • 13. Berner J., Soltesz K., Hägglund T., Åström K.J. An experimental comparison of PID autotuners. Control Engineering Practice 2018; 73: 124–133.
  • 14. Anwaar H., Yixin Y., Ijaz S., Ashraf M.A., Anwaar W. Fractional order based computed torque control of 2-link robotic arm. Advances in Science and Technology Research Journal 2018; 12: 273–284.
  • 15. Åström K. J., Hägglund T. Advanced PID Control. International Society of Automation, 2005.
  • 16. Daniun M., Awtoniuk M., Sałat R. Implementation of PID autotuning procedure in PLC controller. In: ITM Web of Conferences 2017; 15(4): 05009.
  • 17. Berner J., Hägglund T., Åström K. J. Improved relay autotuning using normalized time delay. In: American Control Conference (ACC), 2016, 1869–1875.
  • 18. Berner J., Åström K.J., Hägglund T. Towards a new generation of relay autotuners. IFAC Proceedings Volumes 2014; 47: 11288–11293.
  • 19. Siemens AG. Library for Controlled System Simulation with STEP 7 (TIA Portal), 2017.
  • 20. Siemens AG. SIMATIC S7 S7-1200 Programmable Controller System Manual, 2019.
  • 21. Przystupa K. Tuning of PID controllers – approximate methods. Advances in Science and Technology Research Journal 2018; 12: 56–64.
  • 22. Grelewicz P., Khuat T. T., Czeczot J., Nowak P., Klopot T., Gabrys B. Application of machine learning to performance assessment for a class of PID-based control systems. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2023; 53: 4226–4238.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
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