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Abstrakty
In practical applications, an engineer is sometimes expected to execute the step test for tuning the controller without waiting much for the steady-state or a low level of disturbances. Hence, knowing that the initial settings may not be quite reliable, he/she detunes the controller by reducing its gain as a precaution against possible poor behaviour of the closed-loop system. It is up to their experience to choose by how much to detune. Therefore, the development of a practically oriented approach that would assist the engineer to choose the degree of gain reduction is the goal of this paper. The approach assumes that process parameters are determined by the least-squares approximation of the step response. Accuracy of the approximation is evaluated by a relative approximation error involving integrals of the error and the process response itself. The SIMC tuning rules are applied to choose the initial controller settings. The approach relies on detecting by simulation the worst case that may happen when the step response is triggered at any time. Detuning nomograms specify by how much to reduce the initial gain for PI-FOPTD and PID-SOPTD designs, given the relative approximation error. Two long-lasting lab experiments involving temperature control identify a plant, verify the load disturbance model through multiple step tests and demonstrate usage of the approach in the closed-loop system.
Słowa kluczowe
Czasopismo
Rocznik
Tom
Strony
212--222
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
- Faculty of Electrical and Computer Engineering, Department of Computer and Control Engineering, Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
autor
- Faculty of Electrical and Computer Engineering, Department of Computer and Control Engineering, Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland
Bibliografia
- 1. Åström KJ, Murray RM. Feedback Systems: An Introduction for Scientists and Engineers. Princeton: Princeton University Press; 2008.
- 2. Seborg DE, Edgar TF, Mellichamp DA, Doyle FJ. Process Dynamics and Control, 4th Edition. New York: Wiley; 2016.
- 3. Åström KJ, Hägglund T. Advanced PID Control. Research. Triangle Park; 2005.
- 4. Liu T, Wang QG, Huang HP. A tutorial review on process identifica-tion from step or relay feedback test. Journal of Process Control. 2013; 23(10):1597-1623.
- 5. Skogestad S. Simple analytic rules for model reduction and PID controller tuning. Journal of Process Control. 2003; 13(4):291-309.
- 6. Ljung L. System Identification: Theory for the User, 2nd Edition. New York: Prentice Hall; 1999.
- 7. Söderström T, Stoica P. System Identification, 2nd Edition. New York: Prentice Hall; 2001.
- 8. Ahmed S., Huang B, Shah SL. Novel identification method from step response. Control Engineering Practice. 2007; 15(5):545-556.
- 9. Ahmed S, Huang B, Shah SL. Identification from step responses with transient initial conditions. Journal of Process Control. 2008; 18(2):121-130.
- 10. Liu T, Huang B, Qin SJ. Bias-eliminated subspace model identifica-tion under time-varying deterministic type load disturbance. Journal of Process Control. 2015; 25:41-49.
- 11. Hou J, Liu T, Wang QG. Recursive subspace identification subject to relatively slow time-varying load disturbance. International Journal of Control. 2017; 91(3):622-638.
- 12. Dong S., Liu T, Wang W, Bao J, Cao Y. Identification of discrete-time output error model for industrial processes with time delay subject to load disturbance. Journal of Process Control. 2017; 50:40-55.
- 13. Li LJ, Dong TT, Zhang S, Zhang XX, Yang SP. Time-delay identifica-tion in dynamic processes with disturbance via correlation analysis. Control Engineering Practice. 2017; 62:92-101.
- 14. Kon J, Yamashita Y, Tanaka T, Tashiro A, Daiguji M. Practical appli-cation of model identification based on ARX models with transfer functions. Control Engineering Practice. 2013; 21(2):195-203.
- 15. Hwang SH, Lai ST. Use of two-stage least-squares algorithms for identification of continuous systems with time delay based on pulse responses. Automatica. 2004; 40 (9):1561-1568.
- 16. Yan R, Liu T, Chen F, Dong S. Gradient-based step response identi-fication of overdamped processes with time delay. Systems Science & Control Engineering. 2015; 3(1):504-513.
- 17. RamVD, Chidambaram M. On-line controller tuning for critically damped SOPTD systems. Chemical Engineering Communications. 2014; 202(1):48-58.
- 18. Du YY, Tsai JS, Patil H, Shieh LS, Chen Y. Indirect identification of continuous-time delay systems from step responses. Applied Math-ematical Modelling. 2011; 35(2):594-611.
- 19. Liu Q, Shang C, Huang D. Efficient low-order system identification from low-quality step response data with rank-constrained optimiza-tion. Control Engineering Practice. 2021; 107:104671.
- 20. Jin Q, Liu Q, Huang B. Control Design for Disturbance Rejection in the Presence of Uncertain Delays. IEEE Transactions on Automation Science and Engineering. 2017; 14(4):1570-1581.
- 21. Sung SW, Lee J, Lee IB. Process Identification and PID Control. Wiley-IEEE Pres. 2009.
- 22. Sun L, Xue W, Li D, Zhu H, Su Z. Quantitative tuning of active dis-turbance rejection controller for FOPTD model with application to power plant control. IEEE Transactions on Industrial Electronics. 2022; 69(1):805-815.
- 23. O'Dwyer A. Handbook of PI and PID Controller Tuning Rules, 3rd Edition. Imperial College Press; 2009.
- 24. Dahlin EB. Designing and tuning digital controllers. Instruments and Control Systems. 1968; 41(6):77-83.
- 25. Rivera DE, Morari M, Skogestad S. Internal model control: PID controller design. Industrial & Engineering Chemistry Process Design and Development. 1986; 25(1):252-265.
- 26. Veronesi M, Visioli A. Performance Assessment and Retuning of PID Controllers. Industrial & Engineering Chemistry Research. 2009; 48(5):2616-2623.
- 27. Grimholt C, Skogestad S. Optimal PI and PID control of first-order plus delay processes and evaluation of the original and improved SIMC rules. Journal of Process Control. 2018; 70:36–46.
- 28. Veronesi M, Visioli A. Improving lambda tuning of PI controllers for load disturbance rejection. In Proceedings of the 26th IEEE Interna-tional Conference on Emerging Technologies and Factory Automa-tion (ETFA); 2021; 1-6.
- 29. Hägglund T. A unified discussion on signal filtering in PID control. Control Engineering Practice. 2013; 21(8):994-1006.
- 30. Oliveira PM, Hedengren JD. An APMonitor temperature lab PID control experiment for undergraduate students. In Proceedings of the 24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA). 2019; 790-797.
- 31. Veronesi M, Visioli A. On the Selection of Lambda in Lambda Tuning for PI(D) Controllers. IFAC-PapersOnLine. 2020; 53(2):4599-4604.
- 32. Saxena S, Hote YV. Stabilization of perturbed system via IMC: An application to load frequency control. Control Engineering Practice. 2017; 64:61-73.
- 33. Pintelon R, Schoukens J. System Identification: A Frequency Domain Approach, 2nd Edition. Wiley; 2012.
- 34. Rzońca D, Sadolewski J, Stec A, Świder Z, Trybus B, Trybus L. Developing a multiplatform control environment. Journal of Automa-tion, Mobile Robotics and Intelligent Systems. 2019; 13(4):73-84.
- 35. EN 61131-3, Programmable controllers – Part 3: Programming languages (IEC 61131-3:2013), International Standard; 2013.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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