Startseite Optimal tuning of PID controller in time delay system: a review on various optimization techniques
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Optimal tuning of PID controller in time delay system: a review on various optimization techniques

  • Thomas George EMAIL logo und V. Ganesan
Veröffentlicht/Copyright: 2. Oktober 2020
Veröffentlichen auch Sie bei De Gruyter Brill
Manuskript einreichen Informationen für Autor*innen
Chemical Product and Process Modeling
Aus der Zeitschrift Chemical Product and Process Modeling Band 17 Heft 1

Abstract

The processes which contain at least one pole at the origin are known as integrating systems. The process output varies continuously with time at certain speed when they are disturbed from the equilibrium operating point by any environment disturbance/change in input conditions and thus they are considered as non-self-regulating. In most occasions this phenomenon is very disadvantageous and dangerous. Therefore it is always a challenging task to efficient control such kind of processes. Depending upon the number of poles present at the origin and also on the location of other poles in transfer function different types of integrating systems exist. Stable first order plus time delay systems with an integrator (FOPTDI), unstable first order plus time delay systems with an integrator (UFOPTDI), pure integrating plus time delay (PIPTD) systems and double integrating plus time delay (DIPTD) systems are the classifications of integrating systems. By using a well-controlled positioning stage the advances in micro and nano metrology are inevitable in order satisfy the need to maintain the product quality of miniaturized components. As proportional-integral-derivative (PID) controllers are very simple to tune, easy to understand and robust in control they are widely implemented in many of the chemical process industries. In industries this PID control is the most common control algorithm used and also this has been universally accepted in industrial control. In a wide range of operating conditions the popularity of PID controllers can be attributed partly to their robust performance and partly to their functional simplicity which allows engineers to operate them in a simple, straight forward manner. One of the accepted control algorithms by the process industries is the PID control. However, in order to accomplish high precision positioning performance and to build a robust controller tuning of the key parameters in a PID controller is most inevitable. Therefore, for PID controllers many tuning methods are proposed. the main factors that lead to lifetime reduction in gain loss of PID parameters are described in This paper and also the main methods used for gain tuning based on optimization approach analysis is reviewed. The advantages and disadvantages of each one are outlined and some future directions for research are analyzed.

Keywords: fuzzy-PID controller; integral square error (ISE) method; iterative feedback tuning (IFT) method; PID controller; PSO-PID controller; time delay system
Corresponding author: Thomas George, Department of Electronics & Telecommunication, Sathyabama University, Chennai, India, E-mail:

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

References

1. Gaing, ZL. A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Trans Energy Convers 2004;19:384–91. https://doi.org/10.1109/tec.2003.821821.Suche in Google Scholar

2. Qing-Guo, W, Tong-Heng, L, Ho-Wang, F, Qiang, B, Yu, Z. PID tuning for improved performance. IEEE Trans Contr Syst Technol 1999;4:457–65. https://doi.org/10.1109/87.772161.Suche in Google Scholar

3. Norbert, H. All stabilizing PID controllers for time delay systems. Automatica 2009;45:2678–84. https://doi.org/10.1016/j.automatica.2009.07.026.Suche in Google Scholar

4. Astrom, KJ, Hagglund, T. The future of PID control. Control Eng Pract 2001;9:1163–75. https://doi.org/10.1016/s0967-0661(01)00062-4.Suche in Google Scholar

5. Wang, FS, Juang, WS, Chan, CT. Optimal tuning of PID controllers for single and cascade control loops. Chem Eng Commun 1995;132:15–34. https://doi.org/10.1080/00986449508936294.Suche in Google Scholar

6. Daley, S, Liu, GP. Optimal PID tuning using direct search algorithms. Comput Control Eng J 1999;10:51–6. https://doi.org/10.1049/cce:19990203.10.1049/cce:19990203Suche in Google Scholar

7. Chi-Tsung, H, Yue, SL. Tuning PID controller for open-loop unstable processes with time delay. Chem Eng Commun 1995;133:11–30. https://doi.org/10.1080/00986449508936309.Suche in Google Scholar

8. Deepa, SN, Sugumaran, G. Design of PID controller for higher order continuous systems using MPSO based model formulation technique. Int J Electr Electron Eng 2011;5:289–95.Suche in Google Scholar

9. Sekara, TB, Matausek, MR. Optimization of PID controller based on maximization of the proportional gain under constraints on robustness and sensitivity to measurement noise. IEEE Trans Automat Contr 2009;54:184–9. https://doi.org/10.1109/tac.2008.2008359.Suche in Google Scholar

10. Farkh, R, Laabidi, K, Ksouri, M. PI control for second order delay system with tuning parameter optimization. Int J Electr Electron Eng 2009;3:1–7. https://doi.org/10.1155/2009/212053.Suche in Google Scholar

11. Killingsworth, NJ, Krstic, M. PID tuning using extremum seeking: online, model-free performance optimization. IEEE Control Syst Mag 2006;26:70–9. https://doi.org/10.1109/mcs.2006.1580155.Suche in Google Scholar

12. Madhuranthakam, CR, Elkamel, A, Budman, H. Optimal tuning of PID controllers for FOPTD. SOPTD and SOPTD with lead processes. Chem Eng Process 2008;47:251–64. https://doi.org/10.1016/j.cep.2006.11.013.Suche in Google Scholar

13. Padma Sree, R, Srinivas, MN, Chidambaram, M. A simple method of tuning PID controllers for stable and unstable FOPTD systems. Comput Chem Eng 2004;28:2201–18. https://doi.org/10.1016/j.compchemeng.2004.04.004.Suche in Google Scholar

14. Arturo, YJC, Rene, JRT, Romero, T, Luis, MV, Roque, AOR. PID-controller tuning optimization with genetic algorithms in servo systems. Int J Adv Rob Syst 2013;10:1–14. https://doi.org/10.5772/56697.Suche in Google Scholar

15. Sigurd, S. Simple analytic rules for model reduction and PID controller tuning. J Process Contr 2003;13:291–309. https://doi.org/10.1016/s0959-1524(02)00062-8.Suche in Google Scholar

16. Zhang, J, Zhuang, J, Du, H, Wang, S. Self-organizing genetic algorithm based tuning of PID controllers. Infor Sci 2009;179:1007–18. https://doi.org/10.1016/j.ins.2008.11.038.Suche in Google Scholar

17. Ho, SJ, Li, SS, Shinn, YH. Optimizing fuzzy neural networks for tuning PID controllers using an orthogonal simulated annealing algorithm OSA. IEEE Trans Fuzzy Syst 2006;14:421–34. https://doi.org/10.1109/tfuzz.2006.876985.Suche in Google Scholar

18. Nagaraj, B, Subha, S, Rampriya, B. Tuning algorithms for PID controller using soft computing techniques. Int J Comput Sci Netw Secur 2008;8:278–81.Suche in Google Scholar

19. Edimar, JO, Leonardo, MH, Alexandre, HA, Tamara, XS. Linear programming for optimum PID controller tuning. Appl Math 2014;5:886–97. https://doi.org/10.4236/am.2014.56084.Suche in Google Scholar

20. Kiam, HA, Chong, G, Yun, L. PID control system analysis. design, and technology. IEEE Trans Contr Syst Technol 2005;13:559–76. https://doi.org/10.1109/tcst.2005.847331.Suche in Google Scholar

21. Kadu, CB, Tidame, S, Vikhe, PS, Turkane, SM. Design of PI controller for liquid level system using Siemens distributed control system. Int J Recent Technol Eng 2019;8:2783–9. https://doi.org/10.35940/ijrte.c5005.098319.Suche in Google Scholar

22. Onat, C. A new design method for PI–PD control of unstable processes with dead time. ISA Trans 2019;84:69–81. https://doi.org/10.1016/j.isatra.2018.08.029.Suche in Google Scholar PubMed

23. Zheng, M, Huang, T, Zhang, G. A new design method for PI-PD control of unstable fractional-order system with time delay. Complexity 2019;2019:1–12. https://doi.org/10.1155/2019/3253497.Suche in Google Scholar

24. Onat, C, Hamamci, SE, Obuz, S. A practical PI tuning approach for time delay systems. IFAC Proc Vol 2012;45:102–7. https://doi.org/10.3182/20120622-3-us-4021.00027.Suche in Google Scholar

25. Onat, C. WGC based robust and gain scheduling PI controller design for condensing boilers. Adv Mech Eng 2014;6:659051. https://doi.org/10.1155/2014/659051.Suche in Google Scholar

26. Ozyetkin, MM, Onat, C, Tan, N. PID tuning method for integrating processes having time delay and inverse response. IFAC-PapersOnLine 2018;51:274–9. https://doi.org/10.1016/j.ifacol.2018.06.077.Suche in Google Scholar

27. Ahn, KK, Truong, DQ. Online tuning fuzzy PID controller using robust extended Kalman filter. J Process Contr 2009;19:1011–23. https://doi.org/10.1016/j.jprocont.2009.01.005.Suche in Google Scholar

28. Rames, CP. Synthesis of PID tuning rule using the desired closed-loop response. Ind Eng Chem Res 2008;47:8684–92. https://doi.org/10.1021/ie800258c.Suche in Google Scholar

29. Masami, S. Fixed structure PID controller design for standard H∞ control problem. Automatica 2006;42:93–100. https://doi.org/10.1016/j.automatica.2005.07.006.Suche in Google Scholar

30. Tomislav, BS, Miroslav, RM. Revisiting the Ziegler–Nichols process dynamics characterization. J Process Contr 2010;20:360–3. https://doi.org/10.1016/j.jprocont.2009.08.004.Suche in Google Scholar

31. Jing-Chung, S. New tuning method for PID controller. ISA Trans 2002;41:473–84. https://doi.org/10.1016/s0019-0578(07)60103-7.Suche in Google Scholar

32. Tomislav, BS, Miroslav, RM. Classification of dynamic processes and PID controller tuning in a parameter plane. J Process Contr 2011;21:620–6. https://doi.org/10.1016/j.jprocont.2010.12.004.Suche in Google Scholar

33. Chanchal, D, Rajani, KM. An improved auto-tuning scheme for PID controllers. ISA Trans 2009;48:396–409. https://doi.org/10.1016/j.isatra.2009.07.002.Suche in Google Scholar PubMed

34. Karima, A, Rachid, M, Maamar, B, Ubaid, MAS. Closed-loop step response for tuning PID-fractional-order-filter controllers. ISA Trans 2016;64:247–57. https://doi.org/10.1016/j.isatra.2016.04.017.Suche in Google Scholar PubMed

35. Santosh Kumar, DB, Padma, SR. Tuning of IMC based PID controllers for integrating systems with time delay. ISA Trans 2016;63:242–55. https://doi.org/10.1016/j.isatra.2016.03.020.Suche in Google Scholar PubMed

36. LIANG Xi, M., LI, S.c., HASSAN, A.B. Novel PID controller tuning method based on optimization technique. J Cent South Univ Technol 2010;17:1036–42. https://doi.org/10.1007/s11771-010-0595-0.Suche in Google Scholar

37. Deepa, SN, Sugumaran, G. Design of PID controller for higher order continuous systems using MPSO based model formulation technique. Int J Comput Electr Autom Control Inf Eng 2011;5:949–55.Suche in Google Scholar

38. Ru, W, Chao, T, Jing, X, Zhongbin, W, Jingfei, J, Yiqiao, M. Pressure control for a hydraulic cylinder based on a self-tuning PID controller optimized by a hybrid optimization algorithm. Algorithms 2017;10:19. https://doi.org/10.3390/a10010019.Suche in Google Scholar

39. Zhu, W, Qixin, S, Xionglin, L. Novel HTD-CS based PID controller tuning method for time delay continuous systems with multi-objective and multi-constraint optimization. Chem Eng Res Des 2016;115:98–106. https://doi.org/10.1016/j.cherd.2016.09.025.Suche in Google Scholar

40. Hussain, KM, Allwyn, RZR, Shantha, KM, Giriraj, KSM. Comparison of PID controller tuning methods with genetic algorithm for FOPTD system. Int J Eng Res Appl 2014;4:308–14.Suche in Google Scholar

41. Mouayad Sahib, MA, Bestoun, SA. A new multiobjective performance criterion used in PID tuning optimization algorithms. Int J Adv Res 2016;7:125–34. https://doi.org/10.1016/j.jare.2015.03.004.Suche in Google Scholar PubMed PubMed Central

42. Majid, Z, Masoud, KG, Nasser, S, Mostafa, P. Design of a fractional order PID controller for an AVR using particle swarm optimization. Contr Eng Pract 2009;17:1380–7. https://doi.org/10.1016/j.conengprac.2009.07.005.Suche in Google Scholar

43. Pritesh, S, Sudhir, A. Review of fractional PID controller. Mechatronics 2016;38:29–41. https://doi.org/10.1016/j.mechatronics.2016.06.005.Suche in Google Scholar

44. Reynoso-Meza, G, Javier, S, Xavier, B, Miguel, M. Preference driven multi-objective optimization design procedure for industrial controller tuning. Infor Sci 2016;339:108–31. https://doi.org/10.1016/j.ins.2015.12.002.Suche in Google Scholar

45. Fabrizio, P, Antonio, V. Tuning rules for optimal PID and fractional-order PID controllers. J Process Contr 2011;21:69–81. https://doi.org/10.1016/j.jprocont.2010.10.006.Suche in Google Scholar

46. HongSheng, L, Ying, L, YangQuan, C. A fractional order proportional and derivative (FOPD) motion controller: tuning rule and experiments. IEEE Trans Contr Syst Technol 2010;18:516–20. https://doi.org/10.1109/tcst.2009.2019120.Suche in Google Scholar

47. Saptarshi, D, Suman, S, Shantanu, D, Amitava, G. On the selection of tuning methodology of FOPID controllers for the control of higher order processes. ISA Trans 2011;50:376–88. https://doi.org/10.1016/j.isatra.2011.02.003.Suche in Google Scholar PubMed

48. Feliu-Batlle, V, Rivas, PR, Castillo, GFJ. Fractional order controller robust to time delay variations for water distribution in an irrigation main canal pool. Comput Electron Agr 2009;69:185–97. https://doi.org/10.1016/j.compag.2009.08.005.Suche in Google Scholar

49. Indranil, P, Saptarshi, D, Amitava, G. Handling packet dropouts and random delays for unstable delayed processes in NCS by optimal tuning of controllers with evolutionary algorithms. ISA Trans 2011;50:557–72. https://doi.org/10.1016/j.isatra.2011.04.002.Suche in Google Scholar PubMed

50. Cheng, YC, Chyi, H. Stabilization of unstable first-order time-delay systems using fractional-order PD controllers. J Chin Inst Eng 2006;29:241–9. https://doi.org/10.1080/02533839.2006.9671121.Suche in Google Scholar

51. Cristina, IM, Eva, HD, Roxana, B. A novel tuning algorithm for fractional order IMC controllers for time delay processes. Int J Mech Eng Robot Res 2015;4:218–21. https://doi.org/10.18178/ijmerr.4.3.218-221.Suche in Google Scholar

52. Concepcion, AM, Blas, MV, Vicente, F, YangQuan, C. Tuning and auto-tuning of fractional order controllers for industry applications. Contr Eng Pract 2008;16:798–812. https://doi.org/10.1016/j.conengprac.2007.08.006.Suche in Google Scholar

53. Mohammad, ST, Mohammad, H. A note on the stability of fractional order systems. Math Comput Simulat 2009;79:1566–76. https://doi.org/10.1016/j.matcom.2008.07.003.Suche in Google Scholar

54. Ramiro, SB, Tenreiro, MJA, Alexandra, MG. Performance of fractional PID algorithms controlling nonlinear systems with saturation and backlash phenomena. J Vib Cont 2007;13:1407–18. https://doi.org/10.1177/1077546307077499.Suche in Google Scholar

55. Das, S, Das, S, Gupta, A. Fractional order modeling of a PHWR under step-back condition and control of its global power with a robust PIλDμ controller. IEEE Trans Nucl Sci 2011;58:2431–41. https://doi.org/10.1109/tns.2011.2164422.Suche in Google Scholar

56. Shunji, M. A suggestion of fractional-order controller for flexible spacecraft attitude control. Nonlinear Dyn 2002;29:251–68. https://doi.org/10.1023/A:1016566017098.10.1023/A:1016566017098Suche in Google Scholar

57. Kumar, R, Singla, SK, Chopra, V. Comparison among some well known control schemes with different tuning methods. J Appl Res Technol 2015;13:409–15. https://doi.org/10.1016/j.jart.2015.07.007.Suche in Google Scholar

58. Wei, C, Soffker, D. Optimization strategy for PID-controller design of AMB rotor systems. IEEE Trans Contr Syst Technol 2016;24:788–803. https://doi.org/10.1109/tcst.2015.2476780.Suche in Google Scholar

59. Sen, R, Pati, C, Dutta, S, Sen, R. Comparison between three tuning methods of PID control for high precision positioning stage. MAPAN-J Metrol Soc India 2015;30:65–70. https://doi.org/10.1007/s12647-014-0123-z.Suche in Google Scholar

60. Gorazd, K, Igor, S. Interval-model-based global optimization framework for robust stability and performance of PID controllers. Appl Soft Comput 2016;40:526–43. https://doi.org/10.1016/j.asoc.2015.11.046.Suche in Google Scholar

61. Saxena, N, Chidambaram, M. Improved continuous cycling method of tuning PID controllers for unstable systems. Indian Chem Eng 2018;60:213–31. https://doi.org/10.1080/00194506.2016.1145557.Suche in Google Scholar

62. Astrom, KJ, Hagglund, T. Revisiting the Ziegler–Nichols step response method for PID control. J Process Contr 2004;14:635–50. https://doi.org/10.1016/j.jprocont.2004.01.002.Suche in Google Scholar

63. Jeng, JC, Guo, PG. Disturbance-rejection-based tuning of proportional–integral– derivative controllers by exploiting closed-loop plant data. ISA Trans 2016;62:312–24. https://doi.org/10.1016/j.isatra.2016.02.011.Suche in Google Scholar PubMed

64. Duarte, V, Jose, SC. Tuning of fractional PID controllers with Ziegler–Nichols-type rules. Int J Signal Process 2006;86:2771–84. https://doi.org/10.1016/j.sigpro.2006.02.020.Suche in Google Scholar

65. D’Emilia, G, Antonio, M, Emanuela, N. Use of neural networks for quick and accurate auto-tuning of PID controller. Robot Comput Integr Manufact 2007;23:170–9. https://doi.org/10.1016/j.rcim.2006.04.001.Suche in Google Scholar

66. Anil, C, Sree, RP. Tuning of PID controllers for integrating systems using direct synthesis method. ISA Trans 2015;57:211–19. https://doi.org/10.1016/j.isatra.2015.03.002.Suche in Google Scholar

67. Wen, T, Jizhen, L, Tongwen, C, Horacio, JM. Comparison of some well-known PID tuning formulas. Comput Chem Eng 2006;30:1416–23. https://doi.org/10.1016/j.compchemeng.2006.04.001.Suche in Google Scholar

68. Fathima, SM, Vaishnavi, D, Ramkumar, EL, NithyaRani, N, Kala, H. Comparison of PID controller tuning techniques for temperature process. Int J Innov Res Electr Electron Instrum Contr Eng 2014;2:1880–3.Suche in Google Scholar

69. Panda, RC. Synthesis of PID controller for unstable and integrating processes. Chem Eng Sci 2009;64:2807–16. https://doi.org/10.1016/j.ces.2009.02.051.Suche in Google Scholar

70. Lee, HX, Zhang, L, Cai, KY, Chen, G. An improved robust fuzzy-PID controller with optimal fuzzy reasoning. IEEE Trans Syst Man Cybern B 2005;35:1283–94. https://doi.org/10.1109/tsmcb.2005.851538.Suche in Google Scholar

71. Qing, W, Changhou, L, Wei, P. IMC PID controller tuning for stable and unstable processes with time delay. Chem Eng Res Des 2016;105:120–9. https://doi.org/10.1016/j.cherd.2015.11.011.Suche in Google Scholar

72. Matausek, MR, Branislav, TJ, Ilija, MJ. Series PID controller tuning based on the SIMC rule and signal filtering. J Process Contr 2014;24:687–93. https://doi.org/10.1016/j.jprocont.2013.10.001.Suche in Google Scholar

73. Apekshit, B, Shraddha, D. Comparison of PID tuning techniques for closed loop controller of DC-DC boost converter. Int J Adv Eng Technol 2015;8:2064–73.Suche in Google Scholar

74. Konstantinos, GP, Nikolaos, IM. Optimal automatic tuning of active damping PID regulators. J Process Contr 2013;23:905–15. https://doi.org/10.1016/j.jprocont.2012.11.006.Suche in Google Scholar

75. Olivier, L, Michel, G, Magnus, M, Emmanuel, B, Lionel, T. Iterative feedback tuning of PID parameters: comparison with classical tuning rules. J Contr Eng Pract 2003;11:1023–33. https://doi.org/10.1016/s0967-0661(02)00303-9.Suche in Google Scholar

76. Precup, RE, Mircea, BR, Marius, LT, Emil, MP, Stefan, P. Stable and convergent iterative feedback tuning of fuzzy controllers for discrete-time SISO systems. Expert Syst Appl 2013;40:188–99. https://doi.org/10.1016/j.eswa.2012.07.023.Suche in Google Scholar

77. Agnihotri, SP, Laxman, MW. Iterative algorithm and curve fitting technique for tuning with time delay system PID controller. J Intell Fuzzy Syst 2015;29:1527–37. https://doi.org/10.3233/ifs-151632.Suche in Google Scholar

78. Ho, WK, Hong, Y, Hansson, A, Hjalmarsson, H, Deng, JW. Relay auto-tuning of PID controllers using iterative feedback tuning. Automatica 2003;39:149–57. https://doi.org/10.1016/s0005-1098(02)00201-7.Suche in Google Scholar

79. Agnihotri, SP, Laxman, MW. Regression model for tuning the PID controller with fractional order time delay system. Ain Shams Eng J 2014;5:1071–81. https://doi.org/10.1016/j.asej.2014.04.007.Suche in Google Scholar

80. Karthik, K, Karpagam, G. Comparison of PID controller tuning techniques for a FOPDT system. Int J Curr Eng Technol 2014;4:2667–70.Suche in Google Scholar

81. Jianhua, W, Yong, H, Zhenhua, X, Han, D. Servo performance improvement through iterative tuning feedforward controller with disturbance compensator. Int J Mach Tool Manufact 2017;117:1–10. https://doi.org/10.1016/j.ijmachtools.2017.02.002.Suche in Google Scholar

82. Manoj, KJ, Chidambaram, M. PID controller tuning for unstable systems by optimization method. Chem Eng Commun 2001;185:91–113. https://doi.org/10.1080/00986440108912857.Suche in Google Scholar

83. Romero, JA, Roberto, S, Pedro, B. PI and PID auto-tuning procedure based on simplified single parameter optimization. J Process Contr 2011;21:840–51. https://doi.org/10.1016/j.jprocont.2011.04.003.Suche in Google Scholar

84. Woo, ZW, Hung, YC, Jin, JL. A PID type fuzzy controller with self-tuning scaling factors. Fuzzy Set Syst 2000;115:321–6. https://doi.org/10.1016/s0165-0114(98)00159-6.Suche in Google Scholar

85. Yesil, E, Guzelkaya, M, Eksin, I. Self tuning fuzzy PID type load and frequency controller. Energy Convers Manag 2004;45:377–90. https://doi.org/10.1016/s0196-8904(03)00149-3.Suche in Google Scholar

86. Qiao, WZ, Masaharu, M. PID type fuzzy controller and parameters adaptive method. Fuzzy Set Syst 1996;78:23–35. https://doi.org/10.1016/0165-0114(95)00115-8.Suche in Google Scholar

87. Mohan, BM, Arpita, S. Analytical structure and stability analysis of a fuzzy PID controller. Appl Soft Comput 2008;8:749–58. https://doi.org/10.1016/j.asoc.2007.06.003.Suche in Google Scholar

88. Mann, GKI, Bao, GH, Gosine, RG. Two-level tuning of fuzzy PID controllers. IEEE Trans Syst Man Cybern B 2001;31:263–9. https://doi.org/10.1109/3477.915351.Suche in Google Scholar PubMed

89. Bouallegue, S, Haggege, J, Ayadi, M, Benrejeb, M. PID-type fuzzy logic controller tuning based on particle swarm optimization. Eng Appl Artif Intell 2012;25:484–93. https://doi.org/10.1016/j.engappai.2011.09.018.Suche in Google Scholar

90. Onur, K, Mujde, G, Ibrahim, E, Engin, Y, Tufan, K. Online tuning of fuzzy PID controllers via rule weighing based on normalized acceleration. Eng Appl Artif Intell 2013;26:184–97. https://doi.org/10.1016/j.engappai.2012.06.005.Suche in Google Scholar

91. Rainer, D, Shabroz, G, Harpreet, S, Mark, D. Robust optimization-based multi-loop PID controller tuning: a new tool and its industrial application. Contr Eng Pract 2012;20:355–70. https://doi.org/10.1016/j.conengprac.2011.10.011.Suche in Google Scholar

92. Indranil, P, Saptarshi, D, Amitava, G. Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA Trans 2011;50:28–36. https://doi.org/10.1016/j.isatra.2010.10.005.Suche in Google Scholar PubMed

93. Civelek, Z, Cam, E, Luy, M, Mamur, H. Proportional–integral–derivative parameter optimisation of blade pitch controller in wind turbines by a new intelligent genetic algorithm. IET Renew Power Gener 2016;10:1220–8. https://doi.org/10.1049/iet-rpg.2016.0029.Suche in Google Scholar

94. Ayala, HVH, Leandro, DSC. Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator. Expert Syst Appl 2012;39:8968–74. https://doi.org/10.1016/j.eswa.2012.02.027.Suche in Google Scholar

95. Neath, MJ, Swain, AK, Madawala, UK, Thrimawithana, DJ. An optimal PID controller for a bidirectional inductive power transfer system using multiobjective genetic algorithm. IEEE Trans Power Electron 2014;29:1523–31. https://doi.org/10.1109/tpel.2013.2262953.Suche in Google Scholar

96. Rao, KAG, Amarendra, RB, Durga, BP. Fuzzy pi and integrating type fuzzy PID controllers of linear, nonlinear and time -delay systems. Int J Comput Appl 2010;1:41–7. https://doi.org/10.5120/144-264.Suche in Google Scholar

97. Saad, MS, Hishamuddin, J, Intan, ZMD. PID controller tuning using evolutionary algorithms. WSEAS Trans Syst Control 2012;7:139–49.Suche in Google Scholar

98. Amir, P, Morteza, M. Design of genetic-fuzzy control strategy for parallel hybrid electric vehicles. Contr Eng Pract 2008;16:861–73. https://doi.org/10.1016/j.conengprac.2007.10.003.Suche in Google Scholar

99. Mukherjee, V, Ghoshal, SP. Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electr Power Syst Res 2007;77:1689–98. https://doi.org/10.1016/j.epsr.2006.12.004.Suche in Google Scholar

100. Amal, M, Mostafa, AEH, Hesham, AA. Design of optimal PID controller using hybrid differential evolution and particle swarm optimization with an aging leader and challengers. Appl Soft Comput 2016;38:727–37. https://doi.org/10.1016/j.asoc.2015.10.041.Suche in Google Scholar

101. Kim, TH, Ichiro, M, Toshiharu, S. Robust PID controller tuning based on the constrained particle swarm optimization. Automatica 2008;44:1104–10. https://doi.org/10.1109/cdc.2007.4434126.Suche in Google Scholar

102. Hast, M, Hägglund, T. Optimal proportional–integral–derivative set-point weighting and tuning rules for proportional set-point weights. IET Control Theory Appl 2015;9:2266–72. https://doi.org/10.1049/iet-cta.2015.0171.Suche in Google Scholar

103. Sabir, MM, Tariq, A. Optimal PID controller design through swarm intelligence algorithms for sun tracking system. Appl Math Comput 2016;274:690–9. https://doi.org/10.1016/j.amc.2015.11.036.Suche in Google Scholar

104. Dervis, K, Bahriye, A. A comparative study of Artificial Bee Colony algorithm. Appl Math Comput 2009;214:108–32. https://doi.org/10.1016/j.amc.2009.03.090.Suche in Google Scholar

105. Yang, XS. Firefly algorithm, stochastic test functions and design optimization. Int J Bio-Inspired Comput 2010;2:78–84. https://doi.org/10.1504/ijbic.2010.032124.Suche in Google Scholar

106. Avtar, S, Amrit, K. Tuning techniques of PID controller: a review. Int J Eng Res Appl 2014:40–4.Suche in Google Scholar

107. Xingchen, W, Guihe, Q, He, Y, Song, G, Liu, L, Yang, X. Using improved chaotic ant swarm to tune PID controller on cooperative adaptive cruise control. Optik 2016;127:3445–50. https://doi.org/10.1016/j.ijleo.2015.12.014.Suche in Google Scholar

108. Kumanana, D, Nagaraj, B. Tuning of proportional integral derivative controller based on firefly algorithm. Syst Sci Contr Eng 2013;1:52–6. https://doi.org/10.1080/21642583.2013.770375.Suche in Google Scholar

109. Foley, MW, Julien, RH, Brian, RC. A comparison of PID controller tuning methods. Can J Chem Eng 2005;83:712–22. https://doi.org/10.1002/cjce.5450830412.Suche in Google Scholar
Received: 2019-11-04
Accepted: 2019-11-26
Published Online: 2020-10-02

© 2020 Walter de Gruyter GmbH, Berlin/Boston

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Optimal tuning of PID controller in time delay system: a review on various optimization techniques
  4. Pareto domain: an invaluable source of process information
  5. The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model
  6. Process simulation for the production of methanol via CO2 reforming of methane route
  7. Nitrate removal studies on polyurea membrane using nanofiltration system – membrane characterization and model development
https://doi.org/10.1515/cppm-2020-2001
Schlagwörter für diesen Artikel
fuzzy-PID controller; integral square error (ISE) method; iterative feedback tuning (IFT) method; PID controller; PSO-PID controller; time delay system

Artikel in diesem Heft

  1. Frontmatter
  2. Research Articles
  3. Optimal tuning of PID controller in time delay system: a review on various optimization techniques
  4. Pareto domain: an invaluable source of process information
  5. The optimization of biodiesel production from transesterification of sesame oil via applying ultrasound-assisted techniques: comparison of RSM and ANN–PSO hybrid model
  6. Process simulation for the production of methanol via CO2 reforming of methane route
  7. Nitrate removal studies on polyurea membrane using nanofiltration system – membrane characterization and model development
Jetzt anmelden und 20% sparen
Institutioneller Zugang
Wie funktioniert Authentifizierung?
Haben Sie eine Idee, wie wir unsere Website verbessern können?
Bitte schreiben Sie uns.
© 2025 De Gruyter Brill
Heruntergeladen am 30.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/cppm-2020-2001/html?lang=de
Button zum nach oben scrollen