Microsystem Technologies

, Volume 25, Issue 3, pp 819–827 | Cite as

Fuzzy tuned model based control for level and temperature processes

  • Ujjwal Manikya NathEmail author
  • Chanchal Dey
  • Rajani K. Mudi
Technical Paper


At present, model based controllers are being extensively used in process industries due to their simple tuning strategy. Internal model control (IMC) is one of the widely accepted model based controller design methodologies in close-loop control applications with considerable process lag. But, similar to the other model based controller design techniques, availability of an appropriate linear model of the concerned process is essential for IMC design. However, in reality, most of the industrial processes are nonlinear in nature. Hence, designing of an IMC controller for such cases is truly a difficult task especially for ensuring satisfactory performance during transient as well as steady state operating conditions simultaneously. In this study, to achieve an overall acceptable process response, we propose an auto-tuning scheme for the conventional IMC-PID controller by varying its sole tuning parameter depending on the latest process operating conditions. Superiority of the proposed auto-tuned IMC-PID controller is observed for real-time level and temperature processes where the close-loop time constant (λ) is varied with the help of twenty-five fuzzy rules defined on the values of process error (e) and change of error (\(\Delta e\)).



  1. Chen D, Seborg DE (2002) PI/PID controller design based on direct synthesis and disturbance rejection. Ind Eng Chem Res 41(19):4807–4822CrossRefGoogle Scholar
  2. Chien IL (1988) IMC–PID controller design-an extension. IFAC Proc Ser 6:147–152CrossRefGoogle Scholar
  3. Datta A, Ochoa J (1996) Adaptive internal model control: design stability and analysis. Automtica 32(2):261–266MathSciNetCrossRefzbMATHGoogle Scholar
  4. Horn IG, Arulandu JR, Christopher JG, VanAntwerp JG, Braatz RD (1996) Improved filter design in internal model control. Ind Eng Chem Res 35(10):34–37CrossRefGoogle Scholar
  5. Lee J, Cho W, Edgar TF (2014) Simple analytic PID controller tuning rules revisited. Ind Eng Chem Res 53(13):5038–5047CrossRefGoogle Scholar
  6. Morari M, Zafiriou E (1989) Robust process control. Prentice-Hall, New JersyzbMATHGoogle Scholar
  7. Nath UM, Datta S, Dey C (2015a) Centralized auto-tuned IMC-PI controllers for a real time coupled tank process. Int J Sci Technol Manag 4(1):1094–1102Google Scholar
  8. Nath UM, Datta S, Dey C (2015) Centralized auto-tuned IMC-PI controllers for industrial coupled tank process with stability analysis. In: 2nd IEEE international conference on recent trends in information systems (ReTIS)Google Scholar
  9. Nath UM, Dey C, Mudi RK (2016) Fuzzy-tuned SIMC controller for level control loop. In: Springer—international conference on industry interactive innovations in science, engineering and technology (I3SET)Google Scholar
  10. Nath UM, Dey C, Mudi RK (2017) Fuzzy-based auto-tuned IMC-PID controller for level control process. In: Springer—1st international conference on computational intelligence, communication and business analytics (CICBA)Google Scholar
  11. Paul PK, Dey C, Mudi RK (2013) Design of fuzzy based IMC-PID controller for IPD process. In: IEEE conference on proceedings of the computational and business intelligence (ISCBI)Google Scholar
  12. Rivera DE, Skogested S, Morari M (1986) Internal model control for PID controller design. Ind Eng Chem Process Design Dev 25(1):252–265CrossRefGoogle Scholar
  13. Rupp D, Guzzella L (2010) Adaptive internal model control with application to fueling control. Control Eng Pract 18(8):873–881CrossRefGoogle Scholar
  14. Shamsuzzoha M (2016) IMC based robust PID controller tuning for disturbance rejection. J Cent South Univ 23:581–597CrossRefGoogle Scholar
  15. Silva GJ, Datta A (2001) Adaptive internal model control: the discrete-time case. Int J Adapt Control Signal Process 15(1):15–36CrossRefzbMATHGoogle Scholar
  16. Skogestad S (2003) Simple analytic rules for model reduction and PID controller tuning. J Process Control 13(4):291–309MathSciNetCrossRefGoogle Scholar
  17. Skogestad S (2006) Tuning for smooth PID control with acceptable disturbance rejection. Ind Eng Chem Res 45(23):7817–7822CrossRefGoogle Scholar
  18. User manual (2010) Coupled tank system, Feedback, East Sussex, UKGoogle Scholar
  19. User manual (2014) Heat flow element—HFE, Quanser, CanadaGoogle Scholar
  20. Bequette BW (2004) Process control modeling, design, and simulation. Prentice Hall, New DelhiGoogle Scholar
  21. Datta S, Nath UM, Dey C (2015) Design and implementation of decentralized IMC-PI controllers for real time coupled tank process. In: Michael Faraday IET international summit-2015 (MFIIS)Google Scholar
  22. Driankov D, Hellendron H, Reinfrank M (1993) An introduction to fuzzy control. Springer, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Ujjwal Manikya Nath
    • 1
    Email author
  • Chanchal Dey
    • 2
  • Rajani K. Mudi
    • 1
  1. 1.Department of Instrumentation and Electronics EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Applied PhysicsInstrumentation Engineering, University of CalcuttaKolkataIndia

Personalised recommendations