Online Tuning of Set-point Regulator with a Blending Mechanism Using PI Controller

In this paper, a new control structure that exploits the advantages of one degree of freedom (1-DOF) and two degree of freedom (2-DOF) control structures with an online tuned set-point regulator with blending mechanism (SPR-BM) is proposed. In this structure, the filtered output of the reference and the pure reference signals are blended so that the overall performance of the system is ameliorated with respect to load disturbance rejection and set-point following. Internal Model Control (IMC) based PI controller is used as the primary controller and the blending dynamics are determined with the aim of producing a system output that tries to match to the filtered reference signal. After performing certain manipulations through some approximations, the resulting blending dynamics turn out to be a constant within the range of zero and one. Then, an online intelligence is injected into SPR-BM that changes the blending constant between its extreme values. The effectiveness of the proposed structure is shown both on a simulation example and on a PT-326 heat transfer process trainer experimental setup.

Online Tuning of Set-point Regulator with a Blending Mechanism Using PI Controller

In this paper, a new control structure that exploits the advantages of one degree of freedom (1-DOF) and two degree of freedom (2-DOF) control structures with an online tuned set-point regulator with blending mechanism (SPR-BM) is proposed. In this structure, the filtered output of the reference and the pure reference signals are blended so that the overall performance of the system is ameliorated with respect to load disturbance rejection and set-point following. Internal Model Control (IMC) based PI controller is used as the primary controller and the blending dynamics are determined with the aim of producing a system output that tries to match to the filtered reference signal. After performing certain manipulations through some approximations, the resulting blending dynamics turn out to be a constant within the range of zero and one. Then, an online intelligence is injected into SPR-BM that changes the blending constant between its extreme values. The effectiveness of the proposed structure is shown both on a simulation example and on a PT-326 heat transfer process trainer experimental setup.

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  • J.W. Howze, S.P. Bhattacharyya, “Robust tracking, error feedback, and two-degree-of-freedom controllers”, IEEE Trans. Automatic Control, Vol. 42, No. 7, pp. 980–983, 1997.
  • K.J. ˚Astr¨om, T. H¨agglund, PID Controllers Theory: Design and Tuning, Instrument of Society of America, Research Triangle Park, NC, 1995.
  • A. Visioli, “Fuzzy logic based tuning of PID controllers for plants with under-damped response”, Proceedings of IFAC Digital Control: Past, Present and Future of PID Control pp. 577–582, 2000.
  • E.A. Eitelberg, “A regulating and tracking PI(D) controller”, Int. J. Control, Vol. 45, pp.91–95, 1987.
  • P. Hippe, C. Wurmthaler, F. Dittrich, “Comments on a regulating and tracking PI(D) controller”, Int. J. Control, Vol. 46, No. 5, pp. 1851–1856, 1987.
  • R.J. Mantz, E.J. Tacconi, “Complementary rules to Ziegler-Nichols’ rule for a regulating and tracking controller”, Int. J. Control, Vol. 49, No. 5, pp. 1465–1471, 1989.
  • R.J. Mantz, E.J. Tacconi, “A regulating and tracking PID controller”, Ind. Eng. Chem. Res., Vol. 29, pp. –1253, 1990.
  • K.J. ˚Astr¨om, T. H¨agglund, “Automatic tuning of PID controllers based on dominant pole design”, Proceedings of IFAC Adaptive Control of Chemical Processes, pp. 205–210, 1985.
  • C.C. Hang, K.J. ˚Astr¨om, W.K. Ho, “ReŞnements of the Ziegler-Nichols tuning formula”, IEE Proc. Control Theory App., Vol. 138, No. 2, pp. 111–118, 1991.
  • C.C. Hang, KK.A. Sin, “A comparative performance study of PID auto-tuners”, IEEE Control Syst. Mag., Vol. , pp. 41–47, 1991.
  • A Leva, A.M. Colombo, “Method for optimising set-point weights in ISA-PID autotuners”, IEE Proc. Control Theory App., Vol. 146, No. 2, pp. 137–146, 1999.
  • C.C. Hang, L. Cao, “Improvement of transient response by means of variable set point weighting”, IEEE Trans. Ind. Electron., Vol. 43, No. 4, pp. 477–484, 1996.
  • A. Visioli, “Fuzzy logic based set-point weight tuning of PID controllers”, IEEE Trans. Systems, Man, and Cybernetics A, Vol. 29, No. 6, pp. 587–592, 1999.
  • A. Visioli, “A new design for a PID plus feedforward controller”, J. Process Control, Vol. 14, pp. 457–463, 2004.
  • Q.C. Zhong, H.X. Li, “2-degree-of-freedom proportional-integral-derivative-type controller incorporating the smith principle for processes with dead time”, Ind. Eng. Chem. Res. Vol. 41, pp. 2448–2454, 2002.
  • J.E. Normey-Rico, E. Camacho, “A uniŞed approach to design dead-time compensators for stable and integrating processes with dead-time”, IEEE Trans. Automatic Control, Vol. 47, No. 2, pp. 299–305, 2002.
  • R.E. Precup, S. Preitl, “PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness”, Electrical Eng., Vol. 88, No. 2, pp. 149–156, 2006.
  • S.E. Hamamcı and A U¸car “A robust model-based control for uncertain systems”, Transactions of the Institute of Measurement and Control., Vol. 24, No. 2, pp. 431–445, 2002.
  • I. Kaya, N. Tan, D.P. Atherton, “A reŞnement procedure for PID controllers”, Electrical Eng., Vol. 88, No. 3, pp. 215–222, 2005.
  • E. Yesil, M. Guzelkaya, I. Eksin, O.A. Tekin, “Set-point regulator with blending mechanism”, Instrumentation Science & Technology, Vol. 36, No.1, pp.1–17, 2008.
  • D.E. Rivera, M. Morari, S. Skogestad, “Internal model control. 4. PID controller design”, Ind. Eng. Chem. Res., Vol. 25, pp. 252-265, 1986.
  • E. Yesil, M. Guzelkaya, I. Eksin, O.A. Tekin, “Cross-coupled ratio control structure”, Instrumentation Science & Technology, Vol. 35, No. 5, pp.523–536, 2007.
  • J.G. Zeigler, N.B. Nichols, “Optimum settings for automatic controllers”, Trans. ASME, Vol. 64, pp. 759–768, G.H. Cohen, G.A. Coon, “Theoretical consideration of retarded control”, Trans. Amer. Soc. Mech. Eng., Vol. , pp. 827–834, 1953.
  • S. Skogestad, “Simple analytic rules for model reduction and PID controller tuning”, J. Process Control, Vol. , No. 4, pp. 291–309, 2003.
  • M. Morari, E. ZaŞriou, Robust Process Control; Prentice Hall: Upper Saddle River, NJ, 1989.
  • D.E. Rivera, M. Morari, S. Skogestad, “Internal model control for PID controller design”, Ind. Eng. Chem. Proc. Des. Dev., Vol. 25, pp. 252–265, 1986.
  • J. Arputha Vijaya Selvi, T.K. Radhakrishnan, S. Sundaram, “Model based IMC controller for process with dead time”, Instrumentation Science & Technology, Vol. 34, pp. 463–474, 2006.
  • D.M. de la Pena, D.R. Ramirez, E.F. Camacho, T. Alamo, “Application of an explicit min-max MPC to a scaled laboratory process”, Control Eng. Practice, Vol. 13, No. 12, pp. 1463–1471, 2005.
  • M.R. Matausek, G.S. Kvascev, “A uniŞed step response procedure for autotuning of PI controller and Smith predictor for stable processes”, J. Process Control, Vol. 13, No. 8, pp. 787–800, 2003.
  • R. Bandyopadhyay, U.K. Chakraborty, D. Patranabis, “Autotuning a PID controller: A fuzzy-genetic approach”, J. of Sys. Architecture, Vol. 47, No. 7, pp. 663–673, 2001.
  • J.M. Dias, A. Dourado, “A self-organizing fuzzy controller with a Şxed maximum number of rules and an adaptive similarity factor”, Fuzzy Sets Systems, Vol. 103, No. 1, pp. 27–48, 1999.
  • C. Pereira, J. Henriques, A. Dourado, “Adaptive RBFNN versus conventional self-tuning: comparison of two parametric model approaches for non-linear control”, Contol Eng. Practice, Vol. 8, No. 1, pp. 3–12, 2000.
  • G.W. Ng, P.A. Cook, “Real-time control of systems with unknown and varying time-delays, using neural networks”, Eng. Appl. Artif. Intell., Vol. 11, No. 3, pp. 401–409, 1998.