Skip to main content
SpringerLink
Log in

Operating Range Scheduled Robust Dahlin Algorithm to Typical Industrial Process with Input Constraint

  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

There is a class of typical nonlinear industrial process, which can be characterized by a first-order inertia plus pure delay model in an operating range, but the model parameters are different in different operating ranges. Usually, a set of Proportion Integration Differentiation (PID) controllers may be used to control the process, and the controllers have different parameters in different operating areas. However, the adjustment process of the PID controllers’ parameters is not an easy job in practice, and the control performance may also be not perfect. The Dahlin algorithm may provide very good control performance for the process, but its control performance may become very poor if the model parameters are not accurate and/or the input is constrained. Faced with this issue, this paper proposes an Operating-Range Scheduled Robust Dahlin Algorithm (ORSRDA) for the process control with input constraint, which is designed on the basis of a nominal first-order inertia plus pure delay model and the given parameters uncertainty. The process operating ranges are divided into pre-designed several zones according to the difference between output setting value and current output when a new setting appears. For each operating range, the parameters of ORSRDA are obtained by solving a min-max problem offline to guarantee the closed-loop system’s robust stability and acquire the best step-response control performance. To eliminate the steady state error, the integration control action is added into the ORSRDA when the system output is close to its setting value. The proposed method is applied to temperature control of an experimental electric furnace to demonstrate its effectiveness and implement procedure.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. C. A. Lua, S. D, Gennaro, and M. E. S. Morales, “Nonlinear adaptive controller applied to an Antilock Braking System with parameters variations,” Int. J. Control Autom. Syst., vol. 15, no. 2, pp. 1–10, 2017.

    Google Scholar 

  2. B. B. Gao, S. S. Gao, Y. M. Zhong, G. G, Hu, and C. F, Gu, “Interacting multiple model estimation-based adaptive robust unscented Kalman filter,” Int. J. Control Autom. Syst., vol. 15, no. 5, pp. 2013–2025, 2017.

    Article  Google Scholar 

  3. Y. F, Wu and M. C, Deng, “Operator-based robust nonlinear optimal vibration control for an L-shaped arm driven by linear pulse motor,” Int. J. Control Autom. Syst., vol. 15, no. 5, pp. 2026–2033, 2017.

    Article  Google Scholar 

  4. Y. Chen and Q. W, Yang, “Design and simulation of Fuzzy Smith intelligent temperature controller,” Control Engineering of China, vol. 14, no. 4, pp. 422–429, 2007.

    Google Scholar 

  5. R. Q. Wang, K. Li, N. X, Cui, and C. H, Zhang, “A new PID-type fuzzy neural network controller based on genetic algorithm with improved Smith predictor,” Proc. of IEEE Conference on Decision and Control and 28th Chinese Control Conference, pp. 16–18, 2009.

    Google Scholar 

  6. Y. W, Chen and T. Y. Chai, “Hybrid intelligent preprocessing system of parameters in heating furnace control,” Proc. of International Conference on Natural Computation, pp. 1433–1437, 2010.

    Google Scholar 

  7. J. J. He, Y. Q. Liu, S. Y. Yu, and W. H, Gui, “Neural adaptive PSD decoupling controller and its application in three-phase electrode adjusting system of submerged arc furnace,” Journal of Central South University, vol. 20, pp. 405–412, 2013.

    Article  Google Scholar 

  8. L. Li and Z. Z, Mao, “A novel robust adaptive controller for EAF electrode regulator system based on approximate model method,” Journal of Central South University, vol. 19, pp. 2158–2166, 2012.

    Article  Google Scholar 

  9. G. C. Goodwin, R. H. Middleton, M. M, Seron, and B. Campos, “Application of nonlinear model predictive control to an industrial induction heating furnace,” Annual Reviews in Control, vol. 37, pp. 271–277, 2013.

    Article  Google Scholar 

  10. M. Suzuki, K. Katsuki, J. I. Imura, J. I. Nakagawa, T. Kurokawa, and K. Aihara, “Simultaneous optimization of slab permutation scheduling and heat controlling for a reheating furnace,” Journal of Process Control, vol. 24, pp. 225–238, 2014.

    Article  Google Scholar 

  11. Y. Y. Yang, “Development of a dynamic control model for walking beam reheating furnace,” Control and Decision, vol. 3, no. 3, pp. 51–53, 1988.

    Google Scholar 

  12. L. Wang, H. Q. Ni, R. X. Yang, P. M. Pardalos, J. Li, and M. R, Fei, “Intelligent virtual reference feedback tuning and its application to heat treatment electric furnace control,” Engineering Applications of Articial Intelligence, vol. 46, pp. 1–9, 2015.

    Article  Google Scholar 

  13. T. Y. Chai, “Optimal setting model of reheat furnace temperature,” Acta Automatica Sinica, vol. 26, no. 4, pp. 537–541, 2000.

    Google Scholar 

  14. Y. Y. Tan, J. H, Song, and S. X, Liu, “Model and algorithm for scheduling reheating furnace,” Control Theory & Applications, vol. 28, no. 11, pp. 1549–1557, 2011.

    Google Scholar 

  15. S. S. Ning, W. Wang, and Q. L, Liu, “An optimal scheduling algorithm for reheating furnace in steel production,” Control and Decision, vol. 21, no. 10, pp. 1138–1142, 2006.

    Google Scholar 

  16. E. B. Dahlin, “Designing and tuning digital controllers,” Instruin. Control Syst., vol. 41, pp. 77–83, 1968.

    Google Scholar 

  17. X. Zhao and R. Y, Li, “Comparison and analysis of Dahlin and Dead-beat algorithm for control system,” Industry and Information Education Technology, vol. 1, pp. 37–49, 2016.

    Google Scholar 

  18. Y. H. Tan, “Some research on Dahlin algorithm,” Control and Instruments in Chemical Industry, vol. 17, no. 4, pp. 18–20, 1990.

    Google Scholar 

  19. J. D, Liu and Q. S, Kong, “Boiler temperature control system with Dahlin algorithm,” Fudan University, Shanghai, 2009.

    Google Scholar 

  20. Z. Q, Zheng and Y H. Zhang, “Study on model mismatch time-delay control system based on Dahlin,” Applied Energy Technology, vol. 8, pp. 1–5, 2014.

    Google Scholar 

  21. L. J. Yang, Q. M, Li, and D. Y. Gu, “Dahlin algorithm application in a temperature control system,” Chinese Journal of Scientific Instrument, vol. 26, no. 8, pp. 450–454, 2005.

    Google Scholar 

  22. D. Y. Liang and J. J, Chen, “Simulation research of some aspects on Dahlin algorithm,” Journal of Central South University, vol. 1, no. 39, pp. 103–112, 1984.

    Google Scholar 

  23. C. L. Liu, P. F. Yang, and T. Ning, “Temperature control system of atmospheric petrochemical furnace based on Dahlin-Smith compensation,” Journal of Electronic Measurement and Instrument, vol. 23, no. 2, pp. 89–93, 2009.

    Article  Google Scholar 

  24. H. Xia and T. Xia, “Implementation of the Dahlin digital controller by IIR network method,” Proc. of International Conference on Intelligent Computation Technology and Automation, pp. 621–624, 2009.

    Google Scholar 

  25. D. D. Wen, “Study on adaptive fuzzy-Dahlin control for system with time-varying,” Proc. of International Conference on Intelligent Computation Technology and Automation, pp. 792–795, 2009.

    Google Scholar 

  26. X. L. Yang, Y. R, Du, and Z. Li, “A medical infusion heating system based on Dahlin algorithm,” Proc. of International Conference on Information and Automation, pp. 1263–1267, 2008.

    Google Scholar 

  27. W. Shen, L. S, Hu, and H. H, Shao, “Flow control based on Dahlin control algorithm in ATM networks,” Proceedings of the American Control Conference, pp. 2449–2454, 2002.

    Google Scholar 

  28. S. S. Hu, Automatic Control Theory, Science Press, Beijing, pp. 348–357, 2007.

    Google Scholar 

  29. R. H. Wang, B. X, Xue, and J. B, Zhao, “Time-varying control for discrete-time switched systems with admissible edge-dependent average dwell time,” Int. J. Control Autom. Syst., vol. 17, no. 8, pp. 1921–1934, 2019.

    Article  Google Scholar 

  30. J. H. Yi, K. H. Park, S. H. Kim, Y. K. Kwak, M. Ab-delfatah, and I. Busch-Vishniac, “Robust force control for a magnetically levitated manipulator using flux density measurement,” Control Engineering Practice, vol. 4, no. 7, pp. 957–965, 1996.

    Article  Google Scholar 

  31. R. Fales and A. Kelkar, “Robust control design for a wheel loader using mixed sensitivity H-inflnity and feedback linearization based methods,” Proceedings of the American Control Conference, pp. 4381–4386, 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Automation, Central South University, Changsha, Hunan, 410083, China

    Xiaoying Tian, Hui Peng, Xuguang Luo, Shiyuan Nie & Feng Zhou

  2. College of Mechanical and Vehicle Engineering, Hunan University, Changsha, Hunan, 410082, China

    Xiaoyan Peng

Authors
  1. Xiaoying Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hui Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xuguang Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Shiyuan Nie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Feng Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaoyan Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hui Peng.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Niket Kaisare under the direction of Editor Myo Taeg Lim. This work was supported by the National Natural Science Foundation of China (61773402, 51575167, 61540037).

Xiaoying Tian obtained her M.Eng degree in Control Science and Engineering from Henan Polytechnic University, Jiaozuo, China, in 2015. She is currently working towards a Ph.D. in Control Science and Engineering at the Central South University, Changsha, China. Her current research interests are in complex systems modeling, optimization, and control.

Hui Peng received his B.Eng. and M.Eng degrees in control science and engineering from the Central South University, Changsha, China, in 1983 and 1986, and his Ph.D. degree in Statistical Science from the Graduate University for Advanced Studies, Japan, in 2003. He has been a Professor at the Central South University, Changsha, China, since 1998. His research interests include complex systems modeling, control and optimization; advanced control theory and intelligent automation system; industrial process control system development. Corresponding author of this paper.

Xuguang Luo received his B.Eng. and M.Eng degrees in control science and engineering from the Central South University, Changsha, China, in 2014 and 2017, respectively. His current research interests are in algorithm, embedded system and technology commercialization.

Shiyuan Nie received her B.Eng. and M.Eng degrees in control science and engineering from the Central South University, Changsha, China, in 2014 and 2017. Her research interests are in complex systems modeling, optimization and control.

Feng Zhou received his B.Eng., M.Eng, and Ph.D. degrees in control science and engineering from the Central South University, Changsha, China, in 2005, 2009, and 2017, respectively. His current research interests are in complex systems modeling, optimization and control.

Xiaoyan Peng received her B.S. and M.S. degrees in mechanical engineering and a Ph.D. degree in automatic control from Hunan University, Changsha, China, in 1986, 1989, and 2013, respectively. She is currently a Professor in the College of Mechanical and Vehicle Engineering, Hunan University. Her research interests cover control of mechatronic systems and safety analysis of autonomous vehicles.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tian, X., Peng, H., Luo, X. et al. Operating Range Scheduled Robust Dahlin Algorithm to Typical Industrial Process with Input Constraint. Int. J. Control Autom. Syst. 18, 897–910 (2020). https://doi.org/10.1007/s12555-017-0714-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-017-0714-x

Keywords

Search

Navigation