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Research of Lyapunov-theory-based Adaptive Control Improving on Smith Predictor Methods in Time-delay Systems

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  • Control Theory and Applications
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Abstract

We report on Lyapunov-theory-based model reference adaptive control schemes for improvement of Smith predictor methods applied to automatic control of time-delay systems. The theoretical analysis is carried out for first order plant models, and the Lyapunov-based adaptive control laws are derived. We also provide digital calculation methods and present numerical simulation results to verify this proposal. It is evident that the combination of Smith predictor methods and adaptive control improves the control performance of time-delay systems and prevents the system instability due to the parameter mismatches between the Smith predictor model and the real plant.

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References

  1. O. J. H. Smith, “A controller to overcome dead time,” ISA Journal, vol. 6, no. 2, pp. 28–33, 1959.

    Google Scholar 

  2. G. D. E. Alevisakis, “An extension of the Smith predictor method to multi-variable linear systems containing time delays,” International Journal of Control, vol. 17, no. 3, pp. 541–551, 1973.

    Article  Google Scholar 

  3. M. R. Matausek and A. D. Micic, “On the modified Smith predictor for controlling a process with an integrator and long dead-time,” IEEE Transactions on Automatic Control, vol. 44, no. 8, pp. 1603–1606, 1999.

    Article  MathSciNet  Google Scholar 

  4. T. Liu, Y. Z. Cai, and D. Y. Gu, “New modified Smith predictor scheme for integrating and unstable processes with time delay,” IEE Proceedings-Control Theory and Applications, vol. 152, no. 2, pp. 238–246, 2005.

    Article  Google Scholar 

  5. A. Salehiomran, R. Modirnia, B. Boulet, and M. Rochette, “Optical parametric oscillator longitudinal modes suppression based on Smith predictor control scheme,” IEEE Transactions on Control Systems Technology, vol. 22, no. 5, pp. 2064–2072, 2014.

    Article  Google Scholar 

  6. M. M. Gupta (Ed.), Adaptive Methods for Control System Design, IEEE Press, New York, 1986.

    Google Scholar 

  7. K. S. Narendra and R. V. Monopoli (Eds.), Applications of Adaptive Control, Academic Press, New York, 1980.

    Google Scholar 

  8. K. S. Narendra and L. S. Valavani, “Stable adaptive controller design — Direct control,” IEEE Transactions on Automatic Control, vol. 23, pp. 570–583, 1978.

    Article  Google Scholar 

  9. P. H. Phillipson, “Design methods for model reference adaptive systems,” Proceedings of the Institution of Mechanical Engineers, vol. 183, no. 35, pp. 695–700, 1969.

    Google Scholar 

  10. K. S. Tsakalis, “Robustness of model reference adaptive controllers: An input-output approach,” IEEE Transactions on Automatic Control, vol. 37, no. 5, pp. 556–565, 1992.

    Article  MathSciNet  Google Scholar 

  11. W. Hahn, Theory and Application of Lyapunov’s Direct Method, Prentice Hall, Englewood Cliffs, New Jersey, 1963.

    MATH  Google Scholar 

  12. B. Shackcloth and R. L. Butchart, “Synthesis of model reference adaptive systems by Lyapunov’s second method,” IFAC Proceedings Volumes, vol. 2, no. 2, pp. 145–152, 1965.

    Article  Google Scholar 

  13. R. V. Monopoli, “Lyapunov’s method for adaptive control design,” IEEE Transactions on Automatic Control, vol. 12, no. 3, pp. 334–335, 1967.

    Article  Google Scholar 

  14. C. C. Hang and B. W. Chong, “On methods of treating DC levels in an adaptive digital Smith predictor,” IEEE Transactions on Automatic Control, vol. 35, no. 1, pp. 65–66, 1990.

    Article  Google Scholar 

  15. L. J. Brown and S. P. Meyn, “Adaptive dead-time compensation with application to a robotic welding system,” IEEE Transactions on Control Systems Technology, vol. 6, no. 3, pp. 335–349, 1998.

    Article  Google Scholar 

  16. D. Meng, Y. Jia, J. Du, and F. Yu, “Learning control for time-delay systems with iteration-varying uncertainty: A Smith predictor-based approach,” IET Control Theory & Applications, vol. 4, no. 12, pp. 2707–2718, 2010.

    Article  MathSciNet  Google Scholar 

  17. L. R. da Silva, R. C. C. Flesch, and J. E. Normey-Rico, “Controlling industrial dead-time systems: When to use a PID or an advanced controller,” ISA Transactions, vol. 99, pp. 339–350, 2020.

    Article  Google Scholar 

  18. J. E. Normey-Rico and E. F. Camacho, Control of Dead-time Processes, Springer Science+Business Media, London, 2007.

    MATH  Google Scholar 

  19. A. Visioli and Q. C. Zhong, Control of Integral Processes with Dead Time, Springer Science+Business Media, London, 2011.

    Book  Google Scholar 

  20. H. Ma, H. Ren, Q. Zhou, R. Lu, and H. Li, “Approximation-based Nussbaum gain adaptive control of nonlinear systems with periodic disturbances,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 52, no. 4, pp. 2591–2600, 2022.

    Article  Google Scholar 

  21. H. Ren, H. R. Karimi, R. Lu, and Y. Wu, “Synchronization of network systems via aperiodic sampled-data control with constant delay and application to unmanned ground vehicles,” IEEE Transactions on Industrial Electronics, vol. 67, no. 6, pp. 4980–4990, 2019.

    Article  Google Scholar 

  22. Z. M. Zou, X. Y. Liu, and J. H. Zeng, “Networked control systems based on new Smith predictor and fuzzy-PID controller,” Applied Mechanics and Materials, vol. 530–531, pp. 999–1002, 2014.

    Article  Google Scholar 

  23. A. Dehghani and H. Khodadadi, “Designing a neuro-fuzzy PID controller based on smith predictor for heating system,” Proc. of 17th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea, 2017.

  24. C. L. P. Chen, G. X. Wen, Y. J. Liu, and F. Y. Wang, “Adaptive consensus control for a class of nonlinear multiagent time-delay systems using neural networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 25, no. 6, pp. 1217–1226, 2014.

    Article  Google Scholar 

  25. H. Chen, Z. Zouaoui, and F. Z. Chen, “A modified Smith predictive scheme based back-propagation neural network approach for FOPDT processes control,” Journal of Process Control, vol. 23, no. 9, pp. 1261–1269, 2013.

    Article  Google Scholar 

  26. V. de Oliveira and A. Karimi, “Robust Smith predictor design for time-delay systems with H performance,” IFAC Proceedings Volumes, vol. 46, no. 3, pp. 102–107, 2013.

    Article  Google Scholar 

  27. J. Doyle, B. A. Francis, and A. R. Tannenbaum, Feedback Control Theory, MacMillan, New York, 1992.

    Google Scholar 

  28. K. J. Åström and B. Wittenmark, Computer-Control Systems: Theory and Design, 3rd ed., Prentice Hall, Engle-wood Cliffs, NJ, 1997.

    Google Scholar 

  29. J. D. Faires and R. L. Burden, Numerical Methods, 3rd ed., Brooks Cole, 2002.

  30. J. J. D’Azzo, C. H. Houpis, and S. N. Sheldon, Linear Control System Analysis and Design with MATLAB, Marcel, Dekker, Inc, New York Basel, 2003.

    MATH  Google Scholar 

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Correspondence to Yangdong Zheng.

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This work was supported in part by Grants-in-Aid for the basic research and development of Mitsubishi Electric (China) Company Limited.

Yangdong Zheng received his B.S. degree in electronic engineering from Shanghai University, Shanghai, China, and his M.S. and Ph.D. degrees in electronic physics from Tokyo Institute of Technology, Tokyo, Japan, in 2004 and 2008, respectively. He is currently a professorial senior engineer at the Research and Development Department of Mitsubishi Electric (China) Company Limited. His research interests include adaptive control, intelligent control, robust and nonlinear control, fuzzy control, electronics, and quantum physics.

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Zheng, Y. Research of Lyapunov-theory-based Adaptive Control Improving on Smith Predictor Methods in Time-delay Systems. Int. J. Control Autom. Syst. 20, 3177–3186 (2022). https://doi.org/10.1007/s12555-021-0354-z

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  • DOI: https://doi.org/10.1007/s12555-021-0354-z

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