JACIII Vol.20 No.2 pp. 271-278
doi: 10.20965/jaciii.2016.p0271


A Self-Tuning PID Control System Based on Control Performance Assessment

Weihua Cao*, †, Xuemin Hu**, Min Wu*, and Wei Yin**

*School of Automation, China University of Geosciences
Wuhan 430074, China
**Department of Information Science and Engineering, Central South University
Changsha 410083, China
Corresponding author

November 10, 2015
December 10, 2015
Online released:
March 18, 2016
March 20, 2016
performance assessment, impulse response matrix identification, time-variant system, self-tuning

A Quasi-Newton iterative method is developed for the calculation of the best achievable PID control performance and the corresponding optimal PID setting based on the control parameters and input-output data. At the basis of the proposed method, a self-tuning PID control system is proposed for the time-variant dynamic process. When controllers performance deteriorates below the general performance, controller parameters are directly adjusted with the Quasi-Newton iterative method. When below the poor performance, it can be indirectly adjusted with the identification of the closed-loop impulse response matrix. A data-driven solution is developed for calculation of the closed-loop impulse response matrix. Based on the acquired state information, system is assessed and adjusted cyclically so that a self-tuning PID control system is finally realized. Simulation results show the practicality and utility of this method.

  1. [1]  K. H. Ang, G. Chong, and Y. Li, “PID control system analysis, design, and technology,” IEEE Trans. on Control Systems Technology, Vol.13, No.4, pp. 559-576, 2005.
  2. [2]  H. K. Dong and J. H. Cho, “Robust tuning of PID controller using bacterial-foraging-based optimization,” J. of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.9, No.6, pp. 669-676, 2005.
  3. [3]  T. Mansour, A. Konno, and M. Uchiyama, “MPID control tuning for a flexible manipulator using a neural network,” J. of Robotics and Mechatronics (JRM), Vol.22, No.1, pp. 82-90, 2010.
  4. [4]  M. Veronesi and A. Visioli, “Performance assessment and retuning of PID controllers,” Industrial and Engineering Chemistry Research, Vol.48, No.5, pp. 2616-2623, 2009.
  5. [5]  M. Jelali, “An overview of control performance assessment technology and industrial applications,” Control Engineering Practice, Vol. 14, No.5, pp. 441-466, 2006.
  6. [6]  T. J. Harris, “Assessment of control loop performance,” Canadian J. of Chemical Engineering, Vol.67, No.5, pp. 856-861, 1989.
  7. [7]  T. J. Harris and W. Yu, “Controller assessment for a class of non-linear systems,” J. of Process Control, Vol.17, No.7, pp. 607-619, 2007.
  8. [8]  B. S. Ko and T. F. Edgar, “Performance assessment of cascade control loops,” American Institite of Chemical Engineers J., Vol.46, No.2, pp. 281-291, 2000.
  9. [9]  T. J. Harris, F. Boudreau, and J. F. MacGregor, “Performance assessment of multivariable feedback controllers,” Automatica, Vol.32, No.11, pp. 1505-1518, 1996.
  10. [10]  X. Wang, B. Huang, and T. Chen, “Multirate minimum variance control design and performance assessment: A data-driven subspace approach,” IEEE Trans. on Control Systems Technology, Vol.15, No.1, pp. 65-74, 2007.
  11. [11]  Q. Zhang and S. Y. Li, “Enhanced performance of subspace model-based predictive controller with parameters tuning,” The Canadian J. of Chemical Engineering, Vol.85, No.4, pp. 537-548, 2008.
  12. [12]  Y. F. Zhou and F. Wan, “A neural network approach to control performance assessment,” Int. J. of Intelligent Computing and Cybernetics, Vol.1, No.4, pp. 617-633, 2008.
  13. [13]  M. Bauer and I. K. Craig, “Economic assessment of adcanced process control-a survey and framework,” J. of Process Control, Vol.18, No.1, pp. 2-18, 2008.
  14. [14]  B. S. Ko and T. F. Edgar, “PID control performance assessment: the single-loop case,” American Institite of Chemical Engineers J., Vol.50, No.6, pp. 1211-1218, 2004.
  15. [15]  P. Hennig and M. Kiefel, “Quasi-Newton methods: a new direction,” J. of Machine Learning Research, Vol.14, No.1, pp. 843-865, 2012.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Mar. 24, 2017