Advertisement

Gain and Order Scheduling for Fractional Order Controllers

  • Indranil Pan
  • Saptarshi Das
Part of the Studies in Computational Intelligence book series (SCI, volume 438)

Abstract

In many real world scenarios, the model of the controlled process changes over time. Clearly in such situations the parameters of the controller need to be adjusted to give an acceptable level of control system performance. Many adaptive control techniques exist to cope with such changes in system dynamics. Traditionally since PID controllers have been extensively used in industrial processes, the adaptation laws looked at changing the proportional, integral and derivative gains of the controllers. With the use of Fractional order PID controllers, both the gains and the differentiation and integration orders may be fine-tuned online, to achieve a better system performance. This chapter looks at integration of computational intelligence paradigms with fractional order adaptive control and the various advantages that this synergism can offer.

Keywords

Fractional Order Network Control System Genetic Algorithm Packet Dropout Gain Schedule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Almutairi, N.B., Chow, M.Y., Tipsuwan, Y.: Network-based controlled DC motor with fuzzy compensation. In: The 27th Annual Conference of the IEEE Industrial Electronics Society, IECON 2001, vol. 3, pp. 1844–1849 (2001)Google Scholar
  2. Bhambhani, V., Han, Y., Mukhopadhyay, S., et al.: Hardware-in-the-loop experimental study on a fractional order networked control system testbed. Communications in Nonlinear Science and Numerical Simulation 15, 2486–2496 (2010)CrossRefGoogle Scholar
  3. Chen, J., Huang, T.C.: Applying neural networks to on-line updated PID controllers for nonlinear process control. Journal of Process Control 14, 211–230 (2004)CrossRefGoogle Scholar
  4. Chow, M.Y., Tipsuwan, Y.: Network-based control systems: a tutorial. In: The 27th Annual Conference of the IEEE Industrial Electronics Society, IECON 2001, vol. 3, pp. 1593–1602 (2001)Google Scholar
  5. Chow, M.Y., Tipsuwan, Y.: Gain adaptation of networked DC motor controllers based on QoS variations. IEEE Transactions on Industrial Electronics 50, 936–943 (2003)CrossRefGoogle Scholar
  6. Das, S.: Functional fractional calculus. Springer (2011)Google Scholar
  7. Das, S., Saha, S., Mukherjee, A., et al.: Adaptive Gain and Order Scheduling of Optimal Fractional Order PIλDμ Controllers with Radial Basis Function Neural-Network. In: 2011 International Conference on Process Automation, Control and Computing (PACC), pp. 1–6 (2011)Google Scholar
  8. Eriksson, L.M., Johansson, M.: Simple PID tuning rules for varying time-delay systems. In: 2007 46th IEEE Conference on Decision and Control, pp. 1801–1807 (2007)Google Scholar
  9. Fan, X., Meng, F., Fu, C., et al.: Research of brushless dc motor simulation system based on RBF-PID algorithm. In: 2009 KAM 2009 Second International Symposium on Knowledge Acquisition and Modeling, vol. 2, pp. 277–280 (2009)Google Scholar
  10. Fliess, M., Join, C.: Intelligent PID controllers. In: 2008 16th Mediterranean Conference on Control and Automation, pp. 326–331 (2008)Google Scholar
  11. Hespanha, J.P., Naghshtabrizi, P., Xu, Y.: A survey of recent results in networked control systems. Proceedings of the IEEE 95, 138–162 (2007)CrossRefGoogle Scholar
  12. Hirai, K., Satoh, Y.: Stability of a system with variable time delay. IEEE Transactions on Automatic Control 25, 552–554 (1980)MathSciNetzbMATHCrossRefGoogle Scholar
  13. Jin, Y., Branke, J.: Evolutionary optimization in uncertain environments-a survey. IEEE Transactions on Evolutionary Computation 9, 303–317 (2005)CrossRefGoogle Scholar
  14. Leith, D.J., Leithead, W.E.: Survey of gain-scheduling analysis and design. International Journal of Control 73, 1001–1025 (2000)MathSciNetzbMATHCrossRefGoogle Scholar
  15. Li, H., Chow, M.Y., Sun, Z.: Optimal stabilizing gain selection for networked control systems with time delays and packet losses. IEEE Transactions on Control Systems Technology 17, 1154–1162 (2009)CrossRefGoogle Scholar
  16. Nuella, I., Cheng, C., Chiu, M.S.: Adaptive PID controller design for nonlinear systems. Industrial & Engineering Chemistry Research 48, 4877–4883 (2009)CrossRefGoogle Scholar
  17. Pan, I., Das, S., Gupta, A.: Tuning of an optimal fuzzy PID controller with stochastic algorithms for networked control systems with random time delay. ISA Transactions 50, 28–36 (2011)CrossRefGoogle Scholar
  18. Podlubny, I.: Fractional-order systems and PIλDμ controllers. IEEE Transactions on Automatic Control 44, 208–214 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  19. Sadati, N., Ghaffarkhah, A., Ostadabbas, S.: A new neural network based FOPID controller. In: IEEE International Conference on Networking, Sensing and Control, ICNSC 2008, pp. 762–767 (2008)Google Scholar
  20. Saha, S., Das, S., Ghosh, R., et al.: Design of a Fractional Order Phase Shaper for Iso-Damped Control of a PHWR Under Step-Back Condition. IEEE Transactions on Nuclear Science 57, 1602–1612 (2010)CrossRefGoogle Scholar
  21. Sano, Y., Kita, H.: Optimization of noisy fitness functions by means of genetic algorithms using history of search with test of estimation. In: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002, vol. 1, pp. 360–365 (2002)Google Scholar
  22. Sumar, R.R., Coelho, A.A.R., Coelho, L.S.: Computational intelligence approach to PID controller design using the universal model. Information Sciences 180, 3980–3991 (2010)CrossRefGoogle Scholar
  23. Tipsuwan, Y., Chow, M.Y.: Control methodologies in networked control systems. Control Engineering Practice 11, 1099–1111 (2003)CrossRefGoogle Scholar
  24. Tipsuwan, Y., Chow, M.Y.: On the gain scheduling for networked PI controller over IP network. IEEE/ASME Transactions on Mechatronics 9, 491–498 (2004)CrossRefGoogle Scholar
  25. Tsutsui, S., Ghosh, A.: Genetic algorithms with a robust solution searching scheme. IEEE Transactions on Evolutionary Computation 1, 201–208 (1997)CrossRefGoogle Scholar
  26. Zhang, M., Li, W., Liu, M.: Adaptive PID control strategy based on RBF neural network identification. In: International Conference on Neural Networks and Brain, ICNN&B 2005, vol. 3, pp. 1854–1857 (2005)Google Scholar
  27. Zhang, M.G., Li, W.H.: Single neuron PID model reference adaptive control based on RBF neural network. In: 2006 International Conference on Machine Learning and Cybernetics, pp. 3021–3025 (2006)Google Scholar
  28. Zhao, J., Li, P., Wang, X.S.: Intelligent PID controller design with adaptive criterion adjustment via least squares support vector machine. In: Control and Decision Conference, CCDC 2009, Chinese, pp. 7–12 (2009)Google Scholar
  29. Zhuang, M., Atherton, D.: Automatic tuning of optimum PID controllers. IEE Proceedings D Control Theory and Applications 140, 216–224 (1993)zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Power EngineeringJadavpur UniversityKolkataIndia

Personalised recommendations