Hybrid Control Using Sampling PI and Fuzzy Control Methods for Large Inertia and Time Delay System

  • Jia Xie
  • Shengdun Zhao
  • Zhenghui Sha
  • Jintao Liang
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 100)


In this paper, a hybrid control strategy for large inertia and time delay system was proposed. The proposed hybrid control strategy combines the sampling PI control and the linear control methods together. Respectively, the sampling PI control method is used to overcome the disadvantages of the control problems which are caused by the large inertia and time delay; while the linear control method is applied to achieve stable control of the steady-state control process and improve the system robustness. Since the parameters of the linear control are varied in the control process, the fuzzy control method is utilized to get the optimal parameters so that can realize the effective control. The hybrid control strategy is implemented and practically applied to a large inertia and time delay system — cement mill in a cement plant. The practical control results illustrate that feasibility the proposed strategy both effective and implemental.


Hybrid control strategy Sampling PI control Linear control Fuzzy control Large inertia time delay system 


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  1. 1.
    Vrečko, D., Vrančić, D., Juričić, D.H., Strmčnik, S.: A New Modified Smith Predictor: the Concept, Design and Tuning. ISA Transactions 40, 111–121 (2001)CrossRefGoogle Scholar
  2. 2.
    Han, Z.X., Li, D., Pan, G., Huang, H.P.: Applying GIC to Improve Response of System with Time-varying Parameters and Big-inertia. In: 2nd International Symposium on Systems and Control in Aerospace and Astronautics, pp. 1–5. IEEE Press, New York (2008)CrossRefGoogle Scholar
  3. 3.
    Dikyar, S., Leblebici, T., Ozcelik, D., Unel, M., Sabanovic, A., Bogosyan, S.: Delay Compensation for Nonlinear Teleoperators Using Predictor Observers. In: 36th Annual Conference on IEEE Industrial Electronics Society, pp. 1483–1488. IEEE Press, New York (2010)Google Scholar
  4. 4.
    Fang, M.C., Zhuo, Y.Z., Lee, Z.Y.: The Application of the Self-tuning Neural Network PID Controller on the Ship Roll Reduction in Random Waves. Ocean Engineering 37, 529–538 (2010)CrossRefGoogle Scholar
  5. 5.
    Huang, S.J., Huang, C.L.: Control of an Inverted Pendulum Using Grey Prediction Model. IEEE Transactions on Industry Application 36, 452–458 (2000)CrossRefGoogle Scholar
  6. 6.
    Li, H.X., Tong, S.C.: A Hybrid Adaptive Fuzzy Control for A Class of Nonlinear MIMO Systems. IEEE Transactions on Fuzzy Systems 11, 24–34 (2003)CrossRefGoogle Scholar
  7. 7.
    Åström, K.J., Hägglund, T.: Advanced PID Control. ISA, Research Triangle Park, NC, USA (2006)Google Scholar
  8. 8.
    Neto, P., Mendes, N., Pires, J.N., Moreira, A.P.: CAD-Based Robot Programming: the Role of Fuzzy-PI Force Control in Unstructured Environments. In: 6th Annual IEEE Conference on Automation Science and Engineering, pp. 362–367. IEEE Press, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jia Xie
    • 1
  • Shengdun Zhao
    • 1
  • Zhenghui Sha
    • 2
  • Jintao Liang
    • 1
  1. 1.Department of Mechatronics EngineeringXi’an Jiaotong UniversityXi’anChina
  2. 2.Department of Materials and Mechanical EngineeringWashington State UniversityPullmanUSA

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