Skip to main content

Advertisement

Springer Nature Link
Log in

The intelligent integrated speed controller of DTC for induction motor

  • Original Article
  • Published:
Artificial Life and Robotics Aims and scope Submit manuscript

Abstract

The parameters selection of proportional coefficient and integral coefficient (PI) for speed controller is important for direct torque control system. However, it is difficult to adjust these parameters. In this paper, firstly, we use particle swarm optimization to search the appropriate PI values of the speed controller. Secondly, based on the optimized PI parameters, the fuzzy-PI speed control strategy is presented to solve the poor self-adaptability problem. Thus, the proportional coefficient k p and integral coefficient k i can be adjusted dynamically to adapt to the speed variations. And finally, to obtain the high-speed parallel processing ability, the well-trained RBF neural network replaces the fuzzy-PI speed controller. The comparison with conventional PI speed controller shows that the proposed intelligent integrated speed controller brings good benefits of fast speed response and good stability and reduces the torque ripple. The validity of the proposed intelligent integrated speed controller is verified by the simulation results.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Depenbrock M (1988) Direct self-control of inverter-fed induction machine. IEEE Trans Power Electron 3:420–429

    Article  Google Scholar 

  2. Takahashi I, Noguchi T (1986) A new quick-response and high-efficiency control strategy of an induction motor. IEEE Trans Ind Appl IA-22(5):820–827

    Article  Google Scholar 

  3. Chen P-Y, Wu C-J, Fu Y-Y, Ko C-N (2009) A PSO method with nonlinear time-varying evolution for optimal design of PID controllers in a pendubot system. Artif Life Robot 14:58–61

    Article  Google Scholar 

  4. Le M-D, Nguyen S-H, Nguyen L-A (2004) Study on a new and effective fuzzy PID ship autopilot. Artif Life Robot 8:197–201

    Article  Google Scholar 

  5. Paladugu A, Choedhury BH (2007) Sensorless control of inverter-fed induction motor drives. Electr Power Syst Res 77:619–629

    Article  Google Scholar 

  6. Huh SH, Park JH, Choy I, Park GT (2004) Nonlinear uncertainty observer for AC motor control using the radial basis function networks. IEEE Proc Control Theory Appl 151(3):369–375

    Article  Google Scholar 

  7. Lin FJ, Shieh PH, Hung YC (2008) An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network. IET Electr Power Appl 2(1):32–41

    Article  Google Scholar 

  8. Liang D, Ding W (2009) Modeling and predicting of a switched reluctance motor drive using radial basis function network-based adaptive fuzzy system. IET Electr Power Appl 3(3):218–230

    Article  Google Scholar 

  9. Teng LT, Lin FJ, Chiang HC (2009) Recurrent wavelet neural network controller with improved particle swarm optimization for induction generator system. IET Electr Power Appl 3(2):147–159

    Article  Google Scholar 

  10. Yeh MF, Tsai CH (2010) Standalone CMAC control system with online learning ability. IEEE Trans Syst Man Cybern B 20(9):1377–1384

    Google Scholar 

  11. Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73

    Article  Google Scholar 

  12. Yang X, Zheng J (2003) Artificial neural network and blind signal processing (M). Tsinghua University Press, Beijing

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Chongqing Jiaotong University, No. 66, Xuefu Rd., Nan’an District, Chongqing, 400074, People’s Republic of China

    Kai Xu, Shanchao Liu & Xiangping Wang

Authors
  1. Kai Xu
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Shanchao Liu
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Xiangping Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Kai Xu.

About this article

Cite this article

Xu, K., Liu, S. & Wang, X. The intelligent integrated speed controller of DTC for induction motor. Artif Life Robotics 19, 33–39 (2014). https://doi.org/10.1007/s10015-013-0127-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10015-013-0127-8

Keywords