Skip to main content
SpringerLink
Log in

Fuzzy Brain Emotional Learning Control System Design for Nonlinear Systems

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

This paper aims to develop a new fuzzy neural network by incorporating a brain emotional learning controller (BELC) with the fuzzy inference rules. The brain has an orbitofrontal cortex and an amygdala. The BELC is a mathematical model, which imitates judgment and emotion of a brain. So that BELC contains two neural networks; the former is a sensory neural network and the latter is an emotional neural network. These two systems affect each other and this will effectively improve the approximation ability of BELC. By incorporating fuzzy inference system into the BELC, a novel fuzzy brain emotional learning controller (FBELC) is proposed which includes two fuzzy inference systems. The parameter updating rules have an interacting term between two systems, which will increase the learning performance of this system. Then, the developed FBELC is applied to control nonlinear systems. Two examples, a chaotic system and an inverted double pendulum system, are demonstrated to illustrate the effectiveness of the proposed control method. A comparison between the proposed FBELC with other controllers shows that the proposed controller can achieve better control performance than the other controllers.

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

Access this article

Log in via an institution

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
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

References

  1. Yan, J.J., Shyu, K.K., Lin, J.S.: Adaptive variable structure control for uncertain chaotic systems containing dead-zone nonlinearity. Chaos Solions Fractals 25(2), 347–355 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  2. Lin, C.M., Mon, Y.J.: Decoupling control by hierarchical fuzzy sliding-mode controller. IEEE Trans. Control Syst. Technol. 13(4), 593–598 (2005)

    Article  Google Scholar 

  3. Lin, C.M., Ting, A.B., Hsu, C.F., Chung, C.M.: Adaptive control for MIMO uncertain nonlinear systems using recurrent wavelet neural network. Int. J. Neural Syst. 22(1), 37–50 (2012)

    Article  MATH  Google Scholar 

  4. Puscasu, G., Codres, B.: Nonlinear system identification and control based on modular neural network. Int. J. Neural Syst. 21(4), 319–334 (2011)

    Article  Google Scholar 

  5. Hsu, C.F., Lin, C.M., Yeh, R.G.: Supervisory adaptive dynamic RBF-based neural-fuzzy control system design for unknown nonlinear systems. Appl. Soft Comput. 13(4), 1620–1626 (2013)

    Article  Google Scholar 

  6. Mon, Y.J., Lin, C.M.: Supervisory fuzzy gaussian neural network design for mobile robot path control. Int. J. Fuzzy Syst. 15(2), 142–148 (2013)

    Google Scholar 

  7. Chen, B.S., Wu, C.C., Chen, Y.W.: Human walking gait with 11-DOF humanoid robot through robust neural fuzzy networks tracking control. Int. J. Fuzzy Syst. 15(1), 22–35 (2013)

    MathSciNet  Google Scholar 

  8. Marr, D.: A theory of cerebellar cortex. J. Physiol. 202(2), 437–470 (1969)

    Article  Google Scholar 

  9. Albus, J.S.: A new approach to manipulator control: the cerebellar model articulation controller (CMAC). J. Dyn. Syst. Meas. Control 97(3), 220–227 (1975)

    Article  MATH  Google Scholar 

  10. Jan, J.C., Hung, S.L.: High-order MS_CMAC neural network. IEEE Trans. Neural Netw. 12(3), 598–603 (2001)

    Article  Google Scholar 

  11. Lin, C.M., Peng, Y.F.: Adaptive CMAC-based supervisory control for uncertain nonlinear systems. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 1248–1260 (2004)

    Article  Google Scholar 

  12. Chen, J.Y., Tsai, P.S., Wong, C.C.: Adaptive design of a fuzzy cerebellar model arithmetic controller neural network. IEE Proc. Control Theory Appl. 152(2), 133–137 (2005)

    Article  Google Scholar 

  13. Wu, T.F., Tsai, P.S., Chang, F.R., Wang, L.S.: Adaptive fuzzy CMAC control for a class of nonlinear systems with smooth compensation. IEE Proc. Control Theory Appl. 153(6), 647–657 (2006)

    Article  Google Scholar 

  14. Lee, C.H., Chang, F.Y., Lin, C.M.: An efficient interval type-2 fuzzy CMAC for chaos time-series prediction and synchronization. IEEE Trans. Cybern. 44(3), 329–341 (2014)

    Article  Google Scholar 

  15. Lin, C.M., Li, H.Y.: Dynamic petri fuzzy cerebellar model articulation control system design for magnetic levitation system. IEEE Trans. Control Syst. Technol. 23(2), 693–699 (2015)

    Article  Google Scholar 

  16. LeDoux, J.E.: The amygdala: neurobiological aspects of emotion, pp. 339–351. Wiley-Liss, New York (1992)

    Google Scholar 

  17. Balkenius, C., Moren, J.: Emotional learning: a computational model of the amygdala. Cybern. Syst. 32(6), 611–636 (2001)

    Article  MATH  Google Scholar 

  18. Moren, J.: Emotion and Learning-A Computational Model of the Amygdala, PhD dissertation, Lund University (2002)

  19. Lucas, C., Shahmirzadi, D., Sheikholeslami, N.: Introducing BELBIC: brain emotional learning based intelligent controller. Int. J. Intell. Autom. Soft Comput. 10(1), 11–22 (2004)

    Article  Google Scholar 

  20. Roshanaei, M., Vahedi, E., Lucas, C.: Adaptive antenna applications by brain emotional learning based on intelligent controller. IET Microw. Antennas Propag. 4(12), 2247–2255 (2010)

    Article  Google Scholar 

  21. Yang, G., Cao, Y., Zhang, L.: Design of brain emotional learning model based hydraulic servo system. 2011 Second International Conference on Mechanic Automation and Control Engineering (MACE), pp. 4874–4876 (2011)

  22. Zarchi, H.A., Daryabeigi, E., Markadeh, G.R.A., Soltani, J.: Emotional controller (BELBIC) based DTC for encoderless synchronous reluctance motor drives. 2011 2nd Power Electronics, Drive Systems and Technologies Conference, pp. 478–483 (2011)

  23. Wang, L.X.: Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall, Englewood Cliffs (1994)

  24. Lin, C.T., Lee, C.S.G.: Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems. Prentice-Hall, Englewood Cliffs (1996)

  25. Chang, W.D., Yan, J.J.: Adaptive robust PID controller design based on a sliding mode for uncertain chaotic system. Chaos Solitons Fractals 26(1), 167–175 (2005)

  26. Zhou, S., Feng, G., Feng, C.B.: Robust control for a class of uncertain nonlinear system: adaptive fuzzy approach based on backstepping. Fuzzy Sets Syst. 151(1), 1–20 (2005)

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Innovation Center for Big Data and Digital Convergence, Yuan Ze University, Chung-Li, Tao-Yuan, 320, Taiwan, ROC

    Chih-Min Lin

  2. Department of Electrical Engineering, Yuan Ze University, Chung-Li, Tao-Yuan, 320, Taiwan, ROC

    Chang-Chih Chung

Authors
  1. Chih-Min Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chang-Chih Chung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chih-Min Lin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, CM., Chung, CC. Fuzzy Brain Emotional Learning Control System Design for Nonlinear Systems. Int. J. Fuzzy Syst. 17, 117–128 (2015). https://doi.org/10.1007/s40815-015-0020-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-015-0020-9

Keywords

Search

Navigation