Skip to main content

Advertisement

Log in

Performance Evaluation of Neurobiologically Inspired Brain Emotional Adaptive Mechanism for Permanent Magnet Synchronous Motor Drive

  • Research Article-Electrical Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

This paper presents brain emotional controller (BEC)-based adaptive mechanism in Model Reference Adaptive System (MRAS) to control sensorless permanent magnet synchronous motor (PMSM) drive. BEC is a bio-inspired intelligent controller developed using neurobiological connections of mammalian brain by inspiring limbic system. The developed controller is applied as speed controller and parameter estimation (i.e. rotor speed and rotor position) of PMSM drive. The proposed BEC stability is proved to guarantee convergence characteristics. The performance of BEC-based adaptive mechanism is evaluated in simulation environment, and obtained results are compared in real time in hardware-in-loop (HIL) environment. In order to hold the effective performance of the proposed method compared with fuzzy logic controller with similar tests, further integral performances are evaluated for BEC and fuzzy logic controllers. Results demonstrated in HIL and in simulation show applicability, robust and sensitiveness of the proposed BEC method.

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

Access this article

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

Instant access to the full article PDF.

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

Abbreviations

\(V_{d} ,V_{q}\) :

Stator voltages

\(I_{d}^{*} ,I_{q}^{*}\) :

Reference currents

\(\omega_{{\text{r}}}\) :

Rotor speed

\(\hat{\omega }_{{\text{r}}}\) :

Estimated rotor speed

δ :

Rotor position

\(\hat{I}_{d}^{*} ,\hat{I}_{q}^{*}\) :

Adjustable currents

\(R_{d} ,R_{q}\) :

Stator resistance per phase

\(L_{d} ,L_{q}\) :

d-axis and q-axis inductance

P :

No. of pole pairs of motor

\(\varphi_{{\text{f}}}\) :

Rotor magnetic flux linking the stator

T L :

Load torque

T e :

Electromagnetic torque

B m :

Friction coefficient of motor

J m :

Moment of inertia of the motor and load

AG:

Amygdala

O :

Orbitofrontal cortex

S i :

Sensory input

SC:

Sensory cortex

EC:

Emotional cue

u p :

Plant output

u c :

Controller output

S ia :

Adaptive sensory input

SCa :

Adaptive sensory cortex

ECa :

Adaptive emotional cue

u ac :

Adaptive controller output

References

  1. Holden, A.V.: Nonlinear science: the impact of biology. J. Frankl. Inst. 334B(5/6), 971–1014 (1997)

    Article  MathSciNet  Google Scholar 

  2. Farina, M.; Deb, K.; Amato, P.: Dynamic multi objective optimization problems: test cases, approximations, and applications. IEEE Trans. Evol. Comput. 8, 425–442 (2004)

    Article  Google Scholar 

  3. Baud, R.; Fasola, J.; Vouga, T.; Ijspeert, A.; Bouri, M.: Bio-inspired standing balance controller for a full-mobilization exoskeleton. IEEE Int. Conf. Rehabil. Robot. 2019, 849–854 (2019). https://doi.org/10.1109/ICORR.2019.8779440

    Article  Google Scholar 

  4. Papez, J.W.: Proposed mechanism of emotion. Arch. Neurol. Psychiatry 38, 725–743 (1937)

    Article  Google Scholar 

  5. Arbib, M.A.: From cybernetics to brain theory, and more: a memoir. Cogn. Syst. Res. 50, 83–145 (2018)

    Article  Google Scholar 

  6. Lautin, A.: The Limbic Brain. Kluwer Academic Publishers, New York (2002)

    Google Scholar 

  7. Moren, C.B.: A computational model of emotional learning in the amygdala. Cybern. Syst. 32(6), 611–636 (2000)

    MATH  Google Scholar 

  8. Moren, J.: Emotion and Learning: A Computational Model of the Amygdala. Ph.D. dissertation, Lund University, Lund (2002)

  9. 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 

  10. Sadeghieh, A.; Sazgar, H.; Goodarzi, K.; Lucas, C.: Identification and real-time position control of a servo-hydraulic rotary actuator by means of a neurobiologically motivated algorithm. ISA Trans. 51, 208–219 (2012)

    Article  Google Scholar 

  11. Jafari, M.; Xu, H.; Carrillo, L.R.G.: A neurobiologically-inspired intelligent trajectory tracking control for unmanned aircraft systems with uncertain system dynamics and disturbance. Trans. Inst. Meas. Contol 41(2), 417–432 (2019)

    Article  Google Scholar 

  12. Jafari, M.; Fehr, R.; Carrillo, L.R.G.; Quesada, E.S.E.; Xu, H.: Implementation of Brain Inspired Emotional learning based intelligent controller for flocking of multi-agent systems. IFAC Pap. Online 50(1), 6934–6939 (2017)

    Article  Google Scholar 

  13. Lotfi, E.; Rezaee, A.A.: Generalized BELBIC. Neural Comput. Appl. 31, 4367–4383 (2019). https://doi.org/10.1007/s00521-018-3352-1

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Qutubuddin, M.D.; Yadaiah, N.: Modeling and implementation of brain emotional controller for permanent magnet synchronous motor drive. Eng. Appl. Artif. Intell. 60, 193–203 (2017)

    Article  Google Scholar 

  16. Alahakoon, S.; Fernando, T.; Trinh, H.; Sreeram, V.: Unknown input sliding mode functional observers with application to sensorless control of permanent magnet synchronous machines. J. Frankl. Inst. 350, 107–128 (2013)

    Article  MathSciNet  Google Scholar 

  17. Ayala, H.V.H.; Coelho, L.S.: Cascaded evolutionary algorithm for nonlinear system identification based on correlation functions and radial basis functions neural networks. Mech. Syst. Signal Process. 68–69, 378–393 (2016)

    Article  Google Scholar 

  18. Kim, W.; Chen, X.; Lee, Y.; Chung, C.C.; Tomizuka, M.: Discrete-time nonlinear damping back stepping control with observers for rejection of low and high frequency disturbances. Mech. Syst. Signal Process. 104, 436–448 (2018)

    Article  Google Scholar 

  19. Ohara, M.; Noguchi, T.: Rotor position sensorless control and its parameter sensitivity of permanent magnet motor based on model reference adaptive system. IEEE Trans. Ind. Appl. 132(3), 426–436 (2012)

    Article  Google Scholar 

  20. Esteban, E.; Salgado, O.; Iturrospe, A.; Isasa, I.: Model-based approach for elevator performance estimation. Mech. Syst. Signal Process. 68–69, 125–137 (2016)

    Article  Google Scholar 

  21. Popov, V.M.: Hyperstability of Control Systems. Springer, Berlin (1970)

    Google Scholar 

  22. De la Sen, M.: On the asymptotic hyperstability of dynamic systems with point delays. IEEE Trans. Circuits Syst. I Fundam. Theory Appl. 50(11), 1486–1488 (2003)

    Article  MathSciNet  Google Scholar 

  23. Benlaloui, I.; Drid, S.; Chrifi-Alaoui, L.; Ouriagli, M.: Implementation of a new MRAS speed sensorless vector control of induction machine. IEEE Trans. Energy Convers. 30(2), 588–595 (2015)

    Article  Google Scholar 

  24. Mahesh, P.; Yadaiah, N.: Real time implementation of state estimation technique for a sensorless PMSM drive. In: IEEE 39th National Systems Conference (NSC), pp. 1–6 (2015)

  25. Kou, P.; Zhou, J.; Wang, C.; Xiao, H.; Zhang, H.; Li, C.: Parameters identification of nonlinear state space model of synchronous generator. Eng. Appl. Artif. Intell. 24(7), 1227–1237 (2011)

    Article  Google Scholar 

  26. Wu, Y.; Li, G.: Adaptive disturbance compensation finite control set optimal control for PMSM systems based on sliding mode extended state observer. Mech. Syst. Signal Process. 98, 402–414 (2018)

    Article  Google Scholar 

  27. Chaoui, H.; Sicard, P.: Adaptive fuzzy logic control of permanent magnet synchronous machines with nonlinear friction. IEEE Trans. Ind. Electron. 59(2), 1123–1133 (2012)

    Article  Google Scholar 

  28. Niu, H.; Yu, J.; Yu, H.; Lin, C.; Zhao, L.: Adaptive fuzzy output feedback and command filtering error compensation control for permanent magnet synchronous motors in electric vehicle drive systems. J. Frankl. Inst. 354, 6610–6629 (2017)

    Article  MathSciNet  Google Scholar 

  29. Opal-RT, OP 5600, HIL Box, information guide. www.opal-rt.com

  30. Qutubuddin, M.D.; Yadaiah, N.: A new intelligent adaptive mechanism to for sensorless control for permanent magnet synchronous motor drive. Brain Inspired Cogn. Archit. 24, 47–48 (2017)

    Google Scholar 

  31. Guyton, A.C.: A Text Book of Medical Physiology. Elsevier, Amsterdam (2006)

    Google Scholar 

  32. He, J.; Maltenfort, M.G.; Wang, Q.; Hamm, T.M.: Learning from biological systems: modeling neural control. IEEE Control Syst. Mag. 21(4), 55–69 (2001). https://doi.org/10.1109/37.939944

    Article  Google Scholar 

  33. Mendonca, D.J.; Al Wallace, W.: A cognitive model of improvisation in emergency management. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 37(4), 547–561 (2007)

    Article  Google Scholar 

  34. Gadanho, S.C.; Hallam, J.: Emotion-triggered learning in autonomous robot control. Cybern. Syst. 32, 531–559 (2001)

    Article  Google Scholar 

  35. Pillay, P.; Krishnan, R.: Modeling, simulation and analysis of permanent-magnet motor drives, part i: the permanent-magnet synchronous motor drive. IEEE Trans. Ind. Appl. 25(2), 265–273 (1989)

    Article  Google Scholar 

  36. Tripathi, S.M.; Dutta, C.: Enhanced efficiency in vector control of a surface-mounted PMSM drive. J. Frankl. Inst. 355, 2392–2423 (2018)

    Article  Google Scholar 

  37. Dorf, R.C.; Bishop, R.H.: Modern Control Systems, 7th edn. Addison Wesley Longman, Menlo Park (1995)

    MATH  Google Scholar 

  38. Tavazoei, M.S.: Notes on integral performance indices in fractional-order control systems. J. Process Control 20, 285–291 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to MD Qutubuddin.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qutubuddin, M., Yadaiah, N. Performance Evaluation of Neurobiologically Inspired Brain Emotional Adaptive Mechanism for Permanent Magnet Synchronous Motor Drive. Arab J Sci Eng 47, 3181–3199 (2022). https://doi.org/10.1007/s13369-021-06111-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-06111-7

Keywords

Navigation