Abstract
In this letter, support vector regression (SVR)- and adaptive neuro-fuzzy inference system (ANFIS)-based controllers are implemented for position control of a three-phase permanent magnet brushless DC (PMBLDC) motor. The performance of proposed control schemes is compared with the conventional PI controller for different angular positions of the rotor. The simulation results show the effectiveness of the proposed schemes in terms of rise time \((t_\mathrm{r})\) and steady-state error \((e_\mathrm{ss})\) with ANFIS showing an improvement of 99.2% and SVR showing 90.6% for steady-state error in comparison with the conventional PI approach. The improvement for rise time is 4% and 1.4% by ANFIS and SVR, respectively, in comparison with the conventional PI approach.
References
Premkumar P, Manikandanb BV (2015) Speed control of Brushless DC motor using bat algorithm optimized adaptive neuro-fuzzy inference system. Appl Soft Comput 32:403–419
El-samahya A, Shamseldin MA (2018) Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control. Ain Shams Eng J 9:341–353
Premkumar P, Manikandanb BV (2015) Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 157:76–90
Rubaai A, Jerry J (2014) Hybrid fuzzy bang-bang mode controller with switching function for electric motor drive applications. IEEE Trans Ind Appl 50:2269–2276
Singh B, Kumar K (2016) Simple brushless DC motor drive for solar photovoltaic array fed water pumping system. IET Power Electron 9:1487–1495
Lee BK, Ehsani M (2003) Advanced simulation model for brushless DC motor drives. Electric Power Comp Syst 31:841–868
Astrom KJ, Hagglund T (1995) PID controllers: theory, design and tuning, 2nd edn. Instrument society of America, Research Triangle Park
Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw 17:113–126
Smola AJ, Schölkopf B (2004) A tutorial on support vector regression’. Stat Comput 14(3):199–222
Jang JSR (2002) ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybernet 23:665–685
Acknowledgements
The research of the authors is supported by Thapar Institute of Engineering and Technology, Patiala, under Seed Money Project No. TU/DORSP/57/426.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Chopra, V., Singh, N.J., Kumar, R. et al. Position Control of PMBLDC Motor Using SVR- and ANFIS-Based Controllers. Natl. Acad. Sci. Lett. 45, 57–60 (2022). https://doi.org/10.1007/s40009-021-01054-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40009-021-01054-x