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Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor

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Abstract

Nowadays, high speed and high power density Brushless Direct Current (BLDC) motors have been widely utilized in the industrial area. Moreover, the design of motor simulation strategies is used in the drive system, which controls the complicated problems in the BLDC motors. However, speed regulation is a vital challenge since it affects the controller performance; the Proportional-Integral-Derivative (PID) controller is used in mechanical concerns. Therefore, this study introduces the novel Wavelet-based Fuzzy Adaptive Hybrid Bat-Vulture PID (WFA-HBVPID) controller to control the BLDC motor acceleration. Also, the developed WFA-HBVPID controller organizes the loads in the BLDC motor while verifying the gain scheduling conditions. Furthermore, this proposed PID controller is implemented using MATLAB/Simulink. Here, the performance of the motor is assessed in two ways, i.e., with hybrid optimization and without hybrid optimization. In addition, the efficiency of the developed controller has been checked over the time domain specifications like settling time, rise time, peak overshoot, and gain. To calculate the presented controller efficiency, the performances of the controller were compared with existing techniques. From the comparison of the outcomes, it is found that the proposed controller has less computation time and error rate.

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References

  1. Balamurugan K, Mahalakshmi R (2021) Analysis of brushless DC motor using deep neural network and BAT algorithm. Advances in Smart System Technologies, Springer, Singapore, pp. 51–60. https://doi.org/10.1007/978-981-15-5029-4_5

  2. Cao Y, Dhahad HA, Farouk N, Xia WF, Nikafshan Rad H, Ghasemi A, Kamranfar S, Mostafavi Sani M, Akbar Shayesteh A (2021) Multi-objective bat optimization for a biomass gasifier integrated energy system based on 4E analyses. Appl Therm Eng 196:117339. https://doi.org/10.1016/j.applthermaleng.2021.117339

    Article  Google Scholar 

  3. Chen CJ, Wu KT, Hwang SJ (2021) Development of a servo-hydraulic system with a self-tuning fuzzy PID controller to simulate injection molding process. Microsyst Technol 27(4):1217–1238. https://doi.org/10.1007/s00542-018-4171-0

    Article  Google Scholar 

  4. Dang TS, Duong DT, Le VC, Banerjee S (2021) A combined backstepping and adaptive fuzzy PID approach for trajectory tracking of autonomous mobile robots. J Braz Soc Mech Sci Eng 43(3):1–13. https://doi.org/10.1007/s40430-020-02767-8

    Article  Google Scholar 

  5. El-Hoseny HM, El Kareh ZZ, Mohamed WA et al (2019) An optimal wavelet-based multi-modality medical image fusion approach based on modified central force optimization and histogram matching. Multimed Tools Appl 78:26373–26397. https://doi.org/10.1007/s11042-019-7552-1

    Article  Google Scholar 

  6. Eltoum MAM, Hussein A, Abido MA (2021) Hybrid fuzzy fractional-order PID-based speed control for brushless DC motor. Arab J Sci Eng 1–13. https://doi.org/10.1007/s13369-020-05262-3

  7. Huynh TT, Lin CM, Le NQK, Nguyen NP, Chao F (2021) Intelligent wavelet fuzzy brain emotional controller using dual function-link network for uncertain nonlinear control systems. Appl Intell 52:1–25. https://doi.org/10.1007/s10489-021-02482-4

    Article  Google Scholar 

  8. Kanungo A, Mittal M, Dewan L (2020) Wavelet based PID controller using GA optimization and scheduling for feedback systems. J Interdiscip Math 23(1):145–152. https://doi.org/10.1080/09720502.2020.1721708

    Article  Google Scholar 

  9. Karuppannan A, Muthusamy M (2021) Wavelet neural learning-based type-2 fuzzy PID controller for speed regulation in BLDC motor. Neural Comput Appl 1-23. https://doi.org/10.1007/s00521-021-05971-2

  10. Khwarahm NR, Ararat K, Qader S, al-Quraishi AMF (2021) Modelling habitat suitability for the breeding Egyptian vulture (Neophron percnopterus) in the Kurdistan region of Iraq. Iran J Sci Technol Trans Sci 45(5):1519–1530. https://doi.org/10.1007/s40995-021-01150-z

    Article  Google Scholar 

  11. Kumar A, Sharma R (2018) A genetic algorithm based fractional fuzzy PID controller for integer and fractional order systems. Int J Intell Syst Appl (IJISA) 10(5):23–32. https://doi.org/10.5815/ijisa.2018.05.03

    Article  Google Scholar 

  12. Liu S, Yu M, Li M, Xu Q (2019) The research of virtual face based on deep convolutional generative adversarial networks using TensorFlow. Phys A: Stat Mech Appl 521:667–680. https://doi.org/10.1016/j.physa.2019.01.036

    Article  Google Scholar 

  13. Liu C, Zhao J, Gu J, Du Y et al (2020) Pressure control algorithm based on adaptive fuzzy PID with compensation correction for the tractor electronic hydraulic hitch. Appl Sci 10(9):3179. https://doi.org/10.3390/app10093179

    Article  Google Scholar 

  14. Lu P, Huang W, Xiao J (2021) Speed tracking of Brushless DC motor based on deep reinforcement learning and PID. 2021 7th international conference on condition monitoring of machinery in non-stationary operations (CMMNO), IEEE. https://doi.org/10.1109/CMMNO53328.2021.9467649

  15. Ma R, Li S, Zhang B, Li Z (2021) Towards fast and robust real image Denoising with attentive neural network and PID controller. IEEE Trans Multimedia 24:2366–2377. https://doi.org/10.1109/TMM.2021.3079697

    Article  Google Scholar 

  16. Manap HH, Wahab AKA, Zuki FM (2021) Control for carbon dioxide exchange process in a membrane oxygenator using online self-tuning fuzzy-PID controller. Biomed Signal Process Control 64:102300. https://doi.org/10.1016/j.bspc.2020.102300

    Article  Google Scholar 

  17. Mohanty D, Panda S (2021) Modified salp swarm algorithm-optimized fractional-order adaptive fuzzy PID controller for frequency regulation of hybrid power system with electric vehicle. J Control Autom Electr Syst 32(2):416–438. https://doi.org/10.1007/s40313-020-00683-9

    Article  Google Scholar 

  18. Najariyan M, Zhao Y (2021) Granular fuzzy PID controller. Expert Syst Appl 167:114182. https://doi.org/10.1016/j.eswa.2020.114182

    Article  Google Scholar 

  19. Narayanan KL, Krishnan RS, Son LH, Tung NT, Julie EG, Robinson YH, Kumar R, Gerogiannis VC (2021) Fuzzy guided autonomous nursing robot through wireless Beacon network. Multimed Tools Appl 81:3297–3325. https://doi.org/10.1007/s11042-021-11264-6

    Article  Google Scholar 

  20. Sain D, Mohan BM (2021) Modeling, simulation and experimental realization of a new nonlinear fuzzy PID controller using Center of Gravity defuzzification. ISA Trans 110:319–327. https://doi.org/10.1016/j.isatra.2020.10.048

    Article  Google Scholar 

  21. Shi Q, Lam HK, Xuan C, Chen M (2020) Adaptive neuro-fuzzy PID controller based on twin delayed deep deterministic policy gradient algorithm. Neurocomputing 402:183–194. https://doi.org/10.1016/j.neucom.2020.03.063

    Article  Google Scholar 

  22. Singh R, Bhushan B (2021) Improving self-balancing and position tracking control for ball balancer application with discrete wavelet transform-based fuzzy logic controller. Int J Fuzzy Syst 23(1):27–41. https://doi.org/10.1007/s40815-020-00994-8

    Article  Google Scholar 

  23. Swethamarai P, Lakshmi P (2020) Adaptive-fuzzy fractional order PID controller-based active suspension for vibration control. IETE J res 1-16. https://doi.org/10.1080/03772063.2020.1768906

  24. Vanchinathan K, Selvaganesan N (2021) Adaptive fractional order PID controller tuning for brushless DC motor using artificial bee Colony algorithm. Results in Control and Optimization 4:100032. https://doi.org/10.1016/j.rico.2021.100032

    Article  Google Scholar 

  25. Wang MS, Chen SC, Shih CH (2018) Speed control of brushless DC motor by adaptive network-based fuzzy inference. Microsyst Technol 24(1):33–39. https://doi.org/10.1007/s00542-016-3148-0

    Article  Google Scholar 

  26. Xu Q (2013) A novel machine learning strategy based on two-dimensional numerical models in financial engineering Math Probl Eng:2013. https://doi.org/10.1155/2013/659809

  27. Xu Q, Wu J (2014) Chen Q (2014) a novel mobile personalized recommended method based on money flow model for stock exchange. Math Probl Eng 2014:1–9. https://doi.org/10.1155/2014/353910

    Article  Google Scholar 

  28. Xu Q, Wang Z, Wang F, Gong Y (2019) Multi-feature fusion CNNs for Drosophila embryo of interest detection. Phys A: Stat Mech Appl 531:121808. https://doi.org/10.1016/j.physa.2019.121808

    Article  Google Scholar 

  29. Xu Q, Wang F, Gong Y, Wang Z, Zeng K, Li Q, Luo X (2019) A novel edge-oriented framework for saliency detection enhancement. Image Vis Comput 87:1–12. https://doi.org/10.1016/j.imavis.2019.04.002

    Article  Google Scholar 

  30. Xu Q, Huang G, Yu M, Guo Y (2020) Fall prediction based on key points of human bones. Phys A: Stat Mech Appl 540:123205. https://doi.org/10.1016/j.physa.2019.123205

    Article  MathSciNet  Google Scholar 

  31. Yang S, Wei X, Wang J, Deng B, Liu C, Yu H, Li H (2017) Efficient hardware implementation of the subthalamic nucleus–external globus pallidus oscillation system and its dynamics investigation. Neural Netw 94:220–238. https://doi.org/10.1016/j.neunet.2017.07.012

    Article  Google Scholar 

  32. Yang S, Wei X, Deng B, Liu C, Li H, Wang J (2018) Efficient digital implementation of a conductance-based globus pallidus neuron and the dynamics analysis. Phys A: Stat Mech Appl 494:484–502. https://doi.org/10.1016/j.physa.2017.11.155

    Article  MathSciNet  MATH  Google Scholar 

  33. Yang S, Deng B, Wang J, Li H, Lu M, Che Y, Wei X, Loparo KA (2019) Scalable digital neuromorphic architecture for large-scale biophysically meaningful neural network with multi-compartment neurons. IEEE Trans Neural Netw Learn Syst 31(1):148–162. https://doi.org/10.1109/TNNLS.2019.2899936

    Article  Google Scholar 

  34. Yang S, Gao T, Wang J, Deng B, Lansdell B, Linares-Barranco B (2021) Efficient spike-driven learning with dendritic event-based processing. Front Neurosci 15:97. https://doi.org/10.3389/fnins.2021.601109

    Article  Google Scholar 

  35. Yang S, Wang J, Deng B, Azghadi MR, Linares-Barranco B (2021) Neuromorphic context-dependent learning framework with fault-tolerant spike routing. IEEE Trans Neural Netw Learn Syst 33:7126–7140. https://doi.org/10.1109/TNNLS.2021.3084250

    Article  Google Scholar 

  36. Zeng W, Jiang Q, Liu Y, Yan S et al (2021) Core power control of a space nuclear reactor based on a nonlinear model and fuzzy-PID controller. Prog Nucl Energy 103564. https://doi.org/10.1016/j.pnucene.2020.103564

  37. Zhang Y, Zhang S (2019) PID control with multimedia interaction based on improved genetic algorithm with its application in hydraulic transmission. Multimed Tools Appl. https://doi.org/10.1007/s11042-019-7280-6

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, 201206, India

    Abhas Kanungo

  2. Department of Electronics and Communication Engineering, IMS Engineering College, Ghaziabad, Uttar Pradesh, 201015, India

    Chandan Choubey

  3. Department of Electrical and Electronics Engineering, KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, 201206, India

    Varun Gupta

  4. Department of Electronics and Communication Engineering, SCRIET, Chaudhary Charan Singh University Campus, Meerut, Uttar Pradesh, 250003, India

    Pankaj Kumar

  5. Department of Electronics and Communication Engineering, Greater Noida Institute of Technology, Greater Noida, Uttar Pradesh, 201310, India

    Neeraj Kumar

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  1. Abhas Kanungo
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Correspondence to Abhas Kanungo.

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Kanungo, A., Choubey, C., Gupta, V. et al. Design of an intelligent wavelet-based fuzzy adaptive PID control for brushless motor. Multimed Tools Appl 82, 33203–33223 (2023). https://doi.org/10.1007/s11042-023-14872-6

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