Skip to main content

Advertisement

Log in

A review of integer order PID and fractional order PID controllers using optimization techniques for speed control of brushless DC motor drive

  • REVIEW PAPERS
  • Published:
International Journal of System Assurance Engineering and Management Aims and scope Submit manuscript

Abstract

In the field of speed regulation of special electric motors, the twenty-first century saw significant technical advancements. The speed regulation of sensorless brushless direct current (BLDC) motors using integer order proportional integral derivative (IOPID) and fractional order PID (FOPID) controllers, as well as various optimization techniques for determining the optimum tuning parameter. Based on optimal tuning controller parameters to achieve better time domain specifications. The rotor position is determined with the aid of electrical parameter measurements in the sensor less BLDC motor speed control system of rotor position detection. In several speed control applications, back EMF is calculated in the coils to infer rotor positions. Since conventional motors wear-prone brushes are being replaced with an electronic commutator and nonlinearity issues. In order to overcome the problem the sensorless BLDC motor is becoming increasingly common. This improves the closed loop drives efficiency and controllability. The phase response of time domain features such as the static and dynamic response for managing the speed of BLDC motors when perturbed from the outside was investigated in a unique examination of IOPID and FOPID controllers.

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

References

  • Ab Talib MH, Darus IZM, Samin PM, Yatim HM, Ardani MI, Shaharuddin NMR, Hadi MS (2021) Vibration control of semi-active suspension system using PID controller with advanced firefly algorithm and particle swarm optimization. J Ambient Intell Humaniz Comput 12(1):1119–1137

    Google Scholar 

  • Albert JR, Stonier AA, Vanchinathan K (2022) Testing and performance evaluation of water pump irrigation system using voltage-lift multilevel inverter. Int J Ambient Energy 43(1):8162–8175

    Google Scholar 

  • Alkhafaji FS, Hasan WW, Isa MM, Sulaiman N (2020) A response time reduction for DC motor controller using SISO technique. Indones J Electr Mot Comput Sci 17(2):895–906

    Google Scholar 

  • Atan O, Chen D, Turk M (2016) Fractional order PID and application of its circuit model. J Chin Inst Eng 39:695–703

    Google Scholar 

  • Chakraborty S, Saha AK, Sharma S, Chakraborty R, Debnath S (2021) A hybrid whale optimization algorithm for global optimization. J Ambient Intell Humaniz Comput 1–37

  • Chandrasekaran G, Kumarasamy V, Chinraj G (2019) Test scheduling of core based system-on-chip using modified ant colony optimization. J Eur Des Syst Autom 52(6):599–605. https://doi.org/10.18280/jesa.520607

    Article  Google Scholar 

  • Chandrasekaran G, Karthikeyan PR, Kumar NS, Kumarasamy V (2021) Test scheduling of system-on-chip using dragonfly and ant lion optimization algorithms. J Intell Fuzzy Syst 40(3):4905–4917

    Google Scholar 

  • Chandrasekaran G, Kumar NS, Karthikeyan PR, Vanchinathan K, Priyadarshi N, Twala B (2022) Test scheduling and test time minimization of system-on-chip using modified BAT algorithm. IEEE Access 10:126199–126216

    Google Scholar 

  • Chen CJ (2019) An integrating genetic algorithm and modified Newton method for tracking control and vibration suppression. Artif Intell Rev 1–23

  • De Silva CW (2018) Intelligent control: fuzzy logic applications. CRC Press

    Google Scholar 

  • Dhiman G, Garg M, Nagar A, Kumar V, Dehghani M (2021) A novel algorithm for global optimization: rat swarm optimizer. J Ambient Intell Humaniz Comput 12(8):8457–8482

    Google Scholar 

  • Dineva A, Mosavi A, Ardabili SF, Vajda I, Shamshirband S, Rabczuk T, Chau KW (2019) Review of soft computing models in design and control of rotating electrical machines. Energies 12(6):1049

    Google Scholar 

  • Eckert JJ, Silva LCA, Costa ES et al (2016) Electric vehicle drivetrain optimisation. IET Electr Syst Transp 7:32–40. https://doi.org/10.1049/iet-est.2016.0022

    Article  Google Scholar 

  • El-Saadawi MM, Gouda EA, Elhosseini MA, Essa MS (2020) Identification and speed control of DC motor using fractional order PID: microcontroller. Eur J Electr Comput Sci 4:1–8. https://doi.org/10.24018/ejece.2020.4.1.170

    Article  Google Scholar 

  • El-Wakeel AS, Ellissy AE-EKM, Abdel-hamed AM (2015) A hybrid bacterial foraging-particle swarm optimization technique for optimal tuning of proportional-integral-derivative controller of a permanent magnet brushless DC motor. Electr Power Compon Syst 43:309–319. https://doi.org/10.1080/15325008.2014.981320

    Article  Google Scholar 

  • Farahani G, Rahmani K (2019) Speed control of a separately excited DC motor using new proposed fuzzy neural algorithm based on FOPID controller. J Control Autom Electr Syst 30(5):728–740

    Google Scholar 

  • George MA, Kamat DV, Indiran T (2021) OTA-C realization of an optimized FOPID controller for BLDC motor speed control. IETE J Res 1–19

  • Gnanavel C, Vanchinathan K (2022) Review and design of modular multilevel inverter with modified multicarrier PWM techniques for solar PV applications. Circuit World. https://doi.org/10.1108/CW-06-2021-0162

    Article  Google Scholar 

  • Hekimoğlu B (2019) Optimal tuning of fractional order PID controller for DC motor speed control via chaotic atom search optimization algorithm. IEEE Access 7:38100–38114

    Google Scholar 

  • Ibrahim HEA, Hassan FN, Shomer AO (2013) Optimal PID control of a brushless DC motor using PSO and BF techniques. Ain Shams Eng J 5:391–398. https://doi.org/10.1016/j.asej.2013.09.013

    Article  Google Scholar 

  • Jigang H, Hui F, Jie W (2019) A PI controller optimized with modified differential evolution algorithm for speed control of BLDC motor. Automatika 60(2):135–148

    Google Scholar 

  • Karaboga D, Kaya E (2019) Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artif Intell Rev 52(4):2263–2293

    Google Scholar 

  • Kesarkar AA, Selvaganesan N (2014) Tuning of optimal fractional-order PID controller using an artificial bee colony algorithm. Syst Sci Control Eng 3:99–105. https://doi.org/10.1080/21642583.2014.987480

    Article  Google Scholar 

  • Kommula BN, Kota VR (2021) An integrated converter topology for torque ripple minimization in BLDC motor using an ITSA technique. J Ambient Intell Humaniz Comput 1–20.

  • Kottas TL, Karlis AD, Boutalis YS (2018) A novel control algorithm for DC motors supplied by PVs using fuzzy cognitive networks. IEEE Access 6:24866–24876

    Google Scholar 

  • Kumanan D, Nagaraj B (2013) Tuning of proportional integral derivative controller based on firefly algorithm. Syst Sci Control Eng 1:52–56. https://doi.org/10.1080/21642583.2013.770375

    Article  Google Scholar 

  • Kumar NS, Chandrasekaran G, Thangavel J, Priyadarshi N, Bhaskar MS, Hussien MG, Ali MM (2022) A novel design methodology and numerical simulation of BLDC motor for power loss reduction. Appl Sci 12(20):10596

    Google Scholar 

  • Kumarasamy V, Ramasamy VK, Chinnaraj G (2021) Systematic design of multi-objective enhanced genetic algorithm optimized fractional order PID controller for sensorless brushless DC motor drive. Circuit World 48(4):479–492. https://doi.org/10.1108/cw-07-2020-0137

    Article  Google Scholar 

  • Lee JH, Song JY, Kim DW, Kim JW, Kim YJ, Jung SY (2017) Particle swarm optimization algorithm with intelligent particle number control for optimal design of electric machines. IEEE Trans Ind Electron 65(2):1791–1798

    Google Scholar 

  • Lim SM, Leong KY (2018) A brief survey on intelligent swarm-based algorithms for solving optimization problems. In: Nature-inspired methods for stochastic, robust and dynamic optimization. p 47

  • Liu XY (2016) Optimization design on fractional order PID controller based on adaptive particle swarm optimization algorithm. Nonlinear Dyn 84:379–386. https://doi.org/10.1007/s11071-015-2553-8

    Article  MathSciNet  Google Scholar 

  • Lotfy A, Kaveh M, Mosavi MR, Rahmati AR (2020) An enhanced fuzzy controller based on improved genetic algorithm for speed control of DC motors. Analog Integr Circuits Signal Process 105:141–155

    Google Scholar 

  • Mallik S, Mallik K, Barman A et al (2017) Efficiency and cost optimized design of an induction motor using genetic algorithm. IEEE Trans Industr Electron 64:9854–9863. https://doi.org/10.1109/tie.2017.2703687

    Article  Google Scholar 

  • Nath UM, Dey C, Mudi RK (2021) Review on IMC-based PID controller design approach with experimental validations. IETE J Res 69(3):1–21

    Google Scholar 

  • Padhmanabhaiyappan S, Karthik R, Ayyar K (2020) Optimal utilization of interconnected RESs to microgrid: a hybrid AWO–ANFIS technique. Soft Comput 24(14):10493–10513

    Google Scholar 

  • Patil MD, Vadirajacharya K, Khubalkar S (2020) Design of fractional order controllers using constrained optimization and reference tracking method. Int J Power Electron Drive Syst 11(1):291–301

    Google Scholar 

  • Puangdownreong D (2018) Optimal PID controller design for DC motor speed control system with tracking and regulating constrained optimization via cuckoo search. J Electr Mot Technol 13(1):460–467

    Google Scholar 

  • Rahideh A, Korakianitis T, Ruiz P et al (2010) Optimal brushless DC motor design using genetic algorithms. J Magn Magn Mater 322:3680–3687

    Google Scholar 

  • Rajasekaran P, Vanchinathan K (2013) Improved performance of four switch three phase brushless dc motor using speed-current control algorithm. Int J Comput Appl 68(11):1–7. https://doi.org/10.5120/11620-5237

    Article  Google Scholar 

  • Rajasekhar A, Kumar Jatoth R, Abraham A (2014) Design of intelligent PID/PIλDμ speed controller for chopper fed DC motor drive using opposition based artificial bee colony algorithm. Eng Appl Artif Intell 29:13–32. https://doi.org/10.1016/j.engappai.2013.12.009

    Article  Google Scholar 

  • Ramya A, Balaji M, Kamaraj V (2019) Adaptive MF tuned fuzzy logic speed controller for BLDC motor drive using ANN and PSO technique. J Eng 2019(17):3947–3950

    Google Scholar 

  • Ravikumar S, Kavitha D (2021) IOT based autonomous car driver scheme based on ANFIS and black widow optimization. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-020-02725-1

    Article  Google Scholar 

  • Roeva O (2018) Application of artificial bee colony algorithm for model parameter identification. Innovative Comput Optim Appl 741:285–303. https://doi.org/10.1007/978-3-319-66984-7_17

    Article  Google Scholar 

  • Sahu PC, Prusty RC, Panda S (2021) Improved-GWO designed FO based type-II fuzzy controller for frequency awareness of an AC microgrid under plug in electric vehicle. J Ambient Intell Humaniz Comput 12(2):1879–1896

    Google Scholar 

  • Sain D, Swain SK, Kumar T, Mishra SK (2020) Robust 2-DOF FOPID controller design for maglev system using jaya algorithm. IETE J Res 66(3):414–426

    Google Scholar 

  • Shah P, Agashe S (2016) Review of fractional PID controller. Mechatronics 38:29–41

    Google Scholar 

  • Sharifi MohammadAli, Mojallali H (2019) Multi-objective modified imperialist competitive algorithm for brushless DC motor optimization. IETE J Res 65(1):96–103

    Google Scholar 

  • Singh R, Bhushan B (2021) Improved ant colony optimization for achieving self-balancing and position control for balancer systems. J Ambient Intell Humaniz Comput 12(8):8339–8356

    Google Scholar 

  • Suja KR (2021) Mitigation of power quality issues in smart grid using levy flight-based moth flame optimization algorithm. J Ambient Intell Humaniz Comput 12(10):9209–9228

    Google Scholar 

  • Swethamarai P, Lakshmi P (2020) Adaptive-fuzzy fractional order PID controller-based active suspension for vibration control. IETE J Res 1–16

  • Thangavel J, Chinnaraj G, Chandrasekaran G, Kumarasamy V (2023) Design and development of solar photovoltaic fed modular multilevel inverter using intelligent techniques for renewable energy applications. J Intell Fuzzy Syst 44:1807–1821. https://doi.org/10.3233/jifs-220190

    Article  Google Scholar 

  • Valle RL, de Almeida PM, Ferreira AA, Barbosa PG (2017) Unipolar PWM predictive current-mode control of a variable-speed low inductance BLDC motor drive. IET Electr Power Appl 11(5):688–696

    Google Scholar 

  • Vanchinathan K, Selvaganesan N (2021) Adaptive fractional order PID controller tuning for brushless DC motor using artificial bee colony algorithm. Results Control Optim 4:100032

    Google Scholar 

  • Vanchinathan K, Valluvan KR (2015) Improvement of time response for sensorless control of BLDC motor drive using ant colony optimization technique. Int J Appl Res 10(55):3519–3524

    Google Scholar 

  • Vanchinathan K, Valluvan KR (2016) A study of sensorless BLDC motor drive and future trends. Asian J Res Soc Sci Humanit 6(9):1863–1887. https://doi.org/10.5958/2249-7315.2016.00912.6

    Article  Google Scholar 

  • Vanchinathan K, Valluvan KR (2018a) A metaheuristic optimization approach for tuning of fractional-order PID controller for speed control of sensorless BLDC motor. J Circuits Syst Comput 27(8):1850123

    Google Scholar 

  • Vanchinathan K, Valluvan KR (2018b) Tuning of fractional order proportional integral derivative controller for speed control of sensorless BLDC motor using artificial bee colony optimization technique. Intelligent and efficient electrical systems. Springer, Singapore, pp 117–127. https://doi.org/10.1007/978-981-10-4852-4_11

    Chapter  Google Scholar 

  • Vanchinathan K, Valluvan KR, Gnanavel C, Gokul C (2021a) Design methodology and experimental verification of intelligent speed controllers for sensorless permanent magnet Brushless DC motor: intelligent speed controllers for electric motor. Int Trans Electr Energy Syst 31(9):e12991

    Google Scholar 

  • Vanchinathan K, Valluvan KR, Gnanavel C, Gokul C, Albert JR (2021b) An improved incipient whale optimization algorithm based robust fault detection and diagnosis for sensorless brushless DC motor drive under external disturbances. Int Trans Electr Energy Syst 31(12):e13251

    Google Scholar 

  • Vanchinathan K, Valluvan KR, Gnanavel C, Gokul C (2022) Numerical simulation and experimental verification of fractional-order PIλ controller for solar PV fed sensorless brushless DC motor using whale optimization algorithm. Electr Power Compon Syst 50(1–2):64–80

    Google Scholar 

  • Veni KK, Kumar NS, Kumar CS (2019) A comparative study of universal fuzzy logic and PI speed controllers for four switch BLDC motor drive. Int J Power Electron 10(1–2):18–32

    Google Scholar 

  • Verma SK, Yadav S, Nagar SK (2017) Optimization of fractional order PID controller using grey wolf optimizer. J Control Autom Electr Syst 28:314–322. https://doi.org/10.1007/s40313-017-0305-3

    Article  Google Scholar 

  • Xia C, Jiang G, Chen W, Shi T (2016) Switching-gain adaptation current control for brushless DC motors. IEEE Trans Ind Electron 63(4):2044–2052

    Google Scholar 

  • Xia C, Wu D, Shi T, Chen W (2017) A current control scheme of brushless DC motors driven by four-switch three-phase inverters. IEEE J Emerg Sel Top Power Electron 5(1):547–558

    Google Scholar 

  • Xu L, Song B, Cao M, Xiao Y (2019) A new approach to optimal design of digital fractional-order PIλDμ controller. Neurocomputing 363:66–77

    Google Scholar 

  • Zaheeruddin, Singh K (2020) Intelligent fractional-order-based centralized frequency controller for microgrid. IETE J Res 1–15

  • Zaky MS, Metwaly MK (2017) A performance investigation of a four-switch three-phase inverter-fed IM drives at low speeds using fuzzy logic and PI controllers. IEEE Trans Power Electron 32(5):3741–3753

    Google Scholar 

  • Zhang X, Li J, Dang J, Liu Z, Min Y (2017) Design and parameters optimization of the fractional order anti-windup controller for multileaf collimator. J Mot Sci Technol Rev 10(2):35–41

    Google Scholar 

  • Zhao H, Song B, Zhang J, Xu L (2017) Fractional-order PID controller design based on PSO algorithm. J Shandong Univ Sci Technol (nat Sci) 36(4):60–65

    Google Scholar 

  • Zou L, Wang Z, Han Q, Zhou D (2019) Recursive filtering for time-varying systems with random access protocol. IEEE Trans Autom Control 64(2):720–727

    MathSciNet  MATH  Google Scholar 

Download references

Funding

This study did not receive any funding in any form.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Vanchinathan Kumarasamy.

Ethics declarations

Conflict of interest

The authors has no conflicts of interest.

Ethical approval

None of the authors conducted any human or animal studies.

Informed consent

All individuals expressed their consent before taking part in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumarasamy, V., KarumanchettyThottam Ramasamy, V., Chandrasekaran, G. et al. A review of integer order PID and fractional order PID controllers using optimization techniques for speed control of brushless DC motor drive. Int J Syst Assur Eng Manag 14, 1139–1150 (2023). https://doi.org/10.1007/s13198-023-01952-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13198-023-01952-x

Keywords

Navigation