Speed regulation is one of the significant characteristics to be adopted in the field of brushless DC motor drive for effective and accurate speed and position control operations. In this paper, stability analysis and performance characteristics of brushless direct current motor are studied and implemented with a new deep learning neural network—fuzzy-tuned proportional integral derivative (PID) speed controller. Deep learning architecture is designed for the multi-layer perceptron network, and the output from the neural module fires the rules of the fuzzy inference system mechanism. The parameters of deep perceptron neural network (DPNN) are tuned for near optimal solutions using the unified multi-swarm particle swarm optimization, and in turn the optimized DPNN selects the parameters of the fuzzy inference system. Deep learning neural network with the fuzzy inference system tunes the gain values of the PID controller and performs an effective speed regulation. The performance characteristics of the designed speed controller are tested for a step change in input speed and also for impulsive load disturbances. Further, the stability analysis of the new proposed controller is investigated with Lyapunov stability criterion by deriving the positive definite functions. The weight parameters of DPNN model and the number of rules of fuzzy system are tuned for their near optimal solutions using multi-swarm particle swarm optimization. From the results, it is well proven that the proposed controller is more stable and guarantees consistent performance than other considered controllers in all aspects. Simulation-based comparisons illustrate that the design methodologies outperform other controller designs from the literature.
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Adli MS, Mohamad NHH, Tohara SFT (2018) Brushless DC motor speed controller for electric motorbike. Int J Power Electron Drive Syst 9(2):859
Ahmed AH, Abd El Samie B, Ali AM (2018) Comparison between fuzzy logic and PI control for the speed of BLDC motor. Int J Power Electron Drive Syst 9(3):1116
Alouane A, Rhouma AB, Khedher A (2018) FPGA implementation of a new DTC strategy dedicated to delta inverter-fed BLDC motor drives. Electr Power Compon Syst 46(6):688–700
Alsayid B, Salah WA, Alawneh Y (2019) Modelling of sensored speed control of BLDC motor using MATLAB/SIMULINK. Int J Electr Comput Eng 2088–8708:9
Arabas J, Biedrzycki R (2017) Improving evolutionary algorithms in a continuous domain by monitoring the population midpoint. IEEE Trans Evol Comput 21:807–812
Baharudin NN, Ayob SM (2018) Brushless DC motor speed control using single input fuzzy PI controller. Int J Power Electron Drive Syst 9(4):1952
Balamurugan K, Mahalakshmi R (2019) ANFIS—fractional order PID with inspired oppositional optimization based speed controller for brushless DC motor. Int J Wavel Multiresolut Inf Process. https://doi.org/10.1142/S0219691319410042
Blackwell TM, Bentley PJ (2002) Dynamic search with charged swarms. In: Proceedings of the genetic and evolutionary computation conference, pp 19–26
Dasari M, Reddy AS, Kumar MV (2019) GA-ANFIS PID compensated model reference adaptive control for BLDC motor. Int J Power Electron Drive Syst 10(1):265
Elkholy MM, El-Hay EA (2019) Efficient dynamic performance of brushless DC motor using soft computing approaches. Neural Comput Appl. https://doi.org/10.1007/s00521-019-04090-3
Godfrey AJ, Sankaranarayanan V (2018) A new electric braking system with energy regeneration for a BLDC motor driven electric vehicle. Eng Sci Technol Int J 21(4):704–713
Goldhirsch I, Sulem PL, Orszag SA (1987) Stability and Lyapunov stability of dynamical systems: a differential approach and a numerical method. Physica D 27(3):311–337
Hung CW, Lin CT, Liu CW, Yen JY (2007) A variable-sampling controller for brushless DC motor drives with low-resolution position sensors. IEEE Trans Industr Electron 54(5):2846–2852
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
Krstic M (2010) Lyapunov stability of linear predictor feedback for time-varying input delay. IEEE Trans Autom Control 55(2):554–559
Kumar AS, Sami K (2018) Speed control of brushless DC motor using line to line back EMF by ANFIS controller. Indian J Public Health Res Dev 9(10):821–825
Lad CK, Chudamani R (2018) Simple overlap angle control strategy for commutation torque ripple minimisation in BLDC motor drive. IET Electr Power Appl 12(6):797–807
Li T, Zhou J (2018) High-stability position-sensorless control method for brushless dc motors at low speed. IEEE Trans Power Electron 34(5):4895–4903
Lu S, Wang X (2017) A new methodology to estimate the rotating phase of a BLDC motor with its application in variable-speed bearing fault diagnosis. IEEE Trans Power Electron 33(4):3399–3410
Maharajan MP, Xavier SAE (2018) Design of speed control and reduction of torque ripple factor in BLDC motor using spider based controller. IEEE Trans Power Electron 34(8):7826–7837
Milivojevic N, Krishnamurthy M, Gurkaynak Y, Sathyan A, Lee YJ, Emadi A (2011) Stability analysis of FPGA-based control of brushless DC motors and generators using digital PWM technique. IEEE Trans Industr Electron 59(1):343–351
Murugan M, Jeyabharath R, Veena P (2013) Stability analysis of BLDC motor drive based on input shaping. Int J Eng Technol 5(5):4339–4348
Passino KM, Michel AN, Antsaklis PJ (1994) Lyapunov stability of a class of discrete event systems. IEEE Trans Autom Control 39(2):269–279
Pindoriya RM, Mishra AK, Rajpurohit BS, Kumar R (2018) FPGA based digital control technique for BLDC motor drive. In: 2018 IEEE Power & Energy Society General Meeting (PESGM), IEEE, pp 1–5
Pothorajoo S, Daniyal H (2018) Speed control of BLDC motor with seamless speed reversal capability using modified fuzzy gain scheduling. Jurnal Teknologi 80(2):161–170
Potnuru D, Tummala AS (2019) Grey wolf optimization-based improved closed-loop speed control for a BLDC motor drive. Smart intelligent computing and applications. Springer, Singapore, pp 145–152
Potnuru D, Mary KA, Babu CS (2019) Experimental implementation of Flower Pollination Algorithm for speed controller of a BLDC motor. AIN Shams Eng J 10(2):287–295. https://doi.org/10.1016/j.asej.2018.07.005
Praveen V, Pillai S (2016) Modeling and simulation of quadcopter using PID controller. Int J Control Theory Appl 9(15):7151–7158
Premkumar K, Manikandan BV (2014) Adaptive neuro-fuzzy inference system based speed controller for brushless dc motor. Neurocomputing 138:260–270
Premkumar K, Manikandan BV (2015) Fuzzy PID supervised online ANFIS based speed controller for brushless dc motor. Neurocomputing 157:76–90
Premkumar K, Manikandan BV (2018) Stability and performance analysis of ANFIS tuned PID based speed controller for brushless DC motor. Curr Signal Transduct Ther 13(1):19–30
Rad SM, Azizian MR (2018) Filterless and sensorless commutation method for BLDC motors. J Power Electron 18(4):1086–1098
Shamseldin M, Ghany MA, Mohamed AG (2018) Performance study of enhanced non-linear PID control applied on brushless DC motor. Int J Power Electron Drive Syst 9(2):536
Sreeram K (2018) Design of fuzzy logic controller for speed control of sensorless BLDC motor drive. In: 2018 international conference on control, power, communication and computing technologies (ICCPCCT), IEEE, pp 18–24
Vanchinathan K, Valluvan KR (2018) A metaheuristic optimization approach for tuning of fractional-order PID controller for speed control of sensorless BLDC motor. J Circuits Syst Comput 27(08):1850123
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
Wang H, Zai W (2015) Design and analysis of digital sensor pulse width modulation control scheme of brushless DC motor drive. Sensor Lett 13(2):138–142
Yoon YH, Kim JM (2018) Precision control of a sensorless PM BLDC motor using PLL control algorithm. Electr Eng 100(2):1097–1111
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Gobinath, S., Madheswaran, M. Deep perceptron neural network with fuzzy PID controller for speed control and stability analysis of BLDC motor. Soft Comput 24, 10161–10180 (2020). https://doi.org/10.1007/s00500-019-04532-z
- BLDC motor
- Fuzzy system
- Deep learning neural network
- Perceptron model
- PID controller
- Speed control
- Multi-swarm particle swarm optimization
- Lyapunov stability