Design of dynamic states and digital speed control of BLDC motor

Anitha, E. A. Mary; Vairaprakash, P.

doi:10.1109/iccpct.2016.7530313

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…Furthermore, back EMF is produced in stator windings whenever the rotating magnetic field intermingles with stator pole. Three phase star connected model of BLDC motor is given by [17].…”

Section: Motor Modelingmentioning

confidence: 99%

BLDC Multi-Motor Speed Synchronization by Leader Following Multi-Agent and Hybrid Control

Masroor¹,

Chen²,

Aamir³

et al. 2016

IJHIT

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In this article, a unique and innovative design for addressing the speed synchronization problem in a multi-motor system using Brush less DC (BLDC), motors is presented by utilizing motor back emf equation and rotor position commonly called sensor less technique. In the proposed method, the system is modelled as a consensus problem of leader following multi-agent system (MAS), and a hybrid controller is designed by using Model Reference Adaptive Control (MRAC) along with Variable Structure (VS) control technique of Sliding Mode Control (SMC), herein called Variable Structure Model Reference Adaptive Control (VS-MRAC). The consensus algorithm of MAS (reformed w.r.t motor speed data), is fused with the hybrid controller to reach consensus on speed regulated by proposed hybrid controller. The stability of a system is endorse by designing a Lyapunov function. The efficiency of the proposed methodology is proven by simulations performed in MATLAB and the acquired results validate the success of proposed design methodology.

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“…Furthermore, back EMF is produced in stator windings whenever the rotating magnetic field intermingles with stator pole. Three phase star connected model of BLDC motor is given by [17].…”

Section: Motor Modelingmentioning

confidence: 99%

BLDC Multi-Motor Speed Synchronization by Leader Following Multi-Agent and Hybrid Control

Masroor¹,

Chen²,

Aamir³

et al. 2016

IJHIT

View full text Add to dashboard Cite

show abstract

“…The desired operating points are determined by the clinically intended uses and surgeon experiences. In direct current (DC) motor and brushless DC (BLDC) motor controls, the PID/PI controller and the fuzzy logic based on self-tuning methods and optimisation methods [12][13][14][15][16][17] are used to determine the optimal control parameters to minimise overshoot, enhance setting time, and improve transient responses, such as particle swarm optimisation-PID, fuzzy logic-PID, evolutionary programmingfuzzy control, and genetic algorithm-PI [16,17]. However, selftuning and optimisation methods are used only to tune the optimal control parameters without automatically estimating the desired operating points under various conditions.…”

Section: Introductionmentioning

confidence: 99%

Predictable capability control scheme for oxygen‐exchange blood flow regulation in an extracorporeal membrane oxygenation system

Kan

Chen

Lin³

et al. 2017

IET syst. biol.

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Extracorporeal membrane oxygenation system is used for rescue treatment strategies for temporary cardiopulmonary function support to facilitate adequately oxygenated blood to return into the systemic and pulmonary circulation systems. Therefore, a servo flow regulator is used to adjust the roller motor speed, while support blood flow can match the sweep gas flow (GF) in a membrane oxygenator. A generalised regression neural network is designed as an estimator to automatically estimate the desired roller pump speed and control parameters. Then, the proportional-integral-derivative controller with tuning control parameters showed good performance to achieve speed regulation and speed tracking in the desired operating point. Given the pressure of carbon dioxide, drainage blood flow, and cannula size, the proposed predictable capability control scheme can be validated to meet the intended uses in clinical applications.

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Particular aspects concerning the design of a brushless DC electric motor driving a mini-scooter

Marian-Stefan

Presura

Vlad

et al. 2016

2016 International Conference on Applied and Theoretical Electricity (ICATE)

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Design of dynamic states and digital speed control of BLDC motor

Cited by 4 publications

References 2 publications

BLDC Multi-Motor Speed Synchronization by Leader Following Multi-Agent and Hybrid Control

BLDC Multi-Motor Speed Synchronization by Leader Following Multi-Agent and Hybrid Control

Predictable capability control scheme for oxygen‐exchange blood flow regulation in an extracorporeal membrane oxygenation system

Particular aspects concerning the design of a brushless DC electric motor driving a mini-scooter

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