A Comparative Study of BLDC Motor Speed Control Using PI and ANN Regulator

Beladjine, Djamel Eddine; Boudana, D.; Moualdia, Abdelhafidh; Hallouz, Mohamed; Wira, Patrice

doi:10.1109/ssd52085.2021.9429474

Cited by 19 publications

(2 citation statements)

References 10 publications

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“…The frequency demand values, and armature current were controlled in such a way that the inverter-controlled shaft generated the steady-state properties of a constant step WT. Brushless DC motor and Permanent Magnet Synchronous motor (PMSM) are also utilized as prime movers for implementing WTE [18]. This paper provides to design a WE coupled to 2 kW PMSG for WECS.…”

Section: Possibilities To Control Reactive and Active Powermentioning

confidence: 99%

DC Motor Emulation of Wind Energy Conversion System

fiyad¹,

Mokhtar

Ali³

2021

International Journal of Telecommunications

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This paper presents a novel current control technique to control a separately excited DC motor in order to emulate the Wind Energy Conversion System (WECS) experimentally. This system is utilized to simulate the dynamic behaviors of WECS accurately in extension to a wide range of operating conditions. The suggested system is carried out in the laboratory using a set of separately excited DC motor, permeant magnet synchronous generator, Half-controlled rectifier bridge, firing circuit of the DC motor half-controlled rectifier bridge, current sensor, voltage sensor, speed sensor and a controller. Nonlinear model of WECS is derived and studied in addition to the operation of WECS at maximum mechanical power. DC motor current, voltage and speed are sensed using a set of predesigned sensors in order to feedback the system and record results. Experimental test results of the suggested wind energy conversion system are listed in static and dynamic cases to investigate the effectiveness for applying the suggested control technique to the developed system.

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Section: Possibilities To Control Reactive and Active Powermentioning

confidence: 99%

DC Motor Emulation of Wind Energy Conversion System

fiyad¹,

Mokhtar

Ali³

2021

International Journal of Telecommunications

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“…Neural network PI control is used in many modern control industrial systems [9][10][11], such as high-voltage DC power transmission [12], new energy photovoltaic power generation [13], speed control of motors, permanent magnet synchronous motor drives [14], and some robot control systems with nonlinear multiple intelligence [15]. In the application of neural network PI control, many innovations have been made in combination with the advantages of other control algorithms.…”

Section: Introductionmentioning

confidence: 99%

Current Control of EAST Fast Control Power Supply Based on BP Neuron Network Predictive PI

Chen

Huang

Wang

2022

Preprint

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The dynamic response speed of output current is one of the most important performance indexes of EAST fast control power supply. To further improve the response speed of output current, a predictive PI control algorithm of BP neural network is proposed to overcome the shortcomings of large computation and slow convergence in traditional BP neural network. A branch current prediction model of EAST fast control power supply is established. Based on linear PI control, the BP neural network structure is combined, and the current prediction model is used as the adaptive objective function. To simplify the calculation process of the processor and speed up the convergence rate, a piecewise linear activation function is adopted. The learning rate of BP neural network structure and the activation function factor of the output layer are adjusted in real time according to the error between the predicted current and the reference signal. The online predictive adaptive setting of PI control parameters is realized by the steepest gradient descent method. Simulation and experiments results show that the proposed control algorithm has faster dynamic response speed than the traditional PI control.

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