Speed Control of Brushless DC Motor based on Fractional Order PID Controller

Saleh, Ameer L.; Obed, Adel A.

doi:10.5120/16579-6269

Cited by 14 publications

(11 citation statements)

References 5 publications

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“…FOPID controller (P I λ D μ ) is an expansion of a conventional PID controller, where derivation and integration order has fractional values. The modified PID (P I λ D μ ) controller with a new integral λ and derivative μ orders makes the system less sensitive and more flexible . The fractional order of the PI λ D μ controller is described by the differential equation as

normalu ((), t) = K_{normalp} normale ((), t) + K_{i} D^{- normalλ} normale ((), t) + K_{d} D^{μ} normale ((), t) .

While the controller transfer function can be represented by Laplace transform as follow:

normalG ((), s) = K_{p} + K_{i} S^{- normalλ} + K_{d} S^{μ} .

The generalized block diagram of PI λ D μ controller is indicated in Figure .…”

Section: Control Designmentioning

confidence: 99%

See 1 more Smart Citation

Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor

Saleh

Obed

Al‐Yasir

et al. 2019

Int Trans Electr Energ Syst

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Summary Recently, a new scheme of conventional PID (Proportional Integral Derivative) controller has been utilized in different electrical drives for speed control. This paper presents an optimal P IλDμ controller with 2DOF and anti‐windup techniques based on PSO optimization to produce the proposed controller which is exploited in order to control the velocity control of LIM. A three‐phase inverter with SVPWM technique that is depended on approach of IFOC method is exploited to obtain the necessary output voltage supplied to the main windings of the LIM in order to control the velocity with minimum harmonics and good characteristics. The end effect is taken into account in dq axis equivalent circuit. Computed investigations show that the proposed controller is efficient in improving motor performance.

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normalu ((), t) = K_{normalp} normale ((), t) + K_{i} D^{- normalλ} normale ((), t) + K_{d} D^{μ} normale ((), t) .

While the controller transfer function can be represented by Laplace transform as follow:

normalG ((), s) = K_{p} + K_{i} S^{- normalλ} + K_{d} S^{μ} .

The generalized block diagram of PI λ D μ controller is indicated in Figure .…”

Section: Control Designmentioning

confidence: 99%

“…The modified PID (P I λ D μ ) controller with a new integral λ and derivative μ orders makes the system less sensitive and more flexible. 12,13 The fractional order of the PI λ D μ controller is described by the differential equation as…”

Section: Fractional Order Pid (Proportional Integral Derivative) (Pmentioning

confidence: 99%

Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor

Saleh

Obed

Al‐Yasir

et al. 2019

Int Trans Electr Energ Syst

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“…Equations (4b), (7b), and (8b) are an alternative form of Equations (4a), (7a), and (8a) in terms of the fractional order PI controller. The general block diagram and graphical representation of fractional order PI controller is as follows [14].…”

Section: Outer Voltage Control Loop and Inner Current Control Loopmentioning

confidence: 99%

A Novel Plant Propagation-Based Cascaded Fractional Order PI Controller for Optimal Operation of Grid-Connected Single-Stage Three-Phase Solar Photovoltaic System

Zamee

Won

2019

Applied Sciences

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Grid-connected photovoltaic (PV) inverters are gaining attention all over the world. The optimal controller setting is key to the successful operation of a grid-connected PV system. In this paper, a novel plant propagation algorithm-based fractional order proportional-integrator (FOPI) controller for cascaded DC link voltage and inner current control of a grid-connected PV controller has been proposed, which outperforms particle swarm optimization-based PI and elephant herding optimization-based FOPI in terms of multicriteria-based analysis. The performance of the proposed controller also has been measured in terms of total harmonic distortion to maintain the appropriate power quality. Also, the proposed controllers were tested under various solar irradiance and voltage sag conditions to show the effectiveness and robustness of the controllers. The whole system is developed in OPAL-RT using MATLAB/Simulink and RT-LAB as a machine-in-loop (MIL) system to validate the performance in real time.

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“…The mathematical model and the Simulink model of BLDC motor to control the speed of a BLDC by using conventional methods are introduced in Refs. [2][3][4][5][6]. The DC-DC converter technique is utilized to control the speed of the motor [5,6].…”

Section: Introductionmentioning

confidence: 99%

Wavelet Neural Networks for Speed Control of BLDC Motor

Saleh¹,

Obed²,

Qasim³

et al. 2021

Automation and Control

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In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared.

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Speed Control of Brushless DC Motor based on Fractional Order PID Controller

Cited by 14 publications

References 5 publications

Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor

Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor

A Novel Plant Propagation-Based Cascaded Fractional Order PI Controller for Optimal Operation of Grid-Connected Single-Stage Three-Phase Solar Photovoltaic System

Wavelet Neural Networks for Speed Control of BLDC Motor

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Speed Control of Brushless DC Motor based on Fractional Order PID Controller

Cited by 14 publications

References 5 publications

Anti‐windup scheme based on 2DOF‐PIλDμcontroller for velocity tracking of linear induction motor

Anti‐windup scheme based on 2DOF‐PIλDμcontroller for velocity tracking of linear induction motor

A Novel Plant Propagation-Based Cascaded Fractional Order PI Controller for Optimal Operation of Grid-Connected Single-Stage Three-Phase Solar Photovoltaic System

Wavelet Neural Networks for Speed Control of BLDC Motor

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Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor

Anti‐windup scheme based on 2DOF‐PI^λD^μcontroller for velocity tracking of linear induction motor