A data-driven sigmoid-based PI controller for buck-converter powered DC motor

Ahmad, Mohd Ashraf; Ismail, Raja Mohd Taufika Raja

doi:10.1109/iscaie.2017.8074954

Cited by 27 publications

(19 citation statements)

References 17 publications

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“…CSA optimization technique could be applied to many controllers to find the best gain values to minimize the error, have optimum system response, and maximize the output power. Tunning of the PI controller [27], PID controller [28,29], and FOPID controller [30] has been introduced in many applications. In this work, CSA would be applied to tune the gains of the PI and FOPID controllers for the proposed DFIG wind energy conversion system.…”

Section: Optimization Techniquementioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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Wind Energy has received great attention in this century. It influences the new power systems, adding new challenges to the power system expansion problem. Nowadays, double feed induction generator (DFIG) wind turbines are used majorly in wind farms, due to their advantages over other types. Therefore, the analysis of the system using this type has become very important. In this paper, a wind turbine modelling was introduced with suggested controllers, in order to enhance the system response, with respect to both pitch control and maximum output power. Cuckoo search algorithm (CSA), a meta-heuristic optimization technique, was implemented to determine the gains of a proportional-integral (PI) controller and fractional order proportional-integral-derivative (FOPID) controller to optimize the system, which considered three control loops: pitch, rotor-side converter, and grid-side converter control loop. Simulation results were determined using MATLAB/Simulink. The comparative analysis of the results showed that the PI Controller gave the simplest and the best response in case of the pitch and rotor-side control loops while the FOPID was the best when applied to the grid-side control loop. Based on the results and discussion, a suggestion of using a compination of each controller was introduced.

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Section: Optimization Techniquementioning

confidence: 99%

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Al‐Gabalawy

Hosny

Hussien

2020

IJECE

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“…This system, in general, is composed of three stages, namely, a DC/DC Buck converter, a complete bridge inverter, and a DC motor. Unlike the arrangements presented in [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], such configuration allows the bidirectional driving of the motor shaft. The system average model presented in Figure 1, which was deduced and experimentally validated in [35], is given by…”

Section: Systemmentioning

confidence: 99%

“…Another solution was proposed by Nizami et al in [24], where a neuro-adaptive backstepping control for the angular velocity tracking was developed for the aforementioned system. Other interesting works reported in the last months, on tracking control design for the DC/DC Buck-DC motor system, are [25][26][27][28][29]. Additional works where other topologies of DC/DC power converters driven DC motors are [30][31][32] for the Boost converter, [33] for the Buck-Boost converter, and [34] for the Sepic and Cuk converters.…”

Section: Introductionmentioning

confidence: 97%

Bidirectional Tracking Robust Controls for a DC/DC Buck Converter-DC Motor System

et al. 2018

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Two differential flatness-based bidirectional tracking robust controls for a DC/DC Buck converter-DC motor system are designed. To achieve such a bidirectional tracking, an inverter is used in the system. First control considers the complete dynamics of the system, that is, it considers the DC/DC Buck converter-inverter-DC motor connection as a whole. Whereas the second separates the dynamics of the Buck converter from the one of the inverter-DC motor, so that a hierarchical controller is generated. The experimental implementation of both controls is performed via MATLAB-Simulink and a DS1104 board in a built prototype of the DC/DC Buck converter-inverter-DC motor connection. Controls show a good performance even when system parameters are subjected to abrupt uncertainties. Thus, robustness of such controls is verified.

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“…A considerable amount of literature has been published on control of buck-converter driven DC motor. For instance, H-infinity controller [6], data driven sigmoid-based PI controller [7], PI-fuzzy logic controller [8] and adaptive controller based on Lyapunov function [9].…”

Section: Introductionmentioning

confidence: 99%

PSO Fined-tuned Model-Free PID Controller with Derivative Filter for Buck-Converter Driven DC Motor

2019

IJRTE

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Traditionally, the PID controller parameters are tuned heuristically based on time response behavior of the system. This method is tiresome job and can cause undesirable system response. Therefore, this research suggests the tuning method of a model-free PID controller with derivative filter (PIDF) by implementing Particle Swarm Optimization (PSO). This tuning method is applied to buck-converter driven DC motor control. The speed of DC motor is controlled by PIDF controller. The parameters of PIDF controller are fine-tuned by implementing PSO algorithms. The fitness functions of the algorithm are evaluated based on Sum Square Error (SSE) and Sum Absolute Error (SAE). The state-space representation of buck-converter/DC motor is considered to confirm the design of the control method. The results of the proposed tuning method are compared with PI controller and PIDF controller tuned by PID Tuner Simulink. The time response specifications of angular velocity, armature current and duty cycle input energy are considered as a control scheme performance. Finally, the suggested tuning technique promises a very minimum duty cycle energy and a fast input tracking of DC motor angular velocity.

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A data-driven sigmoid-based PI controller for buck-converter powered DC motor

Cited by 27 publications

References 17 publications

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Cuckoo search algorithm based for tunning both PI and FOPID controllers for the DFIG-Wind energy conversion system

Bidirectional Tracking Robust Controls for a DC/DC Buck Converter-DC Motor System

PSO Fined-tuned Model-Free PID Controller with Derivative Filter for Buck-Converter Driven DC Motor

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