DC Motor Control Using PID Controller Based on Improved Ant Colony Algorithm

Ibrahim, Hussein; Mahmoud, Ahmed A. Hakim

doi:10.15866/ireaco.v7i1.1283

Cited by 8 publications

(6 citation statements)

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“…Inner and outer PI controllers of VSC -MTDC are optimized by PSO algorithm explained in reference [15]. The resulting parameters of PI inner current controller and outer controller obtained using ACO [35] in subsection IV-A, classical methods [33] in subsection IV-B and PSO are presented in Table 2.…”

Section: Comparison With Existing Methodsmentioning

confidence: 99%

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

et al. 2021

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Voltage source converter based multi -terminal DC grids (VSC -MTDC) are widely used for integration of renewable resources. Control of these grids requires vector control method, which includes inner and outer current controllers, tuning of these controllers is very important. Conventionally used tuning methods consider approximated linear model to tune the proportional -integral (PI) parameters of voltage source converter (VSC) which fails to produce optimum disturbance rejection results. In this research an Ant Colony Optimization (ACO) technique is used to get the optimum parameters of inner and outer power control layers for multi -terminal DC system. ACO is applied simultaneously on inner and outer control layers and results are compared with classical tuning method and well established meta -herustic technique, Particle Swarm Optimization (PSO) using MATLAB software. Dynamic models of VSC -MTDC are developed in PSCAD/EMTDC to exalt the qualities of the presented tuning method. In this paper, a four terminal VSC -MTDC has been used under different sequence of events of disturbances e.g. load change on AC side, active power reference change for inverter and rectifier mode and disconnection of one terminal to evaluate the robustness of ACO algorithm with respect to classical method. The proposed tuning method gives superior results under different disturbance compared to conventional method.

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Section: Comparison With Existing Methodsmentioning

confidence: 99%

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

et al. 2021

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“…The values of this parameters are adopted from the DC motor Pittman Model 9234S004 which are provided in Table 1 (Ibrahim & Mahmoud, 2014).…”

Section: Model Of DC Motormentioning

confidence: 99%

“…A wide range of metaheuristic algorithms have being developed over the last three decades, and many metaheuristics are increasingly becoming popular (Yang, 2011). These metaheuristics techniques have been applied on control systems, and the results obtained shows significant improvement over classical techniques (Agnihotri et al, 2018;Ekinci et al, 2019;Ibrahim & Mahmoud, 2014;Meena & Chahar, 2017;Obeng & Karam, 2018;Sabir & Khan, 2014;Solihin et al, 2011;Taki El-Deen et al, 2015). Abdulameer et al, (2016) designed a PID controller for DC speed motor using the Ziegler-Nichols and Chien-Hrones-Reswick method.…”

Section: Introductionmentioning

confidence: 99%

“…The NM-SA method is more robust as compared to NM-GA and NM-PSO, GA, PSO and SA technique. Ibrahim & Mahmoud, (2014) proposed an improved ant colony optimization (ACO) for optimal tuning of PID parameters. The results obtained shows that the ACO has self-adaptation and positive feedback process to escape the trap of local minimum and achieve the best solution.…”

Section: Introductionmentioning

confidence: 99%

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Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Abba¹,

Karataev²,

Thomas³

et al. 2022

FUOYEJET

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This paper presents an optimal PID controller for speed control of DC motor using Smell Agent Optimization (SAO) Algorithm. DC Motor is an energy converter which transforms electrical energy into mechanical energy used in several industrial applications. A SAO based PID is proposed for optimal tuning of the parameters of the controller to improve speed control of the motor. The SAO uses the phenomenon of smell perception and the intuitive trailing behavior of an agent to identify the source of smell. The integral of time multiplied absolute error (ITAE) is taken as the objective function for obtaining the parameters of the PID controller. It has been observed SAO-PID controller outperforms the GA based PID controller with less settling time, rise time and overshoot. Keywords— DC motor, PID controller, rising time, settling time, smell agent optimization algorithm

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“…In 2014 Ibrahim et. Al., [11] introduce Ant Colony Optimization (ACO) tuning PID controller to control separated excited dc motor speed. In 2014 Ibrahim et.…”

Section: Introductionmentioning

confidence: 99%

Speed Control of PMDCM Based GA and DS Techniques

Abed

Saleh

Hameed

2018

IJPEDS

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<span lang="EN-US">Permanent magnet direct current motors (PMDCM) are widely used in various applications such as space technologies, personal computers, medical, military, robotics, electrical vehicles, etc. In this paper, the mathematical model of PMDCM is designed and simulated using MATLAB software. The PMDCM speed is controlled using rate feedback controller due to its ability of improving system damping. To improve the controller performance, it’s parameters are tuned using genetic algorithm (GA) and direct search (DS) techniques. The tuning process based on different performance criteria. The most four common performance criteria used in this paper are JIAE (Integral of Absolute Error), JISE (Integral of Square Error), JITAE (Integral of Time-Weighted Absolute Error), and JITSE (Integral of Time-Weighted Square Error). The results obtained from these evolutionary techniques are compared. The results show an obvious improvement in system performance including enhancing the transient and steady state of PMDCM speed responses for all performance criteria.</span>

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DC Motor Control Using PID Controller Based on Improved Ant Colony Algorithm

Cited by 8 publications

References 1 publication

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

Controller Parameters Optimization for Multi-Terminal DC Power System Using Ant Colony Optimization

Optimal PID Controller Tuning for DC Motor Speed Control Using Smell Agent Optimization Algorithm

Speed Control of PMDCM Based GA and DS Techniques

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