Speed control of dc motor using artificial bee colony optimization technique

Mishra, Akhilesh Kumar; Tiwari, Vineet Kumar; Kumar, Rohit; Verma, Tushar

doi:10.1109/care.2013.6733772

Cited by 33 publications

(27 citation statements)

References 1 publication

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“…Direct current machines has a widely used in many industrial applications such as motive applications, rolling mills, paper mills, mine winders, hoists, machine tools, traction, printing presses, textile mills, excavators and cranes. Fractional horsepower DC motors are widely used as servomotors for positioning and tracking [1]. So that it is very important to design a suitable controller to adjust the speed of the motor according to the required reference speed demands by the user.…”

Section: Introductionmentioning

confidence: 99%

“…Many control strategies used for speed adjustment of DC motors such as PI, PID, fuzzy, and ANFIS. In [1] the researchers designed a speed controller of DC motor using PID controllers but uses the bioinspired optimization technique of Artificial Bee Colony Optimization (ABC) to select the parameters of the controller. Fuzzy Neural Model Reference controller has used in [2] to control the speed of SEDCM, the results obtained presented a good performance and robust response in load variations, this work uses Fuzzy Neural Model Reference controller and MRAC method, while our present work uses fuzzy and ANFIS techniques to implement the speed control for the DC motor.…”

Section: Introductionmentioning

confidence: 99%

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Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Mahmood¹

2018

ETJ

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In this work, a closed loop control system has designed to control the speed of separately excited direct current motor (SEDCM) using Fuzzy (Mamdani and Sugeno) and an Adaptive Neuro-Fuzzy techniques (ANFIS). The action of these control techniques is to produce the reference armature current that has fed to the hysterics current controller (HCC) that produces the required gating signal to a Buck chopper .Different load conditions has been applied to the motor to obtain many mode of operation, the speed held constant at the required references values using both fuzzy (Mamdani-type and Sugenotype) and ANFIS techniques. The results has collected and compered with a classical PID controller using MATLAB/Simulink. Step response for the speed has drawn and the control parameters for this response have evaluated. According to the results, the Mamdani fuzzy controller technique is better than as compared with the other controllers. There are many applications for this plant such as production process that need to fill or Packaging any product or used in the autopilot channels. The new goal for this proposed system is to get robust speed controllers that track the speed at any mode of operation using three artificial intelligent techniques.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Mahmood¹

2018

ETJ

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“…Since its advent [2], ABC and its variants have often successfully employed to wide and diverse range of problems, such as numeric optimization [3], discrete optimization [4], multi-objective optimization [5], industrial process control [6], structural design [7], design of digital IIR filters [8], PID controller [9], machine learning [10] and so on [11].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems

ShabnamHasan¹

2015

IJAIS

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A major problem with the Artificial Bee Colony (ABC) algorithm is its premature convergence to the locally optimal points of the search space, which often originates from the lack of explorative search capability of its mutation operator. This paper introduces ABC with Adaptive Mutation Rate (ABC-AMR), a novel algorithm that modifies the basic mutation operation of the original ABC algorithm in an explorative way. The novelty of the proposed algorithm lies in an adaptive mutation strategy that enables ABC-AMR to automatically adjust the mutation rate, separately for each candidate solution of the bee population, in order to customize the degree of explorations and exploitations around each candidate solution, while the original ABC algorithm employs a naïve fixed mutation rate. Besides, a few more explorative schemes and parameter values are employed by ABC-AMR to assist the adaptive mutation procedure. ABC-AMR is evaluated on several benchmark numerical optimization problems and results are compared with the basic ABC algorithm. Results show that ABC-AMR can perform better optimization than the original ABC algorithm on some of the benchmark problems.

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“…Since their advent, they have been widely and successfully employed to complex and diverse problems from the fields of science and engineering, such as numeric function optimization [1]- [3], discrete optimization [4], multi-objective optimization [5], industrial process control [6], structural design [7], design of digital IIR filters [8], PID controller [9], machine learning [10] and so on [11]. In comparison to other greedy and local search based algorithms, both EAs and SIAs are more resilient against premature convergence and fitness stagnation, because the population/swarm of candidate solutions can maintain some amount of diversity that is necessary to continue search space explorations avoiding the locally optimal points.…”

Section: Introductionmentioning

confidence: 99%

Comparison between Fast Evolutionary Programming and Artificial Bee Colony Algorithm on Numeric Function Optimization Problems

2015

IJSR

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Abstract:The Evolutionary and Swarm Intelligence algorithms are two recently introduced population based meta-heuristic algorithms that have been successfully employed to numerous scientific and engineering problems. In this paper, we have selected two recent and representative algorithms -one from the evolutionary algorithm family, the other from the swarm intelligence family and compared their performance on high dimensional function optimization problems. The evolutionary algorithm that isselected in this paper is the Fast Evolutionary Programming (FEP) which uses Cauchy mutation to improve over the basic Gaussian mutation scheme. The swarm intelligence algorithm that is selected is the Artificial Bee Colony (ABC) algorithm which has been introduced recently and found to be very effective on many continuous optimization problems. This paper compares the performance of these two algorithms on a common set of benchmark problems in order to achieve a better understanding of their algorithmic nature and characteristics. The experimental results show that the performance of ABC is usually better than FEP, especially on complex multimodal functions, because ABC can deal with the problems of premature convergence and fitness stagnation more effectively than FEP.

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Speed control of dc motor using artificial bee colony optimization technique

Cited by 33 publications

References 1 publication

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Speed Reference Tracking for Separately Excited DC Motor Based ANFIS and Hysteresis Current Control Techniques

Adaptive Mutation Rate for the Artificial Bee Colony Algorithm: A Case Study on Benchmark Continuous Optimization Problems

Comparison between Fast Evolutionary Programming and Artificial Bee Colony Algorithm on Numeric Function Optimization Problems

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