Optimization of PID Parameters Using Ant Colony Algorithm for Position Control of DC Motor

Simsek, Enes Can; Kose, Ali; Şahin, Murat; Irmak, Erdal

doi:10.1109/icrera47325.2019.8997108

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Previous studies proposed various control methods for DC motors, but some of their evaluation was limited to simulation systems [54], [55]. Consequently, the need for further validation remains crucial to ascertain the efficacy of these methods in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Jallad,

Badran

2024

Bulletin EEI

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This paper aims to design an accurate angular position control for DC motors using a proportional integral derivative (PID) controller. The estimated DC motor parameters have been calculated using the parameter estimator toolbox in MATLAB, Arduino Mega 2560 and speed sensor to build an accurate model in MATLAB. The optimized PID coefficients are found for the DC motor model using the firefly algorithm (FA), which aims to make the actual angle match the desired value without overshooting and oscillations. The PIC16F877A microcontroller was used to implement the code based on optimized PID coefficients found in MATLAB/Simulink to generate the suitable pulse width modulation (PWM) output. In this work, step input was tested to analyze the characteristics of the system response in terms of rise time, settling time and overshoot. It was found that the controller output response curve which is produced from FA-based-PID reached the desired position without overshoot and any oscillations. The findings established that closed-loop control of any system using a system identification toolbox and optimized PID technique can be applied in real applications using low-cost controllers and sensors such as PIC16F877A microcontroller and analog rotary position sensor, respectively.

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Section: Introductionmentioning

confidence: 99%

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Jallad,

Badran

2024

Bulletin EEI

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“…Several studies on DC motor control are divided into two concepts, namely: conventional methods and artificial intelligence methods. several artificial intelligence concepts for controlling such as: Whale optimization algorithm [8], [9], Harris Hawks optimization algorithm [10], flower pollination algorithm [11], firefly algorithm [12], ant colony algorithm [13], [14], and neural network [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Aribowo

Suprianto

Joko

2021

IJPEDS

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A DC motor is applied to delicate speed and position in the industry. The stability and productivity of a system are keys for tuning of a DC motor speed. Stabilized speed is influenced by load sway and environmental factors. In this paper, a comparison study in diverse techniques to tune the speed of the DC motor with parameter uncertainties is showed. The research has discussed the application of the feed-forward neural network (FFNN) which is enhanced by a sine tree-seed algorithm (STSA). STSA is a hybrid method of the tree-seed algorithm (TSA) and Sine Cosine algorithm. The STSA method is aimed to improve TSA performance based on the sine cosine algorithm (SCA) method. A feed-forward neural network (FFNN) is popular and capable of nonlinear issues. The focus of the research is on the achievement speed of DC motor. In addition, the proposed method will be compared with proportional integral derivative (PID), FFNN, marine predator algorithm-feed-forward neural network (MPA-NN) and atom search algorithm-feed-forward neural network (ASO-NN). The performance of the speed from the proposed method has the best result. The settling time value of the proposed method is more stable than the PID method. The ITAE value of the STSA-NN method was 31.3% better than the PID method. Meanwhile, the ITSE value is 29.2% better than the PID method.

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“…The development of artificial intelligence theory continues to increase the impact on the DC motor control system. Several methods used in DC motor control, namely particle swarm optimization [5][6], Ant Colony optimization [7][8], teaching-learning-based optimization [9][10], Jaya optimization algorithm [11][12], Harris Hawks Optimization [13][14], Flower pollination algorithm [15][16], Fuzzy [17][18], and Artificial Neural Network [19] [20].…”

Section: Introductionmentioning

confidence: 99%

Atom Search Optimization – Neural Network for Driving Dc Motor

Aribowo

Joko

Isnur

et al. 2021

Sinergi

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DC motor applications are very widely used because DC motors are very suitable for applications, especially control. Thus, a proper DC motor controller design is required. DC motor speed control is very important to maintain the stability of motor operation. A recent type of metaheuristic algorithm that mimics the motion of atoms is introduced. Atom search optimization (ASO) is a mathematical model and duplicates the behavior of atoms in nature. Atoms intercommunicate with each other via the delivering contact force in the form of the Lennard-Jones potential and the constraint force produced from the potential bond length. The algorithm is simple and easy to be applied. In this study, the atomic search optimization (ASO) algorithm is proposed as a speed controller for the control dc motor. First, the ASO proposed by the algorithm is applied for the optimization of the neural network. Second, the ASO-NN proposal was the result compared to other algorithms. This paper compares the performance of two different control techniques applied to DC motors, namely the ASO-NN and proportional integral derivative (PID) methods. The results show that the proposed method has effectiveness. The calculation of the proposed ASO-NN control shows the best performance in the settling time. The ASO-NN method has the capability of settling time 0.04 seconds faster than the PID method.

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Optimization of PID Parameters Using Ant Colony Algorithm for Position Control of DC Motor

Cited by 4 publications

References 20 publications

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Firefly algorithm tuning of PID position control of DC motor using parameter estimator toolbox

Improving neural network using a sine tree-seed algorithm for tuning motor DC

Atom Search Optimization – Neural Network for Driving Dc Motor

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