Control of DC Motor Using Genetic Algorithm Based PID Controller

Tiwari, Shubham; Bhatt, Ankit; Unni, Arjun C.; Singh, Jai Govind; Ongsakul, Weerakorn

doi:10.23919/icue-gesd.2018.8635662

Cited by 20 publications

(13 citation statements)

References 3 publications

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“…Teknik pencarian ini meniru proses evolusi dimana sekumpulan calon solusi yang memiliki nilai ketahanan terbaik akan bereproduksi dan bermutasi untuk menghasilkan generasi selanjutnya. Proses ini akan terus berlanjut dimana tiap individu pada tiap generasi akan dilakukan evaluasi ketahanannya hingga diperoleh hasil yang paling optimal untuk sistem [11].…”

Section: Pendahuluanunclassified

Analisis Tuning Parameter PID Menggunakan Algoritma Genetika pada Pengontrolan Kecepatan Motor DC

Aliansyah¹,

Nurhayati²,

Jaya`³

et al. 2022

JTE

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Intisari— Pengaplikasian motor DC sering mengalami ketidakstabilan kecepatan putaran akibat adanya pembebanan saat dioperasikan. Untuk mengatasinya kontrol PID dapat diaplikasikan untuk menstabilkan kecepatan. Kontrol PID merupakan kombinasi dari pengendalian proportional, integral dan derivative. Untuk mendapatkan performa yang baik diperlukan penalaan (tuning) ketiga parameter ini menggunakan beberapa metode, salah satunya algoritma genetika. Algoritma genetika bekerja dengan membentuk populasi dari beberapa individu yang berpotensi menghasilkan solusi optimal dinilai dari nilai ketahanannya (fitness). Beberapa parameter digunakan dalam algoritma genetika seperti ukuran populasi, jumlah generasi, probabilitas crossover dan probabilitas mutasi. Penelitian ini bertujuan untuk menganalisis metode algoritma genetika untuk tuning parameter kontrol PID pada pengontrolan kecepatan putaran motor DC. Hasil pengujian diperoleh parameter algoritma genetika dengan ukuran populasi dan jumlah generasi sebesar 70, probabilitas crossover sebesar 0,9 dan probabilitas mutasi sebesar 0,4 dengan nilai fitness sebesar 6,261e+18. Parameter kontrol PID dengan nilai Kp = 9,4429; Ki = 19,3255 dan Kd = 0,45602 dan respon sistem dengan nilai rise time sebesar 0,1212 s, settling time sebesar 0,2562 s, overshoot sebesar 0,0366 % dan steady-state error sebesar 0,1739 %. Dengan parameter kontrol PID ini motor DC mampu bekerja mengikuti setpoint baik pada keadaan nilai beban yang tetap maupun berubah.

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Section: Pendahuluanunclassified

Analisis Tuning Parameter PID Menggunakan Algoritma Genetika pada Pengontrolan Kecepatan Motor DC

Aliansyah¹,

Nurhayati²,

Jaya`³

et al. 2022

JTE

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“…The setting of the proper parameter value of a controller indicates tuning it. The response of the system becomes unstable and poor, if improper value of gain parameters of a controller is used [15], [16]. Therefore, implementation of PID Tuning is critical to the controller tuning suitable to induce the favored response.…”

Section: Pid Controllermentioning

confidence: 99%

Implementing optimization of PID controller for DC motor speed control

Rashid

Hussain

2021

IJEECS

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The point of this paper presents an optimization technique which is flexible and quick tuning by using a genetic algorithm (GA) to obtain the optimum proportional-integral-derivative (PID) parameters for speed control of aseparately excited DC motor as a benchmark for performance analysis. The optimization method is used for searching for the proper value of PID parameters. The speed controller of DC motor using PID tuning method sincludes three types: MATALB PID tunner app., modified Ziegler-Nicholsmethod and genetic algorithm (GA). PID controller parameters (Kp, Ki and Kd) will be obtained by GA to produce optimal performance for the DC motor control system. Simulation results indicate that the tuning method of PID by using a genetic algorithm is shown to create the finest result in system performance such as settling time, rise time, percentage of overshoot and steady state error. The MATLAB/Simulink software is used to model and simulate the proposed DC motor controller system.

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“…A basic structure of GA-PID controller consists of a conventional controller, whose gain coefficients are auto tuned by the GA technique for a given plant. Hence a GA algorithm consists of three basic things, reproduction, crossover and mutation [12]…”

Section: Genetic Algorithm For Pid Tuningmentioning

confidence: 99%

Optimization of Proportional Integral Derivative Parameters of Brushless Direct Current Motor Using Genetic Algorithm

Adebayo

Aborisade

Adetayo³

2020

JERR

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Optimal performance of the Brushless Direct Current (BLDC) motor is to be realized using an efficient Proportional Integral Derivative (PID) controller. However, conventional tuning technique fails to perform satisfactorily under parameter variations, nonlinear conditions and time delay. Also using conventional technique to tune the parameters gain of the PID controller is a difficult task. To overcome these difficulties, modern heuristic optimization technique are required to optimally tune the Proportional, Integral, Derivative of the controller for optimal speed control of three phase BLDC motor. Thus, genetic algorithm (GA) based PID controller was used to achieve a high dynamic control performance. The Brushless DC Motor mathematical equation which describes the voltage and corresponding rotational angular speed and torque of the brushless DC motor was employed using electrical DC Machines theorem. The Genetic algorithm was further analyzed by adopting the three common performance indices i.e. Integral Time Absolute Error (ITAE), Integral Square Error (ISE) and Integral Absolute Error (IAE) in order to capture and compare the most suitable BLDC Motor speed and torque control characteristics. All simulations were done using MATLAB (R2018a). The simulation result showed that the system with GA-PID controller had the better system response when compared with the existing technique of ZN-PID controller.

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Control of DC Motor Using Genetic Algorithm Based PID Controller

Cited by 20 publications

References 3 publications

Analisis Tuning Parameter PID Menggunakan Algoritma Genetika pada Pengontrolan Kecepatan Motor DC

Analisis Tuning Parameter PID Menggunakan Algoritma Genetika pada Pengontrolan Kecepatan Motor DC

Implementing optimization of PID controller for DC motor speed control

Optimization of Proportional Integral Derivative Parameters of Brushless Direct Current Motor Using Genetic Algorithm

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