Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms

Katal, Nitish; Singh, Sanjay Kumar

doi:10.5120/8860-2822

Cited by 5 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Cascaded DC motor controllers are optimized using multi-objective optimization evolutionary algorithms (MOEAs) selecting different solution on the Pareto-set in [29]. Optimal PID controllers are developed by NSGA-II algorithm, and compared to the traditional Ziegler Nichols methods in [30] and [23].…”

Section: Controller Multi-objective Optimizationmentioning

confidence: 99%

Multi-Scenario Multi-Objective Optimization of a Fuzzy Motor Controller for the Szabad(ka)-II Hexapod Robot

Kecskés¹,

Ódry²

2018

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

The aim of this research was to develop a robust motor controller for the Szabad(ka)-II hexapod robot. A Fuzzy-PI controller that utilized a lookup table was chosen because of its reported promising performance and its ability to be embedded in the microcontrollers of the robot. The variables of the controller were defined by a particle swarm optimization method to minimize the five quality objectives related to the walking of the robot. The preferences of the five objectives were successfully expressed by a biasedweighted geometric mean utility function. The resulting optimal solutions were significantly altered by changing the bias and exponential weights of preferences. Therefore, we checked the robustness of the solution against the controller's variables as a secondary objective.

show abstract

Section: Controller Multi-objective Optimizationmentioning

confidence: 99%

Multi-Scenario Multi-Objective Optimization of a Fuzzy Motor Controller for the Szabad(ka)-II Hexapod Robot

Kecskés¹,

Ódry²

2018

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

show abstract

“…Beberapa metode optimasi berbasis metode konvensional maupun metode cerdas telah banyak digunakan untuk mengoptimasi parameter PID pada motor listrik, diantaranya Artificial Bee Colony [3], Evolutionary Algorithm [4,5,9], Particle Swarm Optimization [6], Bio-Inspired Algorithm [7], Bacterial Foraging [8], Tabu Search [10], Fuzzy Logic [11,12], Cuckoo Search [13] dan Flower [14]. Terdapat dua langkah kunci di algoritma ini, yaitu penyerbukan global dan penyerbukan lokal.…”

Section: Pendahuluanunclassified

Modifikasi Desain PID Controller Pada Permanent Magnet Synchronous Motor Dengan Flower Pollination Algorithm

Djalal

Ali

Imran

et al. 2017

JTE

View full text Add to dashboard Cite

The use of PID (Proportional-Integral-Derivative) controllers on a synchronous motor is very widely used, because of its simple, robust structure that is robust and easy to use. The use of PID controllers requires proper parameter setting for optimal performance on the motor. The most commonly used solution is the trial-error method, to determine the proper parameters for PID, but the results do not make the PID controller optimal. Lately there has been a lot of research to optimize PID controller, wrong with smart method. For this purpose, we will use Flower Pollination Algorithm (FPA) optimization method to optimize and determine the exact parameters of PID. FPA is one method that is being adapted from the process of plant pollination, so this concept is adapted and applied to be. From the results that have the FPA method can be well tuned parameters PID, so that the resulting overshoot faster and settling time is very fast. Optimization results Kp 0.9441, Ki 0.9311, Kd 0.0840. In this study will discuss uncontrolled motors, with PID trial-error controller, PID-PSO and PID-FPA.

show abstract

“…Optimization [4,8], Evolutionary Algorithm [7,11], Bio-Inspired Algorithm [9], Tabu Search [12], Bacterial Foraging [10], Fuzzy Logic [13,14], Cuckoo Search [15], dan Flower Algorithm [16]. Dari beberapa penelitian yang telah dilakukan menunjukkan pengembangan metode penalaan parameter PID dengan menggunakan metode kecerdasan buatan.…”

Section: Jurnal Nasional Teknik Elektro 77unclassified

Aplikasi Metode Cerdas untuk Optimasi Controller PID Motor DC Berbasis Firefly Algorithm

Djalal

Nurohmah

Imran

et al. 2017

JNTE

View full text Add to dashboard Cite

Pengontrolan kecepatan motor dc merupakan hal yang sangat penting untuk menjaga stabilitas operasi motor. Salah satu metode pengontrolan yang sering digunakan adalah kontroler proportional integral derivative (PID). Agar dapat beroperasi dengan optimal, kontroler PID membutuhkan penalaan parameter yang tepat. Salah satu permasalahan dalam penggunaan kontroler PID adalah penentuan parameter PID yang tepat. Dalam penentuan parameter kontroler PID selama ini masih dilakukan secara konvensional, sehingga kinerja PID menjadi tidak optimal. Untuk itu pada penelitian ini akan diusulkan salah satu metode penalaan parameter PID dengan menggunakan metode cerdas berbasis Firefly Algorithm (FA), untuk mengoptimasi dan menentukan parameter yang tepat dari PID. FA adalah salah satu metode cerdas yang terinspirasi dari perilaku firefly yang bergerak dimalam hari dengan kebiasaan berkedip, yang kemudian diadaptasi dan diterapkan menjadi algoritma cerdas untuk menyelesaikan masalah optimasi. Dari hasil yang diperoleh metode Firefly dapat dengan baik menala parameter PID, sehingga overshoot yang dihasilkan tidak ada dan settling time sangat cepat. Sebagai pembanding, pada penelitian ini juga akan dibahas penggunaan metode cerdas berbasis Bee Colony dan Cuckoo Search.

show abstract

Optimal Tuning of PID Controller for DC Motor using Bio-Inspired Algorithms

Cited by 5 publications

References 10 publications

Multi-Scenario Multi-Objective Optimization of a Fuzzy Motor Controller for the Szabad(ka)-II Hexapod Robot

Multi-Scenario Multi-Objective Optimization of a Fuzzy Motor Controller for the Szabad(ka)-II Hexapod Robot

Modifikasi Desain PID Controller Pada Permanent Magnet Synchronous Motor Dengan Flower Pollination Algorithm

Aplikasi Metode Cerdas untuk Optimasi Controller PID Motor DC Berbasis Firefly Algorithm

Contact Info

Product

Resources

About