scholarly journals DESIGN OF OPTIMAL PID CONTROLLER FOR THREE PHASE INDUCTION MOTOR BASED ON ANT COLONY OPTIMIZATION

SINERGI ◽  
2020 ◽  
Vol 24 (2) ◽  
pp. 125 ◽  
Author(s):  
Muhammad Ruswandi Djalal ◽  
Faisal Faisal
Keyword(s):  
Ant Colony Optimization ◽  
Induction Motor ◽  
Pid Controller ◽  
Fitness Function ◽  
Ant Colony ◽  
Food Sources ◽  
Optimal Parameters ◽  
Motor Speed ◽  
Tuning Method ◽  
Pid Controller Tuning

Speed control of an induction motor is an important part of the operation of an induction motor. One method of regulating motor speed is the addition of a PID controller. PID parameters must be tuned properly to get the optimal speed. In this study, the PID controller tuning method uses an artificial intelligence method based on Ant Colony Optimization (ACO). ACO algorithm in an intelligent algorithm that is inspired by the behavior of ants looking for food sources in groups with traces of feromone left behind. In this study, food sources are represented as optimal parameters of PID. From the computational results obtained optimal parameters respectively, P (Proportional) 0.5359, I (Integral) 0.1173, D (Derivative) 0.0427. ACO computing found the optimal parameters in the 21st iteration with a minimum fitness function of 11.8914. Case studies are used with two variations of the speed of the induction motor input. With optimal tuning, the performance of the induction motor is increasing, marked by a minimum overshoot of 1.08 pu and a speed variation of both overshoots of 1,201 pu, whereas without control 1.49 pu and 1.28 pu, as well as with PID trial control of 1.22 pu and 1.23 pu respectively. The benefits of this research can be used as a reference for the operation of induction motors, by tuning the Ant Colony intelligent method for the PID controller in real-time with the addition of microcontroller components.

Tuning Methods of PID Controller for DC Motor Speed Control

2016 ◽  
Vol 3 (2) ◽  
pp. 343 ◽  
Author(s):  
Ashwaq Abdulameer ◽  
Marizan Sulaiman ◽  
MSM Aras ◽  
Dawood Saleem
Keyword(s):  
Pid Controller ◽  
Engineering Students ◽  
Speed Control ◽  
Dc Motor ◽  
Motor Speed ◽  
Tuning Method ◽  
Pid Tuning ◽  
Pid Controller Tuning ◽  
Long Time ◽  
Tuning Methods

The traditional PID controllers are used for a long time to control the DC motor for many industrial processes, that because of the simplicity, flexibility, and satisfactory performance of this type of controller. This paper discusses the basic PID tuning method (Ziegler-Nichols) and its modification (Chien-Hrones-Reswick). Also, analysis the speed control DC motor response using the PID controller parameters that result from the tuning methods mentioned earlier. Moreover, explain the advantage and disadvantage of each formula of these methods.  GUL/MATLAB windows used to implementing both methods to create more comfortable and friendly environment for better understanding of the PID controller tuning methods formula for engineering students and practicing engineers.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

PID CONTROLLER TUNING USING ANT COLONY METHOD FOR SERVO HYDRAULIC SYSTEM

2019 ◽  
Vol 42 (4) ◽  
pp. 148-152
Author(s):  
Shaymaa Mahmood Mahdi ◽  
Karam Samir Khalid ◽  
Shakir Mahmood Mahdi
Keyword(s):  
Hydraulic System ◽  
Pid Controller ◽  
Ant Colony ◽  
Controller Tuning ◽  
Pid Controller Tuning

scholarly journals A Hybrid Ant Colony and Cuckoo Search Algorithm for Route Optimization of Heating Engineering

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2675 ◽  
Author(s):  
Yang Zhang ◽  
Huihui Zhao ◽  
Yuming Cao ◽  
Qinhuo Liu ◽  
Zhanfeng Shen ◽  
...  
Keyword(s):  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Fitness Function ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Elapsed Time ◽  
Route Design

The development of remote sensing and intelligent algorithms create an opportunity to include ad hoc technology in the heating route design area. In this paper, classification maps and heating route planning regulations are introduced to create the fitness function. Modifications of ant colony optimization and the cuckoo search algorithm, as well as a hybridization of the two algorithms, are proposed to solve the specific Zhuozhou–Fangshan heating route design. Compared to the fitness function value of the manual route (234.300), the best route selected by modified ant colony optimization (ACO) was 232.343, and the elapsed time for one solution was approximately 1.93 ms. Meanwhile, the best route selected by modified Cuckoo Search (CS) was 244.247, and the elapsed time for one solution was approximately 0.794 ms. The modified ant colony optimization algorithm can find the route with smaller fitness function value, while the modified cuckoo search algorithm can find the route overlapped to the manual selected route better. The modified cuckoo search algorithm runs more quickly but easily sticks into the premature convergence. Additionally, the best route selected by the hybrid ant colony and cuckoo search algorithm is the same as the modified ant colony optimization algorithm (232.343), but with higher efficiency and better stability.

scholarly journals Speed Control Series Dc Motor Using Ant Colony Optimization

2019 ◽  
Vol 20 (2) ◽  
pp. 105 ◽  
Author(s):  
Muhammad Ruswandi Djalal ◽  
Herman HR
Keyword(s):  
Ant Colony Optimization ◽  
Fitness Function ◽  
Speed Control ◽  
Dc Motor ◽  
Ant Colony ◽  
Control Series ◽  
System Optimal ◽  
Non Linear

Motor DC yang terhubung dengan seri atau shunt banyak digunakan dalam berbagai aplikasi. Karena memiliki torsi yang relatif tinggi untuk memikul beban dibandingkan dengan motor permanen magnet dengan ukuran yang sama. Motor permanen magnet bersifat linear sedangkan motor DC bersifat non linear. Karakteristik non linear dari motor DC seperti saturasi dan gesekan dapat menurunkan kinerja dari konvensional Kontrol. Algoritma Ant Coloni pada prinsipnya mencari sumber makanan berdasarkan jejak feromone yang kemudian secara berkelompok akan mengikuti jejak yang memiliki feromone yang terbesar. Dengan prinsip ini algoritma akan mencari parameter yang paling optimal untuk diisikan pada parameter PID, sehingga didapatkan kendali optimal pada kecepatan Motor DC Seri. Hasil optimasi ant colony didapatkan nilai fitness function sebesar 79.4892, dengan 50 kali iterasi, dan parameter nilai PID yang optimal di mana, parameter P (Proportional) sebesar 23.0337, I (Integral) sebesar 7.9168, dan D (Derivative) sebesar 7.8163. Dengan beberapa case kecepatan dan perubahan beban pada Motor DC Seri, dengan kendali PID Ant Colony didapatkan kinerja system optimal dengan overshoot yang minimum dan settling yang cepat.

scholarly journals Drafs A Routing Algorithm Based On Distributed Food Sources Using Ant Colony Optimization

2014 ◽  
Vol 08 (03) ◽  
pp. 265-281
Author(s):  
Arash Ghorbannia Delavar ◽  
Emetis Niazmand ◽  
Javad Bayrampoor ◽  
Vahe Aghazarian
Keyword(s):  
Ant Colony Optimization ◽  
Routing Algorithm ◽  
Ant Colony ◽  
Food Sources
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