Optimization of PID Control for DC Motor Based On Artificial Bee Colony Algorithm

Oleh :Muhammad Ruswandi Djalal

2213201008

Wudai Liao, Yingyue Hu, Haiquan Wang Zhongyuan University of Technology, China

IEEE, Proceedings of the 2014 International Conference on Advanced Mechatronic Systems, Kumamoto, Japan, August 10-12, 2014

1

Problems and Solutions... Tuning parameter PID

Tuning parameter PID using ABC

2

3

Coba dibandingkan

Firefly AlgorithmCuckoo Search AlgorithmBat AlgorithmFlower Pollination AlgorithmDifferential EvolutionAnt Colony OptimizationParticle Swarm OptimizationImperialist Competitive Algorithm

I. INTRODUCTION PID : Simple structure, good Stability & strong Robustness

PID Parameter Manual method, large overshoot and difficult to get ideal control effect.

Artificial bee colony

4

II. ABC ALGORITHM

5

Artificial Bee Colony

6

Foraging

Sharing Information

Select Best Food

Konsep : Lebah mencari sumber makanan (madu) yang terbaik

FORAGING

7

SourceB

SourceA Sampling

SHARING INFORMATION

waggle dance

8

A,B,CSource A,B,C Sample Hive

SELECT FOOD SOURCE

9

Source A

10

Inisialisasi

Movement of scout bee

Compare current & new foodfi is the fitness value of solution Zi. If the nectar amount of the new solution is higher than of the previous one(the fitness of Zi is better than Yi), the bee memories the new position and forgets the old one.

11

Artificial Bee Colony

12

Inisialisasi Populasi lebah (Kp, Ki, Kd)

Proses (Foraging)

Select Best FoodKp “best”Ki “best”Kd “best”

Konsep : Lebah mencari sumber makanan (madu) yang terbaik Objective Function

III. DESIGN OF PID CONTROLLER DC Motor Modeling PID controller Designing

13

DC Motor Modeling

DC permanent magnet motor

rated speed is 1400rpm

speed measured 1250rpm

In order to obtain the parameters of the first-order system, system identification theory[19] is adopted

Mechanical gainMechanical time constant.

14

PID controller Design how to configure the three parameters of PID (Kp, Ki,

Kd)

P element : to reduce the deviationI element : to eliminate static error and improve the stability of systemD element : to reduce the setting time

15

Objective Function

16

Objective Function

IV. Simulation & Analysis

sampling time, T = 10 ms Kp [0,40] Ki [0,10] Kd [0,1] Maxcycle 100

17

18

19

20

V. Conclusions

Optimization problem of PID parameters for DC motor can be effectively solved by ABC algorithm

the validity of ABC algorithm which can be effectively applied to optimize the parameters of PID controller in DC motor system is proved.

21

22

Coba dibandingkan Firefly Algorithm Cuckoo Search Algorithm Bat Algorithm Flower Pollination Algorithm Differential Evolution Ant Colony Optimization Particle Swarm Optimization Imperialist Competitive Algorithm

Firefly Algorithm

Firefly Algorithm

24

Inisialisasi Kunang-Kunang

Perpindahan (Proses)

Best Firefly

Konsep : Kunang-Kunang akan tertarik pada yang lebih terang

25

26

Cuckoo Search Algorithm

Cuckoo Search Algorithm

27

Inisialisasi Sarang

Pencarian (Proses)

Best Nest

Konsep : Menempatkan telurnya di sarang burung lain

28

Bat Algorithm

Bat Algorithm

30

Inisialisasi Populasi

Pencarian (Proses)

Posisi Terbaik

Konsep : terbang di kegelapan malam mencari makanan tanpa menabrak sesuatu apapun (Kemampuan Ekolokasi)

31

Flower Pollination Algorithm

Flower Pollination Algorithm

33

Inisialisasi Populasi Flowers

Random (Proses)

Best Solution

Konsep : terinspirasi dari alam sekitar, yaitu proses pernyebukan bunga (Biotik & abiotik)

34

35

Differential Evolution

Differential Evolution

36

Inisialisasi Populasi

Mutasi Populasi

Populasi Baru

Konsep : Terinspirasi dari evolusi biologis berbasis populasi yang menggunakan siklus perulangan dari rekombinasi dan seleksi untuk mengarahkan populasi mencari nilai optimum

37

Ant Colony Optimization

Ant Colony Optimization

39

Inisialisasi

tour

Best rute

Konsep : Menemukan jalur terpendek antara sarang dan sumber makanan dengan mengikuti jejak feromon

40

Particle Swarm Optimization

Particle Swarm Optimization

42

Inisialisasi Partikel

Random (Proses)

Best Position

Konsep : meniru proses alam dalam berkomunikasi satu sama lain dalam berkumpul, migrasi, atau berburu

43

Imperialist Competitive

Algorithm

Imperialist Competitive Algorithm

45

konsep : kompetisi kerajaan untuk memperoleh kekuasaan terbesar

Inisialisasi Empire

Kompetisi (Proses)

Best Empire

46

47

Param.FireflyAlgorith

m

CuckooAlgorith

m

BatAlgorith

m

FlowerAlgorith

m

Diff. Evolutio

n

Ant Colony Optimizatio

n

Particle Swarm Opt.

Imperialist Competitive

Alg.Kp 40 4.36 25.2082  39.6776  40 39.7801 37.6829 34.7941Ki 2.8170 9.1  7.2255  2.8930 9.9951 9.1684 1.4529 0.0953Kd 1 0.59  0.0648  0.0052 1 0.9775 0.4777 1

Sekian...

48