Tugino ST MT [email protected]/files/2012/07/7-Kontrol-Robot.pdfsensor)...

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1 Tugino ST MT [email protected] Jurusan Teknik Elektro STTNAS Yogyakarta Robot Control Control Methods Conventional Joint PID Control Widely used in industry Advanced Control Approaches Computed torque approach Nonlinear feedback Ad ti t l Tugino, ST MT STTNAS Yogyakarta 2 Adaptive control Variable structure control ….

Transcript of Tugino ST MT [email protected]/files/2012/07/7-Kontrol-Robot.pdfsensor)...

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Tugino ST [email protected]

Jurusan Teknik ElektroSTTNAS Yogyakarta

Robot Control

Control MethodsConventional Joint PID Control

Widely used in industryAdvanced Control Approaches

Computed torque approachNonlinear feedback Ad ti t l

Tugino, ST MT STTNAS Yogyakarta 2

Adaptive controlVariable structure control….

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Kontrol ON/OFF

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Sistem Kontrol Robotik(kontrol robot loop terbuka/tertutup)

Referensi Kontroler Robot

Gerak

Referensi

Hasil Gerak sesungguhnya (dibaca oleh

sensor)

Error = Gerak referensi – Gerak aktual

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Robot

Referensi Gerak

Kontroler

sensor)

Gerak aktual

+

-

3

Teori Dasar:Penggunaan Transformasi Laplace

∫∞

)()}({ dff st∫ −=0

)()}({ dtetftfL st

jika )()}({ sXtxL =maka )()}({ ssXtxL =& ))(()}({ ssXstxL =&&

k i i

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percepatan/akselerasi

s1

s1

kecepatan posisi

)(tx&& )(tx& )(tx

s(s X(s)) s X(s) X(s)

Contoh: Robot Tangan Satu Sendi

Robot (lengan tunggal)

Sensor posisi (potensiometer)

Y

tact ΔΔ

=θθ&

Robot (lengan tunggal)

Aktuator (Motor DC)

θ

X

tact ΔΔ

=θθ&

&&

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+

-

Error = refθ – actθ

Amplifier s1

s1 Sendi

Robot Motor DC actθ&& actθ& actθrefθ

Sistem Robot

τIKtn

Kontrol

Sistem Kontroler

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Metoda Kontrol Klasik (P)

H(s) r Kp y +

-

e u

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eKpu ⋅=

Metoda Kontrol Klasik (I)

r Ki y

u H(s) r

sKi

y +

-

e u

⎤⎡

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KidTTetut

⎥⎦⎤

⎢⎣⎡= ∫0 )()(

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Metoda Kontrol Klasik (P-I)

Kp

H(s) r

sKi

y +

-

e u

Kp +

+

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sKiKpsG +=)(

Metoda Kontrol Klasik (D)

r y

u

H(s) r Kds ⋅

y +

-

e u

Δ

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eKdu &⋅=teKdu

ΔΔ⋅=

6

Metoda Kontrol Klasik (P-I-D)

H(s) r

Kds ⋅

y +

-

e u +

+

Kp

sKi +

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Motor DC magnet permanen

R L

Va Ia

Vb

θωτ ,,

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ωbaa

a KRIdt

dILV ++= ] [)(

)(

btneffeff

tn

a

LKKfRJsRs

nKsVs

++=

θ

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Fungsi transfer open loop Motor DC magnet permanen

sθ(s) (s) Va(s)

+

- RsL +

1

effeff fsJ +1Ktn

s1

Kb

θ(s) ( )( )

)( tL nKsθ

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] [)()(

btneffeff

tn

a

LKKfRJsRs

nKsVs

++=

θ

Ia(s) 1)( −sHKtn

s1 θ(s) sθ(s) (s)

Motor DC Servo

Motor DC-MP

Kecepatan

Referensi, refθ&

Kontrol PID +

-

Kecepatan

aktual, actθ&

Motor DC Servo dengan kontrol kecepatan

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Skema ekivalen Motor DC Servo dengan kontrol kecepatan

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Control Theory Review (I)PID controller: Proportional / Integral / Derivative control

e= ψd − ψa

actual ψadesired ψd V

Motor- compute V using PID feedback

ψd − ψa

Error signal e

V = Kp • e + Ki ∫ e dt + Kd )d edt

Closed Loop Feedback Control

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actual ψa

Reference book: Modern Control Engineering, Katsuhiko Ogata, ISBN0-13-060907-2

Evaluating the response

steady-state errorovershoot steady state error

settling time

overshoot -- % of final value exceeded at first oscillation

rise time -- time to span from 10% to 90% of the final value

ss error -- difference from the system’s desired value

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How can we eliminate

the steady-state error?rise time

settling time -- time to reach within 2% of the final value

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Control Performance, P-type

Kp = 20 Kp = 50

Tugino, ST MT STTNAS Yogyakarta 17Kp = 200 Kp = 500

Control Performance, PI - type

Kp = 100

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Ki = 50 Ki = 200

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You’ve been integrated...

Kp = 100

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unstable & oscillation

Control Performance, PID-typeKp = 100 Ki = 200 Kd = 2 Kd = 5

Kd = 10 Kd = 20

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PID final control

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Motor DC Servo dengan kontrol kecepatan

Tegangan Supply DC

(misal 0÷24V)

refθ&

Rangkaian Driver

actθ&

+ -

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+ -

Vref(+) Vref(-)

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RC Servo

Arah piringan 120º s/d +120º 0º Sinyal Tegangan Input PWM piringan

Servo -120 s/d +120 0 Sinyal Tegangan Input PWM

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RC Servo

Prinsip kerja RC Servo

Teknik Pulse Width Modulation

Tegangan PWM Tegangan ekivalen linier V V

=

=

0V

Vsat

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t t

=

Prinsip kerja PWM

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Sebuah rangkaian pembangkit PWM lengkap dengan driver H-bridge untuk motor DC-MP/DC-SV

1K2

+

-+

-

74HCT245¼ LM324

¼ LM324 +12V +12V

1K2 1K2 BD643 BD643+(12÷24)V

+5V

74HCT245

Gnd Trg Dis

Vcc 20K

+ +

+

-

+

- + -

74HCT24574HCT04

¼ LM324

¼ LM324 ¼ LM324

¼ LM324

+12V

+12V +12V

1K2

1K2 1K2

2K2

2SD1314

BD643

BD643 BD643

BD643

2SD1314

2SD1314

2SD1314 2SD1314

1K2

LM555

M

1K2+12V

20K

CW/CCW

1

2

8

7

1/0

H-bridge

74HCT04

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.01

Trg Out Rst Ctl

Thr Dis 20K

.01

(0÷5)V refθ&

3

4

6

5

M

Arah (1/0)

Kecepatan (0÷5)V

Driver Motor DCBerbasis

PWM (ep)

Low-level & High Level Control

P i h Lingkungan

Low-level Control

Endra Pitowarno © 2007

Perintah Gerak Aktuator

Lingkungan Robot

Sensor Internal

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Sensor Eksternal

High-level Control

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Low-level & High Level Control

Sensor Internal:sensor posisi,

sensor kecepatan, dan sensor percepatan,

Sensor Eksternal: sensor taktil (tactile), berbasis sentuhan: misalnya limit switch pada bemper robot

f d t i (t )

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sensor force dan sensor torsi (torque sensor),sensor proksimiti,

sensor jarak (sonar, PSD, dll),sensor vision (kamera),

gyro, kompas digital, detektor api, dan sebagainya.

Low-level & High Level Control

P i h Lingkungan

Low-level Control

Perintah Gerak Aktuator

Lingkungan Robot

Sensor Internal

Kontroler

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Sensor Eksternal

High-level Control

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Low-level Control

Kontrol PosisiReferensi posisi yg selalu berubah

Posisi aktual tiap derajat aktuator

Perintah Gerak Aktuator

Lingkungan Robot

Sensor Internal

Kontroler

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Sensor Eksternal

High-level Control

Low-level Control

Kontrol PosisiReferensi posisi yg selalu berubah

Posisi aktual tiap derajat aktuator

Algoritma program (ex:

IF-THEN-ELSE)

Kontroler PID + Aktuator

Lingkungan Robot

Sensor posisi (rotary encoder)

Kontroler

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Proximity sensor (ex: line

sensor)

High-level Control

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Low-level Control

Kontrol Posisi & KecepatanReferensi posisi & kecepatan yg selalu berubah

Posisi & kecepatan aktual tiap derajat aktuator

Perintah Gerak

(posisi) & Kecepatan

Lingkungan Robot

Sensor Internal (posisi &

kecepatan)

Kontroler PID

Aktuator

Kontroler

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Sensor Eksternal

High-level Control

Penggunaan Kontrol Cerdas

Sistem r Kontroler y +

e u

Endra Pitowarno © 2007

Sistem Robot

r berbasis

AI

y +

-

e u

AI & Terminologi:orang pertama > Alan Turing (1937)

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Neural Network: Warren McCulloch (1943)Teori Fuzzy: Lukacewick (1930an)Fuzzy Sets: Lotfi Zadeh (1965)Genetic Algorithm: Teori DarwinKonsep GA dalam Evolutionary Computation (EC): Holland (1975)

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Neural Network Controluntuk Line-Tracer/Route Runner Robot

Endra Pitowarno © 2007

PB0

Jalur PUTIH di lantai GELAP

Robot Neural Network

ROUTE RUNNERPB1

PB2PB3

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Motor Kanan

Motor Kiri DAC1 DAC0

Korelasi fungsi I/O pada robot Route Runner

NoSensor (1:gelap 0:putih) Motor (0÷5)V

PB3 PB2 PB1 PB0 ML MR

Endra Pitowarno © 2007

1 0 0 0 0 1.0 1.0

2 0 0 0 1 4.5 0

3 0 0 1 0 4.5 1.2

4 0 0 1 1 4.5 2.7

5 0 1 0 0 1.2 4.5

6 0 1 0 1 1.2 4.0

7 0 1 1 0 4.6 4.6

8 0 1 1 1 4.6 4.5

9 1 0 0 0 0 4 5

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9 1 0 0 0 0 4.5

10 1 0 0 1 3.0 3.0

11 1 0 1 0 1.2 4.5

12 1 0 1 1 4.5 3.0

13 1 1 0 0 2.7 4.5

14 1 1 0 1 3.0 4.5

15 1 1 1 0 0 4.5

16 1 1 1 1 0 0

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Disain BP-NN dengan Struktur 4-5-2

Endra Pitowarno © 2007

i = 3

i = 1

i = 2

j = 1

j = 2

j = 3

k = 1

k = 2

PB1

PB3

PB2 ML

wij wjk

MR

Sensor

Motor

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Hidden layer

Output layer

Input layer

i = 4

j = 4

PB0 j = 5

Disain BP-NN dengan Struktur 4-5-2

Endra Pitowarno © 2007

⎥⎥⎥⎥

⎢⎢⎢⎢

=

1010101010101010110011001100110011110000111100001111111100000000

PB

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⎥⎦

⎤⎢⎣

⎡=

0.05.45.45.40.35.40.35.45.46.40.45.47.22.10.00.10.00.00.37.25.42.10.30.06.46.42.12.15.45.45.40.1

M

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Disain BP-NN dengan Struktur 4-5-2Endra Pitowarno © 2007

Tugino, ST MT STTNAS Yogyakarta 37

Disain BP-NN dengan Struktur 4-5-2

⎥⎥⎥⎤

⎢⎢⎢⎡ −

092860183740902971950807325.063.64435.78533.830463.031589.01446.1067357.0

Endra Pitowarno © 2007

⎥⎥⎥⎥

⎦⎢⎢⎢⎢

⎣ −−−−−−−=

15491.044677.065177.012292.05697.77116.103039.163449.240928.6018374.09029.719508.0Hw

⎥⎦

⎤⎢⎣

⎡−−−−−−−

=4454.93478.1366.39181.62514.64279.1026415.25084.821555.00012.62

outw

⎤⎡

Tugino, ST MT STTNAS Yogyakarta 38

⎥⎥⎥⎥⎥⎥

⎢⎢⎢⎢⎢⎢

−−

=

79925.01907.1746388.0

6095.2333687.0

Hb [ ]3654.70134.46 −−=outb

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Evaluasi fungsi I/O pada robot NN Route Runner

Sensor (1:gelap 0:putih] Motor (0÷5)V

PB3 PB2 PB1 PB0Target Output NN

ML MR ML MR

Endra Pitowarno © 2007

0 0 0 0 1.0 1.0 0.9998 0.98506

0 0 0 1 4.5 0 4.5008 0.001066

0 0 1 0 4.5 1.2 4.4994 1.2138

0 0 1 1 4.5 2.7 4.4997 2.6993

0 1 0 0 1.2 4.5 1.2072 4.3958

0 1 0 1 1.2 4.0 1.193 4.1073

0 1 1 0 4.6 4.6 4.5917 4.684

0 1 1 1 4.6 4.5 4.6104 4.3922

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1 0 0 0 0 4.5 0.0032461 4.4852

1 0 0 1 3.0 3.0 2.9982 3.0094

1 0 1 0 1.2 4.5 1.1977 4.5151

1 0 1 1 4.5 3.0 4.5014 2.9912

1 1 0 0 2.7 4.5 2.701 4.5272

1 1 0 1 3.0 4.5 2.9967 4.4935

1 1 1 0 0 4.5 1.1123e-006 4.5

1 1 1 1 0 0 1.6049e-006 9.6619e-007

Aplikasi logika fuzzy pada sistem multi-sensor-multi-aktuator

(-12)÷(+12)V(0 ÷ 5)V atau

Logika FUZZY

Sensor-1Sensor-2Sensor-3Sensor-4Sensor-5

( 12)÷(+12)V(0 / 5)V

Motor Kanan

Motor Kiri

Tugino, ST MT STTNAS Yogyakarta 40

Sensor-6

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Dilemma: kecerdasan vs. kecepatan mesin

kece

rdas

an Algoritma program makin kompleks

Algoritma program makin sederhana/singkat

Tugino, ST MT STTNAS Yogyakarta 41kecepatan mesin

sederhana/singkat

Kasus: Line Follower

Makin tinggi kecepatan, makin mudah tergelincir (inersia membesar seiring kecepatan gerak

Endra Pitowarno © 2007

(inersia membesar seiring kecepatan gerak membesar)Pada kasus simpangan: makin laju gerak robot, makin sulit mendeteksi jalur simpang, makin sulit mengontrol efek inersiaStruktur/instalasi posisi sensor terhadap

Tugino, ST MT STTNAS Yogyakarta 42

Struktur/instalasi posisi sensor terhadap aktuator: isu penting

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Kasus: Mata Kamera

Makin tinggi kecepatan, makin rendah kualitas citra yg ditangkap (dynamic noise) >> informasi

Endra Pitowarno © 2007

citra yg ditangkap (dynamic noise) >> informasi posisi tidak akuratDilemma: makin tinggi resolusi citra, makin lambat proses identifikasinya. Makin rendah resolusinya, makin tidak akurat hasil identifikasinya

Tugino, ST MT STTNAS Yogyakarta 43

identifikasinya.Isu penting: high speed camera

Metode: Model-Plan-ActEndra Pitowarno © 2007

Pemodelan

Baca Sensor

Lingkungan/ Environment ( d j

Perencanaan

Model

Plan

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Action (aktuasi)

(Medan Kerja Robot)

Gerak Plan

Act

Gerak Aktuator

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Metode: Model-Plan-ActEndra Pitowarno © 2007

Tugino, ST MT STTNAS Yogyakarta 45

GOAL

START

Metode: Model-Plan-ActEndra Pitowarno © 2007

PMID1 PC1

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GOAL

START

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Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

Tugino, ST MT STTNAS Yogyakarta 47

GOAL

START PMID2

PC2

Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

Tugino, ST MT STTNAS Yogyakarta 48

GOAL

START PMID2

PC2

PMID4 PC4

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Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

Tugino, ST MT STTNAS Yogyakarta 49

START PMID2

PC2

GOAL

PC4 PMID4

PC5

Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

PMID4

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GOAL

START PMID2

PC2 PC3

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Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

Tugino, ST MT STTNAS Yogyakarta 51

START PMID2

PC2

GOAL

PC4A PMID4A PC5

PMID4B

Metode: Model-Plan-ActEndra Pitowarno © 2007

PC1 PMID3

PMID1 PC3

Tugino, ST MT STTNAS Yogyakarta 52

START PMID2

PC2

GOAL

PC4A PMID4A PC5

PMID4B

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Pengendalian Berbasis Komputer

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Komputer Sebagai Pengendali

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Pengendali Berbasis Prosesor Mikro

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Pengendali Berbasis Prosesor Mikro

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Bentuk Robot Industri tipe REVOLUTE

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Sistem Kendali Robot Industri

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