Pyconkr 20150627_simulation_on_optical_image_stabilizer_using_python

Simulation on Optical Image Stabilizer using Python
부제 : control library로 바로 해보는 system simulation
HyungKwan Kim
hiraikaen@gmail.com

Today’s presenter is
• 김형관 주임
• LG Innotek (12’ 4~ 현재)
• Mobile OIS Camera 개발자
• G4/G3/G2/G-Flex2/Nexus5 (OIS engineering)
• MS in EE (Computer Vision, ITS)
© 2015 HyungKwan Kim

contents
• What is the Optical Image Stabilizer(OIS)?
• Motivation – Why? and Why Python?
• Library for System Simulation in Python
• Transfer Function & Bode Plot
• Generate Filters, PID, Time Delay
• Make Serial/Parallel/Feedback Connections
• Modelling Gyro & Actuator
• Let the system make “response”
• Measure the performance(Gain Cal., Trace Error)
• Usage of the simulation @ work
시스템 설계에 필요
한 기본 요소 세팅
한땀, 한땀 이어서
시스템으로 완성
평가 및 활용
OIS 갂단한 설명

What is the Optical Image Stabilizer?
이미지
센서
렌즈
손떨림에 의한 광경로 변동
밀린 stroke만큼
Lens를 Shift시켜
떨림을 보정
흔들린 사진(떨림각:2도)삼각대 촬영(무진동)

What is the Optical Image Stabilizer?
렌즈
홀센서 Y-axis
홀센서X-axis
자이로 드라이버컨트롤러
이미지
센서
• Very basic feedback system using Gyro, Hall, Actuator.
• Simple but enough to practice a closed-loop simulation

Motivation – Why Simulation?
• Closed-loop System Quality Control
현 Actuator Spec. 하에 안정성이 충분히 확보되는가? *
Step Response에 의한 정상상태 도달은 충분히 빠른가?
Gyro 열특성상, 품질 마짂은 얼마나 두어야 하는가?
• 샘플 제작 없이, 다양한 조합, 다양한 조건의 모듈 성능 검토 필요
A사 컨트롤러는 B사에 비해 추종오차가 얼마나 적을것인가? *
가격이 저렴한 A사의 Gyro의 노이즈는 성능에 영향을 줄 정도로 유의한가?
• Analystic Industrial Inspection
PCB 실장시 발생하는 SMT Rotation Angle은 얼마나 허용되는가?
Calibration 장비 정확도는 최소 얼마이상 확보되야 하는가?
• User에게 실제 의미가 있는 Spec. 정립 필요
성능이 얼마나 나와야 체감할 수 있을까?
감성평가가 가능한 이미지를 생성할 수 없을까? *
*표시 항목은
후반부에 결과 공유

Motivation – Why Python?
• It is free
M사의 프로그램도 좋지만… 돈도 돈이고 사내 구입 프로세스도 번거롭죠
각 Filter들을 Fully Customize가능하고, Fully Open된 홖경이 더 끌렸습니다.
• and easy to write code, easy to debug
출근길에 떠오른 idea를 퇴근 젂에 확인해 볼 수 있다는 점
C로는 쉽지 않죠?
• and even effective to debug one’s thought
컴파일 없는 즉각적인 실행, 시각화.
사고의 흐름을 놓치지 않게 하고, 잘못된 직관은 금방 바로 잡고.

Block Diagram of OIS
Gyro
HPF/LPF
Servo
Filter
(PID)
Sine
Wave
Gyro
Phase
Delay
Actuator
(cmk
model)
Gyro
Gain
Servo
Gain
Integrator
dps->deg.
Hall
Filter
+
-

Library for System Simulation in Python
• Control Systems Library for Python (Murray, Caltech)
젂달 함수를 이용한 시스템 시뮬레이션
직렬, 병렬, 피드백 커넥션 함수 제공
Time Response(Impulse, Step 등) 계산 가능
Documentation도 충실하며, 지속적인 업데이트
• Getting Started
1. http://sf.net/projects/python-control/files lib 다운로드
2. python setup.py install
3. source code 상에서 import

Make Transfer Function & Bode Plot
• General Form of Transfer Function
• Make Instance of Transfer Function
F = control.tf([1, a0, a1, … , an],
[1, b0, b1, … , bn])
• Make Bode Plot
plt.figure()
control.bode_plot(tf, freq_range)
𝑠 𝑛
+ 𝑎0 ∗ 𝑠 𝑛−1
+ ⋯ + 𝑎𝑛
𝑠 𝑛 + 𝑏0 ∗ 𝑠 𝑛−1 + ⋯ + 𝑏𝑛
• Transfer Function of HPF
• Make Transfer Function of HPF(10Hz)
• Make Bode Plot
𝐺 𝑠 =
𝑠 + 1
𝑠 + 𝑓

Generate Filters, PID, Time Delay
LPF
(Low Pass Filter)
𝐺 𝑠 =
1
𝑠 + 𝑓𝑐
𝐺 𝑠 =
𝑠 + 𝑓𝑙
𝑠 + 𝑓ℎ
𝐺 𝑠 =
𝑠 + 𝑓ℎ
𝑠 + 𝑓𝑙
𝐺 𝑠 =
𝑠2
+
𝐴
𝑄
𝑓𝑝 𝑠 + 𝑓𝑝
2
𝑠2 +
1
𝐴𝑄
𝑓𝑝 𝑠 + 𝑓𝑝
2
LBF
(Low Boost Filter)
HBF
(High Boost Filter)
PKF
(Peaking Filter)P
(Proportional)
I
(Integrator)
D
(Derivate)
𝐺 𝑠 =
𝐺𝑎𝑖𝑛
0 ∙ 𝑠 + 1
𝐺 𝑠 =
1
𝑠
𝐺 𝑠 = 𝑠
HPF
(High Pass Filter)
𝐺 𝑠 =
𝑠 + 1
𝑠 + 𝑓𝑐

• Make the transfer function of time delay
𝑠 𝑛
+ 𝑎0 ∗ 𝑠 𝑛−1
+ ⋯ + 𝑎𝑛
𝑠 𝑛 + 𝑏0 ∗ 𝑠 𝑛−1 + ⋯ + 𝑏𝑛
Generate Filters, PID, Time Delay
N-deg. Pade
estimation
(−1.0)𝑠 + 2.0
(1.0)𝑠 + 2.0
𝑒− 1.0 𝑠
𝑒−𝑡𝑠

w/ white noise w/ ZRO Shift
• Generate Wave
Choose the frequency that simulates hand shaking
• Add Degrading Factors
Sensitivity shift over temperature
Zero rate offset shift over
static noise ( sqrt/root(Hz) )
2Hz 6Hz 10Hz
Modelling Gyro
w/ Sensitivity Change

Modelling Actuator
• Spring-Damper Model (c-m-k model)
• Add Degrading Factors
𝐺 𝑠 =
𝑤 𝑛
2
𝑠2 + 2 ∗ 𝑡𝑎𝑢 ∗ 𝑠 + 𝑤 𝑛
2
Figure from http://ocw.mit.edu Freq(wn)=60, ampQ1(1/(2tau))=30db
Q1 amp-range variation Add harmonicsQ1 f-range variation

Gyro
HPF/LPF
Servo
Filter
(PID)
Sine
Wave
Gyro
Phase
Delay
Actuator
(cmk
model)
Gyro
Gain
Servo
Gain
Integrator
dps->deg.
Hall
Filter
+
-
System
Of
Single
Transfer
Function

Make Serial/Parallel/feedback connections
From http://www.cds.caltech.edu/~murray/books/AM05/pdf/am06-xferfcns_16Sep06.pdf,
Chapter 8. Transfer Functions, p. 253,

Gain + LPF = HBF꼴,
역시 제대로 표현 됨.
Serial
Connection
Parallel
Connection
= G
G2
HPF
G1
HBF
= G
G2
Gain
G1
LPF
HBF x HPF 이며, 곱셈에 따라서
Gain 수렴 지점 두 곳이 제대로 표현 됨.

feedback
Connection
= G
G1
HPF
G2
HBF
+
-

System
Total
Transfer
Function
Gyro
HPF/LPF
P
I
D
Sine
Wave
Gyro
Phase
Delay
Actuator
(cmk
model)
Gyro
Gain
Servo
Gain
Integrator
dps->deg.
Hall
Filter
+
-
Servo
Total
Transfer
Function
Gyro
Total
Transfer
Function
Hall
Filter
+
-
By Serial,
Parallel
Connection
By
Feedback
Connection

Let the system make “response”

Let the system make “response”
LPF( 𝒇 𝒄 : 6Hz)
HPF(𝒇 𝒄 : 6Hz)
INPUT
SINWAVE
2Hz 6Hz 10Hz

Measure the performance (Gain Cal., Trace Error)
• Gain Calibration
(minimize the gap between Input sinwave & output lens position
• Trace error  OIS 성능(dB) 홖산
Gain:0.18 Gain:0.20 Gain:0.22
Input(Hand Shake) &
Output(Lens Position)
Trace Error
(Hand Shake
–Lens Position)
OIS 성능
(=20*log10(hand_shake
/trace_error))

Usage#1 – actuator maker별 제어 안정성 비교
A사
B사

Usage#2 – controller chip maker별 OIS성능 비교
Minimal
Performance
23.0 dB
A사
B사 Minimal
Performance
14.8 dB

Usage#3 - 시뮬레이션 된 OIS 성능을 화상으로 확인
OIS Off OIS ON
Grade B
OIS ON
Grade A
Very useful when to decide “Target Spec.”

Usage#3 - 시뮬레이션 된 OIS 성능을 화상으로 확인
Overlaping images cropped with
the offset of trace-error
during pre-set exposure time
Trace error
 overlaping
offset

transfer functions, coefficients on this presentation
are not necessirily reflecting the real characteristics
of the product for confidential reason.

들어주셔서 감사합니다.
Thank you very much for your attending.

Pyconkr 20150627_simulation_on_optical_image_stabilizer_using_python

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