1. Fuzzy Logic Workshop
Design of Fuzzy Controller for Temperature Chamber  

2. Syllabus What is the system in control ? System Identification
Mathematical model and Choice of sampling period.
FiO Boards and RapidSTM32 Blockset for Applied on Temperature Control (HIL Test)
Matlab Fuzzy Logic Toolbox
Finite State Machine
Stand-Alone System Implementation
ทฤษฎีระบบควบคุม เป็นสาขาหนึ่งของคณิตศาสตร์และวิศวกรรมศาสตร์ ในที่นี้ การควบคุมหมายถึง การควบคุมระบบพลศาสตร์ ให้มีค่าเอาต์พุตที่ต้องการ โดยการป้อนค่าอินพุตที่เหมาะสมให้กับระบบ

3. What is a control system ?
Control Signal
(input)
Output
SISO
Heater
(input)
Temperature
(Output)
In most systems there will be an input and an output.  This block diagram represents that.  (Control system designers and engineers use block diagrams to represent systems.  Get used to them.)  Signals flow from the input, through the system and produce an output.
Heater
FAN
(2 input)
Temperature
(Output)
MISO

4. Controller Sensor Temperature (Output) Set Point Error Heater dError
FAN
Sensor

5. How to Identify the System
Analyze the input-output data pairs to fit the parameters in the used model (structure)
Unknown
System X Y ? ? ?
Experimental determination of system model. There are two methods of system identification:
Parametric Identification: The input-output model coefficients are estimated to “fit” the input-output data.
Frequency-Domain (non-parametric): The Bode diagram [G(jw) vs. w in log-log scale] is estimated directly form the input-output data. The input can either be a sweeping sinusoidal or random signal.
Unknown

6. Equivalent DC motor

7. How to Identify the System
Unknown
System X Y
two methods
1.Parametric identification
2.Non-parametric identification.
Experimental determination of system model. There are two methods of system identification:
Parametric Identification: The input-output model coefficients are estimated to “fit” the input-output data.
Frequency-Domain (non-parametric): The Bode diagram [G(jw) vs. w in log-log scale] is estimated directly form the input-output data. The input can either be a sweeping sinusoidal or random signal.

8. System identification process
Prior
knowledge
Experiment
Design
In building a model, the designer has control over three parts of the process
Generating the data set ZN
Selecting a (set of) model structure (1st or 2nd order)
Selecting the criteria (least squares for instance), used to specify the optimal parameter estimates
There are many other factors that influence the final model, however, this course will focus on these three factors and the method for (recursive) parameter estimation
Data
Choose
Model Set
Choose
Criterion
of Fit
Calculate Model 
Validate
Model 

9. Unknown
System X Y
Output
Input

10. Model validation ^
u(t)
y(t)
Model q ^
u(t)
u(t)
Plant q
y(t)
d(t)

11. Prepare plant for system identification

12. Temperature Control Module

13. Initial preparations Connect CN9(GND) to GND Fio Board
Connect Temperature sensor from CN2 to Analog input
Select PWM Output pins (B6, B7, B8, etc.) form Fio to CN3,CN4 and CN6
Connect 12 VDC to connector J1

14. FiO Board Std
Temp. Board

15. FiO Board Lite
Temp. Board

16. ADC Configuration

17. PWM Output pins

18. Temperature sensor module
refer on P.8
Temperature sensor module

19. refer on P.65
Then

20. refer on P.66

21. Heater and Fan control modules

22. refer on P.66

23. 10msec = 100Hz
FAN2
FAN1
10% Duty Cycle
HALOGEN BULB
40% Duty Cycle

24. refer on P.10
Design of Display

25. refer on P.11

26. Let’s try
Send the command for control heater and fan on Target from HOST

27. refer on P.16
Design of Display

28. Let’s try
Send the temperature from Target to HOST and display on the Matlab Scope

29. Filter Design

30. Filter design
First order Low pass filter

32. Continuous
C2D
Discrete
Define cutoff frequency
Define sample period

33. ‘ jω’ is substituted for ‘s’

34. Now, since we know that the cutoff frequency, ω , occurs at Magnitude=0.707, this can be substituted into above to get
Solve for wc

35. we define cutoff frequency at 20 Hz, then

36. we define cutoff frequency at 5 Hz, then

37. Fast Fourier Transform (FFT)

39. System identification
Introduction

40. SYSTEM IDENTIFICATION
The System Identification Problem is to estimate a model of a system based on input-output data.
System
disturbance (not observed)
v(t)
y(t)
u(t)
output (observed)
input (observed)
continuous

41. Typical to be used as input for identification
Step
PRBS
Sinusoidal

42. Parametric ID of step response
First order process with dead time
Most common industrial process model
Response to a control step applied

43. refer on P.17
Unknown
System X Y

44. refer on P.19

45. Import data to system identification toolbox

46. Matlab Command for open system identification toolbox

47. refer on P.17

48. Unknown
System X Y Y X

49. temp = simout(:,2)
ตัดข้อมูล 2000 ถึง 6000
duty = simout(2000:1:6000,1)
tem = temp(2000:1:6000)

51. Choice of sampling period

53. Applied in many appliances.
Researchers have developed a theory for the same concept as the FUZZY set stand out.
Applied in many appliances.
Air conditioning
Washing machine
Rice cooker.
And more…
Neuro Fuzzy Rice Cooker
Fuzzy Washing Machine

54. Fuzzy Set Theory 38.7°C 38°C 40.1°C 41.4°C 42°C 39.3°C 38.7°C 38°C
Conventional (Boolean) Set Theory:
38.7°C
38°C
“Strong Fever”
40.1°C
41.4°C
Fuzzy Set Theory:
42°C
39.3°C
38.7°C
38°C
37.2°C
40.1°C
41.4°C
42°C
39.3°C
“Strong Fever”
“More-or-Less” Rather Than “Either-Or” !
37.2°C

55. Fuzzy Set Definition Discrete Definition:
µSF(35°C) = 0 µSF(38°C) = 0.1 µSF(41°C) = 0.9
µSF(36°C) = 0 µSF(39°C) = 0.35 µSF(42°C) = 1
µSF(37°C) = 0 µSF(40°C) = 0.65 µSF(43°C) = 1
Continuous Definition:

56. Linguistic Variable
A Linguistic Variable Defines a Concept of Our Everyday Language!
76
77
78
79
80
81
82
83
84
Low temp
Normal
High temperature
Rise temperature 1
0.7
0.2

57. Fuzzy control provides a formal methodology for representing, manipulating, and implementing a human’s heuristic knowledge about how to control a system.
Plant
Fuzzification
Defuzzification
Inference
Mechanism
Knowledge
Base
Reference input
Output
Command variable

58. Basic Elements of a Fuzzy Logic System
Command Variable
(Linguistic Value)
Measured Variable
(Linguistic Value)
2. Fuzzy-Inference
Linguistic Value
Numerical Value
1.Fuzzufication
3.Defuzzification
Measured Variable
(Numerical Value)
Plant
Command Variable
(Numerical Value)

60. Choice of Sampling Interval
Another important aspect in sampled data control systems is the choice of sampling intervals.

61. Plant testing on Target

62. Plant testing on Host

63. 2 1

64. 1

71. Process Value
Set Point

72. dError 0
Error +
Error 0
dError -
dError +
Error -
dError 0

73. CASE: 1
Error 0
dError 0

74. CASE: 2
dError -
Error 0

75. CASE: 3
dError +
Error 0

76. CASE: 4
Error -
dError 0

77. CASE: 5
dError -
Error -

78. CASE: 6
dError +
Error -

79. CASE: 7
Error +
dError -

80. CASE: 8
dError 0
Error +

81. CASE: 9
Error +
dError +

82. Fuzzy Control Rules Z Z Z Z Z PS PS PM PL Zero Positive Small Z PS
Positive Medium
Positive Large PM PL

83. Fuzzy Control Rules Zero Positive Small Z PS Positive Medium
Positive Large PM PL