1. · Phase control TCA 785 Control thyristors, triacs, and transistors. The trigger pulses can be...

Computational Intelligence in Complex Decision Systems G. Oltean

1. Implementation of a

Temperature Control System using

ARDUINO


Close control loop

Fuzzy controller

Fuzzy logic system: 9 rules

Temperature Sensor

One Wire Digital Temperature Sensor - DS18B20

Heating element

Heating resistor 2Ω, supplied in ac (12V peak value)

Heating power control

Phase control of a SCR (thyristor) – TCA 785

System structure


Arduino development board

• “Brain” of the entire system

• Read current temperature

• Compute error and change-of-error

• Run fuzzy logic system

• Determine digital value of control signal

DAC - MCP4725, I2C interface

Provide analog value of control signal

Phase control board

Analog amplifier for control voltage – AD820

TCA 785 – phase control IC

System implementation


Block diagram


5

Functional diagram u(k) = u(k-1) - duc(k)


6

ARDUINO UNO development board


7

Programmable Resolution 1-Wire Digital Temperature Sensor

9-bit to 12-bit Celsius temperature measurements

Unique 1-Wire® Interface Requires Only One Port Pin for

Communication

Allows multiple DS18B20s to function on the same 1-Wire bus


8

12-Bit Resolution

On-Board Non-Volatile Memory (EEPROM)

External Voltage Reference (VDD)

Rail-to-Rail Output

Single-Supply Operation: 2.7V to 5.5V

I2C Interface

Eight Available Addresses

DAC MCP4725


9

Phase control TCA 785

Control thyristors, triacs, and transistors.

The trigger pulses can be shifted within a

phase angle between 0 ˚ and 180 ˚


10

TCA 785


11

Thermal enclosure Phase control board


12

16 x 2 LCD


13

Experimental setup


14

Experimental setup


15

Fuzzy logic system


16

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

err; cerr

Neg

Zero

Pos

-1 -0.5 0 0.5 10

0.2

0.4

0.6

0.8

1

du

N

Z

P

Input fuzzy sets

Output fuzzy sets


Control surface

errFls

cerrFlsNeg Zero Pos

Neg N N Z

Zero N Z P

Pos Z P P

Rule base

1

2

3

4

5

6

7

8

9


Waveforms

for the

phase

control

circuit

CH3 - the analog control voltage applied at pin 11 of the TCA785 IC, 4.8V

CH2 - the ramp voltage, generated by the TCA785 IC, at pin 10

CH1 - the positive voltage pulse generated by the TCA785 at pin 15, to be applied in the gate of the

SCR to set it on (ch1, yellow); the voltage pulse is generated when the ramp voltage exceeds the

analog control voltage

CH4 - the almost sinusoidal supply voltage, in the secondary of the line transformer; the moment

when the SCR switches on (when the positive pulse appears in its gate) is obvious on the waveform –

the voltage decreases due to the large current ensured through the 2Ω heating resistor


1

9 Waveforms

for the

power

circuit

CH1 - the supply voltage, in the secondary of the line transformer

CH2 - the voltage drop across the SCR

MATH - the voltage drop across the heating resistor


20

0 500 1000 1500 2000 250025

30

35

40

45

50

time [s]

tem

pera

ture

[C

]

Tref

T

Experimental results, Tref = 45oC

process perturbation:

opening the thermal enclosure


21

0 500 1000 1500 2000 250020

40

60

tem

p

0 500 1000 1500 2000 2500-20

0

20

err

0 500 1000 1500 2000 2500-20

0

20

cerr

0 500 1000 1500 2000 2500-1000

0

1000

du

0 500 1000 1500 2000 25000

2000

4000

u



0 500 1000 1500 2000 250020

40

60te

mp

0 500 1000 1500 2000 2500-1000

0

1000

du

0 500 1000 1500 2000 25000

2000

4000

u

0 500 1000 1500 2000 25000

2

4

con

tr.

vo

ltag

e




0 100 200 300 400 500 600 700 800 90026

28

30

32

34

36

38

time [s]

tem

pera

ture

[C

]

Tref

T


Experimental results, Tref = 34oC from 37oC

0 200 400 600 800 1000 120033.5

34

34.5

35

35.5

36

36.5

37

time [s]

tem

pera

ture

[C

]

Tref

T


0 200 400 600 800 1000 120030

35

40te

mp

0 200 400 600 800 1000 1200-1000

0

1000

du

0 200 400 600 800 1000 12002000

4000

6000

u

0 200 400 600 800 1000 12002

3

4

con

tr.

vo

ltag

e

Experimental results, Tref = 34oC from 37oC


2.

IMPLEMENTAREA

UNUI SISTEM DE

CONTROL AL TEMPERATURII

UTILIZÂND

MATLAB


Controler fuzzy

reguli definite de utilizator

fiecărei reguli îi corespunde o mulţime parţială de ieşire

∆e = e(k) - e(k-1)

∑ ieşire+

-∑

Controler fuzzy

Întârziere

∆t

senzor

Element de control

*y e

∆e

cu

y

yye *

Fundamentare teoretică


Implementare practică

Incinta termică Platforma EEboard

CAN

CNA

Senzor

Execuţie

Prelucrare

analogică pentru

achiziţie

Placa de sunetMATLAB/

Simulink

Prelucrare

analogică pentru

comandă

Flux comandă

Flux achiziţie

Controler

fuzzy

Placă de sunet - limitare

cuplaj capacitiv

valori tensiune: [-1V; +1V]

- amplificare

achiziţie 3:1

comandă 1:1

Schema de principiu


Incinta termică

Senzor de temperatura LM35

factor de scală liniar +10 mV/°C (ex. 30°C 300mV)

măsurarea temperaturii în intervalul -55°C,+125°C

temperatura citită - diferită cu 0.01 °C de temperatura suprafeţei

precizie 0.5 °C (la +25°C )

tensiune de alimentare - între 4V şi 30V

Rezistenţă termică două rezistenţe ceramice conectate în paralel

Implementare practică

echivR

UP

2

max


Platforma Electronic Explorer

Flux de achiziţie

Implementare

LM741

+

-

V+

V-

OUT LM555 GND

TRIGGEROUTPUTRESET

CONTROLTHRESHOLDDISCHARGE

VCC

Vee

9

0

Vcc

1k

Vcc

GND

Vcc

1k

2.2u

0

Vcc

-9

0

GND

Vee

Vee

11k

1k

0

LM35

Vcc OUT

GN

D

GND

Vcc

LM741

+

-

V+

V-

OUT

3k

0

Vcc

Vcc

1k

1k

47n

0

Amplificator Av=12 Repetor (buffer)

Senzor de

temperatură

Conversie

cc ca


Implementare – cont.

Flux de comandă

Detector de vârf pozitivAmplificator

Av = 6.5

Tranzistor

Darlington

Rezistenţa

de încălzire

9

0

Vcc

-9

0

Vee

LM741

+

-V

+

V-

OUT

LM741

+

-

V+

V-

OUT

D

10u 27k

6.8k

Q1

2N2221

22k

Q2

BD237

0

0 0

5.5k

1k

0

+12

0

Vsursa Vcc

Vee

Vee

Vcc

Vsursa

6,822


Schemă SimulinkImplementare


Controler fuzzy T-S

Variabile de intrare: e, de

3 mulţimi de tip zmf, gauss, smf

Variabila de ieşire: du

3 mulţimi singleton: Ne = -0.2

Ze = 0

Po = 0.2

Baza de regulie

de

N Z P

N Ne Ne Ze

Z Ne Ze Po

P Ze Po Po


Exemplu de activare a regulilor

Reguli activate

– 5: Dacă (e este Z) şi (de este Z) atunci (du este Ze)

– 8: Dacă (e este P) şi (de este Z) atunci (du este Po)

Defuzzificare - medie ponderată

85

8855*

zzy


Rezultate experimentale

Semnalul preluat de la 555

Semnalul preluat de la senzor

Semnalul transmis spre Simulink

Flux de achiziţie


Semnalul provenit din Simulink

Semnalul de la iesirea detectorului de vârf

Rezultate experimentaleFlux de comandă


Evoluţia temperaturii incintei:Tref=45°C

Rezultate experimentale


Rezultate experimentaleEvoluţia semnalelor: Tref = 45°C

Te

cu →

maxcu

cdue → 0

cu

e, cdu

cu, → const.


Rezultate experimentaleEvoluţia temperaturii incintei:Tref-variabilă

45°C 37°C


3. Implementation of a Fuzzy

Logic-Based Embedded System for Engine RPM

Control


Introduction

implements an embedded system for the

Engine RPM control based on a

development board

developed around an Arduino Mega board

fuzzy logic system as controller

offers an easy understanding of the main

concepts regarding embedded systems


System implementationBlock diagram


DC-Motor: Gear ratio: 30:1

Free run speed at 6V: 1000RPM

Free run current at 6V: 120mA

Stall current at 6V: 1600mA


Quadrature Encoder:

• Six pole magnetic disk +PCB

• Dual Channel

• 12 counts/revolution

• 2.8V -18V

Output signal

of the encoder


Motor Driver:

• L298

• Middle class

• 2 Motors

• Sensors power supply


ArduinoMega

Pinout LCD screen


The Arduino Mega board is the “brain” of the entire system. It is primarily

responsible for the update of the digital control signal u, at every time

instance. Therefore, the actual RPM, RPMk is read and the actual RPM error

(errk) and change of RPM error (cerrk) are updated, as follows:

(1)

where is the RPM error in the previous time instance.

The star of the entire system is the fuzzy logic controller, whose role is to

infer the best modification in the control signal, in every time instance . The

operation of the fuzzy logic controller is explained later on. The digital

version of the actual control signal is updated using the relation:

(2)


Compute RPMTo obtain the actual RPM:

a method based on a fixed time interval (time window) to

count the revolutions of the main motor shaft.

a counter is triggered at the initial time ti and it counts the

pulses received from the Hall effect sensor up to the final

increment tf.

The RPM is computed using the relation :

Cf - final value of the counter

Ci - initial value of the counter

Cr = 12, number of counts/revolution

Gr = 30, the gear ratio (30:1)

tf, ti – are measured in seconds


The Fuzzy Logic Controller

first-order Takagi-Sugeno

two inputs errFls and cerrFls

one output ΔuFls

Fuzzy sets for the inputs Fuzzy sets for the output


errFls

cerrFls Neg Zero Pos

Neg N N Z

Zero N Z P

Pos Z P P

Rule base of the

fuzzy logic system

Block diagram of the fuzzy logic controller


The defuzzification method, used to transform the partial output fuzzy sets resulted from the inference process into a crisp value is the weighted average method.


Control surface of the fuzzy logic controller

ou

tpu

t

cErr

Err


Control Circuit

Fuzzy

logic

system cerr + _

+ _

RPM ref

z

1

ΔuFls

- +

z

1

u

RPM

errFls

cerrFls

0

255 -1

+1

-1

+1 su

err se

sc

Δu Motor

Driver

DC

Motor ua


System setup


Experimental resultsRPM from 0 to 1000

rise time = 8.8 s;

max. positive error = 5 rpm ;

max. negative error = 5rpm;

RPM from 1000 to 500

fall time = 6.75 s;



RPM from 500 to 750

rise time = 4.75 s;



RPM from 750 to 0

fall time = 6.5 s;


To drastically decrease the time response of the control system,

the control strategy should be slightly modified.

Because the control characteristic of the DC motor driven by

the H-Bridge is almost liner, when a large variation of the motor

speed is required (larger than 60 rpm), the control signal is not

determined by the fuzzy logic system, but it is estimated by a

simple linear interpolation, that acts as a course adjustment of

the control signal.

Then, the fuzzy logic system regains its role for the fine

adjustment of the speed.

Decreasing the time response


Tracking mode operation: RPM tracks the temperature variation


Duty Cycle

23%

Low Speed

55%

Medium Speed

90%

High Speed


4.

Controlul

pendulului

inversat


4. Controlul pendulului inversat –

demonstratie video

pendul.fis

http://www.razorrobotics.com/articles/fuzzy-control-system/

1. · Phase control TCA 785 Control thyristors, triacs, and transistors. The trigger pulses can be...

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Transcript of 1. · Phase control TCA 785 Control thyristors, triacs, and transistors. The trigger pulses can be...