Presentation of my M.Sc. graduation work, titled "Determination of canal pool characteristics with experimental modeling". It describes the use of System Identification techniques to identify (irrigation) canal pool characteristics needed for automatic controller design from tests on the real canal rather than on model representations. There is a special focus on potential resonance behavior of canal pools and the identification of these characteristics. Experiments on irrigation canal pools in Arizona, USA are described.

1. Determination of canal characteristics
with experimental modeling
Graduation presentation 0.7
0.6
Ivo Miltenburg 0.5
Amplitude
0.4
09-04-2008 0.3
0.2
0.1
0
-20 0 20 40 60 80 100 120 140
Time (10 second interval)
Input Flow (m3/s)
0.3 0.7
0.6
0.2
0.5
Magnitude (abs)
0.1
0.4
06:00 06:30 07:00 07:30 Rp
Time 0.3
0.04 0.2
deviation (m)
Waterlevel
0.02
0.1 ωp
0
0
-3 -2 -1
-0.02 10 10 10
Frequency (rad/s)
06:00 06:30 07:00 07:30
Time 1600
Estimated storage area (m )
1400
2
1200
Prof. dr. ir. Nick van de Giesen 1000
Prof. dr. ir. Xavier Bombois 800
Dr. ir. Peter-Jules van Overloop 600
Dr. Bert Clemmens 400
10
-4
10
-3
10
-2
Frequency (rad/s)
Ir. Robert Strand

2. Introduction
● Operational Water Management
● Irrigation canal systems
● Optimization crop growth
R3 and water scarcity
R4 – Flexibility
R5 – Efficiency
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 2
with experimental modeling

3. Introduction
Automation of irrigation systems
● Different types of control possible set point
desired
Slave controller flow rate Master controller
Flow control Level control
controlled
u water level
y
● Proportional Integral (PI) control
most widely used
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 3
with experimental modeling

4. Introduction
Tuning based on canal characteristics
• Long, shallow, steep canal pools
- Delay time
- Storage area
1
10
A
Magnitude (abs)
0
10
Integrator
ID model
Delay time (reservoir) -1
10
(transport) -4 -3 -2
10 10 10
• Pool characteristics can be determined relatively simple
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 4
with experimental modeling

5. Introduction
Tuning based on canal characteristics
• Short, deep, flat canal pools
- Delay time
- Storage area
- Resonance behavior
• What is resonance??
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 5
with experimental modeling

6. Intermezzo - Resonance
• System sensitive to input of certain frequency
• In a canal: standing wave
• Wave action distorts perception of water level
First harmonic Second harmonic
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 6
with experimental modeling

7. Introduction
Tuning based on canal characteristics
• Resonance waves can lead to loose control or even instability:
Water level (m)
1.22
1.2
1.18
00:00 02:00 04:00 06:00 08:00 10:00
Time
Resonance behavior in real canal - Delay time
- Storage area
- Resonance frequency
ID model - Resonance peak
low frequency
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 7
with experimental modeling

8. Introduction
Tuning based on canal characteristics
Apply PIF-control
1
10
●
0
10
Magnitude (abs)
– Filter water levels:
-1
10
Original model
Filter
-2
10 Filtered model
-4 -3 -2 -1
10 10 10 10
– PIF tuning rules for distant downstream control
• How to determine resonance characteristics?
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 8
with experimental modeling

9. Introduction 0.17
F w(m /s)
∆Q
3
0.16
∆Q
0.15
lo
Visualization Resonance 0.14
02:00 03:00 04:00
Time
05:00 06:00
Chirp input signal 1.25
W te e th (m)
● 1.2
∆h
∆h
a rd p
1.15
1.1
02:00 03:00 04:00 05:00 06:00
● Time
Low flow High flow
1.42
0.17
Flow (m3/s)
Flow (m3/s)
1.41
0.16
1.4
0.15
1.39
0.14
00:00 03:00 06:00 00:00 03:00 06:00
Time Time
1.25
Waterdepth (m)
1.2
1.15
1.1
1.05
00:00 01:00 02:00 03:00 04:00 05:00 06:00
Time
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 9
with experimental modeling

10. Introduction
Visualization Resonance
SOBEK chirp test Real chirp test
● Simple determination not possible
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 10
with experimental modeling

11. Introduction
Tuning PIF-controller based on canal characteristics
● Calculation
● Use hydrodynamic model:
– Model construction
– Tedious and
time consuming
– Possibly inaccurate
● Model errors
● Changing system
● Existing methods can not be used on a real canal pool
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 11
with experimental modeling

12. Objective
Research question:
● What is the potential of System Identification for controller
design for various irrigation canal pools?
● How can the resonance characteristics in
(potentially oscillating) irrigation pools
be identified
Research objective:
• Finding a procedure to identify all
relevant canal pool characteristics for
controller design using experimental
modeling
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 12
with experimental modeling

13. System Identification
● Apply known disturbances and
measure output
● Capture the relevant dynamics
● Determine canal characteristics
General System Identification procedure ● Controller and filter design
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 13
with experimental modeling

14. System Identification
Model set
● Model type
– System transfer function
– Error transfer function
Box-Jenkins (BJ) model structure most flexible
● Model order
– Reservoir behavior Step Response
– Resonance behavior 1
– Initial water level jump Amplitude
0.5
– Discrete data
0
5th order model structure 0 100 200 300 400
tim (sec)
e
500 600 700 800
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 14
with experimental modeling

15. System Identification
Experiment design
● Open loop experiment ● Flow control gate discharge
Input signal
● RBS signal
0.4
Frequency range
Input Flow (m3/s)
● 0.3
Step size
Base flow
● Test base flow 0.2
0.1
● Test step size 00:00 01:00 02:00 03:00
Time
● Control time step
● Sample time step
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 15
with experimental modeling

16. System Identification
Input design
● ●
●
●
●
● ●
●
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 16
with experimental modeling

17. System Identification
Determination canal characteristics
● Delay time ● Storage area
0.7 – Filter data
0.6
0.5
m
A
u
p
d
e
t
l
i
0
.
6
5
0
.
4
3
0.05
Amplitude
0.4
deviation (m)
Waterlevel
0.3
0
0.2
0.1
-0.05
0
-20 0 20 40 60 80 100 120 140
Time (10 second interval)
00:00 00:30 01:00 01:30 02:00 02:30 03:00 03:30 04:00
● Resonance characteristics
0.7
0.6
1600
0.5
Estimated storage area (m2)
1400
Magnitude (abs)
0.4
1200
Rp
0.3
1000
0.2 800
0.1 600
ωp
0 400
-3 -2 -1
10 10 10 -4 -3 -2
10 10 10
Frequency (rad/s) Frequency (rad/s)
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 17
with experimental modeling

18. Model and field experiments
Test location
● Arizona, USA
● Pinal County
● CAIDD
NB 11
NB lateral
NB 11A
NB 12
NB 13
NB 14
NB 16
NB 17
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 18
with experimental modeling

19. Model and field experiments
Real chirp test SOBEK chirp test Real Resonance peak
10
0 R e a l C h irp T e s t 1
0.04 0.04 R e a l C h irp T e s t 2
R e a l C h irp T e s t 3
Waterlevel deviation (m)
Waterlevel deviation (m)
Amplitude
0.02 0.02
0 0
-1
10
-0.02 -0.02
-4 -3 -2 -1
10 10 10 10
-0.04 -0.04 F re q u e n c y (ra d / )
00:00 02:00 04:00 06:00 00:00 02:00 04:00 06:00
Time Time
SOBEK Resonance peak
0
Waterlevel deviation (m)
Waterlevel deviation (m)
10
0.02 0.02
0 0
Amplitude
-1
10
-0.02 -0.02
M o d e l C h irp T e s t 1
00:00 02:00 04:00 06:00 00:00 02:00 04:00 06:00 M o d e l C h irp T e s t 2
Time Time M o d e l C h irp T e s t 3
-2
10
-4 -3 -2 -1
10 10 10 10
F re q u e n c y (ra d / )
Waterlevel deviation (m)
Waterlevel deviation (m)
0.02 0.02
0 0
● Consistent results
-0.02 -0.02
● Difference SOBEK/real
00:00 01:00 02:00 03:00 00:00 01:00 02:00 03:00
Time Time
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 19
with experimental modeling

20. Model and field experiments
Upstream RBS-tests
Input Flow (m3/s)
0 .4
0 .2
0
06 :00 06 :30 07 :00 07 :30
Resonant sensitive pools 0.0 5
Tim e
deviation (m)
Waterlevel
0
-0 . 0 5
06 :00 06 :30 07 :00 07 :30
Tim e
Input Flow (m3/s)
0 .4
0 .3
0 .2
0 .1
Delay time dominant pools 0 1 :3 0 0 2 :0 0
T im e
0 2 :3 0 0 3 :0 0
0 .0 6
deviation (m)
Waterlevel
0 .0 4
0 .0 2
0
-0 . 0 2
0 1 :3 0 0 2 :0 0 0 2 :3 0 0 3 :0 0
T im e
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 20
with experimental modeling

21. Model and field experiments
Upstream RBS-tests
Real US RBS-tests SOBEK US RBS-tests
Characteristics NB-12 NB-11A Characteristics NB-12 NB-11A
Delay time τ 330 770 [s] Delay time τ 320 720 [s]
not
Storage Area As 2750
possible [m ]
2 Storage Area As 2950 2100 [m ]
2
Resonance period TP 750 - [s] Resonance period TP 680 - [s]
Resonance frequency ωP 0.008418 - [ rad /s] Resonance frequency ωP 0.009257 - [ rad /s]
−2
Resonance peak RP 0.29 0 [s⋅m ]
−2 Resonance peak RP 0.34 0 [s⋅m ]
● Procedure works
● Difference SOBEK/real,
but consistent
● Experiments can fail
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 21
with experimental modeling

22. Conclusions
● Resonance plays an important role
● 5th order BJ-model captures all relevant dynamics
● Pool characteristics can be estimated
● Resonance peak and storage area sensitive to flow conditions
● Resonance frequency stable
● Different pool behavior for US and DS disturbance (culvert)
● Differences between SOBEK/real experiments stipulate the
importance of experimental modeling
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 22
with experimental modeling

23. Recommendations
● Research closed loop experiments
● Investigate difference between US-DS canal pool reaction
● Apply PIF-control on a real canal
● Investigate ‘plug-and-play’-control based on proposed
procedure
Introduction – Objective – System Identification – Model and field experiments – Conclusions/recommendations
Determination of canal characteristics 23
with experimental modeling

24. “Only a fool measures the depth of the water with both feet”
- African proverb -
Determination of canal characteristics 24
with experimental modeling

25. Thanks for your attention
Drinks:
Delftsche Studenten Bond (DSB)
Oude Delft 123
Determination of canal characteristics 25
with experimental modeling