Integrated sensing and modeling on a sensor node Yeonjeong Park and Tom Harmon UC Merced...
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Transcript of Integrated sensing and modeling on a sensor node Yeonjeong Park and Tom Harmon UC Merced...
Integrated sensing and modeling on a
sensor node
Yeonjeong Park and Tom HarmonUC Merced Environmental Systems program
Outline• Why do this?• Moisture, specific conductivity, and temperature
sensing in soils• A closed-loop system demonstration pilot scale• Demonstration at full scale
Motivation
• Several reasons for local, automated analysis
– Sensor system design (optimize numbers, locations of sensors while you are installing them)
– Feedback-control algorithms: observe, model, forecast, control, …, observe [emphasis of this presentation]
• Computations locally or remotely?
– If speed is not an issue, than remote computations may be important
Creating higher order virtual sensors
• We notice that data analysis can become routine with arrays of individual sensors
– Energy balances– Water balances– Metabolism– Mixing
• Let the sensor array behave as a more sophisticated “sensor”
“Salinity flux” sensor
Example: Irrigation in the Mojave Desert
Typical sensor array for field testing
• Sensors– Moisture– Temperature– Soil salinity– (also meteorology)
Decagon 5TE
These sensors are robust (much testing in agriculture)
Moi
stu
re (
v/v)
25 cm blue50 cm red100 cm black
Irrigation control “sensor”
Coupling sensors readings with models(compressing the timeframe for analysis)
• Palmdale water reuse experimental site (not in the dairy site, but could be…)
• Microclimate + soil pylons (moisture, temp, short-term nitrate and ammonium)
• sensor feedback, model calibration, model forecast
• After a reasonable amount of time, the model parameters become stable
Pilot demonstration: Sensor-trained simulation model with a management model (feedback-control)
• Receding horizon control• Optimize irrigation rate for
current system state and future states
• Execute the best estimate for the current state and move the management horizon forward, repeating…
Step 1: observe and model
Sample model fits (all at 5 cm depth,
different management steps)
Soi
l moi
stur
e (c
m3 /cm
3 )
Management step 20.225
0.22
0.215
0.21
0.205
0.2
time (min)0 5 10 15 20 25 30
estimated
measured
estimated
measured
time (min)
estimated
measured
estimated
measured
Management step 10.214
0.212
0.21
0.208
0.206
0.204
0.202
0.2
0.198
0.196
0.1940 5 10 15 20 25 30
Soi
l moi
stur
e (c
m3 /cm
3 )
Soi
l moi
stur
e (c
m3 /cm
3 )
Management step 40.245
0.24
0.235
0.23
0.225
0.22
0.215
0.21
time (min)0 5 10 15 20 25 30
estimated
measured
estimated
measured
time (min)
estimated
measured
estimated
measured
Management step 30.225
0.22
0.215
0.21 0 5 10 15 20 25 30
Soi
l moi
stur
e (c
m3 /cm
3 )
Soi
l moi
stur
e (c
m3 /cm
3 )
Management step 60.246
0.244
0.242
0.24
0.238
0.236
0.234
0.232
0.23
0.228
0.226
time (min)
estimated
measured
estimated
measured
time (min)
estimated
measured
estimated
measured
Management step 5
0.245
0.24
0.235
0.23
0.225
0.220 5 10 15 20 25 30
Soi
l moi
stur
e (c
m3 /cm
3 )
0 5 10 15 20 25 30
Note: model is a coupled flow, mass and energy transport model (one-dimensional, 2 soil layers assumed)
Receding Horizon Controlqk | k qk+1| k
• • •
qk+1 | k+1 • • •
qk+2 | k+2 qk+3 | k+2 • • •
k
k
k+2
k+1
k+1 k+2
• • •
qk+2| k+1 k+2 • • •
• • •
k
k
k+2
k+1
k+1 k+2
Optimization Horizon
Next Optimization
Next Optimization
The first Optimal Values
Optimal Sequence
qk | kqk | k qk+1| k qk+1|qk+1| k • • •
qk+1 | k+1qk+1 | k+1 • • •
qk+2 | k+2qk+2 | k+2 qk+3 | k+2 qk+3 | k+2 • • •
k
k
k+2
k+1
k+1 k+2
• • •
qk+2| k+1 k+2qk+2| k+1 k+2 • • •
• • •
k
k
k+2
k+1
k+1 k+2
Optimization Horizon
Next Optimization
Next Optimization
The first Optimal Values
Optimal Sequence
Nonlinear optimization algorithm producing an array of future control actions (here, irrigation rates)
q1
q6
q5q4
q3q2
Optimization Horizon (36hr)
Management Step (6hr)
Op
tim
al A
pplic
atio
n R
ate
(cm
/hr)
10min
q1
q6
q5q4
q3q2
Optimization Horizon (36hr)
Management Step (6hr)
Op
tim
al A
pplic
atio
n R
ate
(cm
/hr)
10min
0 100 200 3000.23
0.235
0.24
0.245
0.25
Depth (cm)
Soi
l moi
stur
e (c
m3 /c
m3 )
0 10 20 30 40 500
0.2
0.4
0.6
0.8
Management step
Opt
imal
val
ue
0 10 20 30 40 504
5
6
7
8
Management step
appl
icat
ion
rate
(cm
/hr)
0 10 20 30 40 500.21
0.22
0.23
0.24
0.25
0.26
Management stepMax
imum
wat
er c
onte
nt (
cm3 /c
m3 )
Upper Bound
Lower Bound: zero
Threshold (0.25)
(a)
(d)
(b)
(c)
Soil Moisture ControlVariable Application Rate of Fixed Frequency and Duration
q is fixed
Applic
atio
n r
ate
(cm
/hr)
Management step is
changing
Optimization horizon
td1
tdi is duration of irrigation, where i=1,…,4
tmg is update time
td2 td3 td4tmg
q is fixed
Applic
atio
n r
ate
(cm
/hr)
Management step is
changing
Optimization horizon
td1
tdi is duration of irrigation, where i=1,…,4
tmg is update time
td2 td3 td4tmg 0 10 20 30 40 50
0
0.1
0.2
0.3
0.4
0.5
Management step
time
inte
rval
(hr
)
0 10 20 30 40 500
0.5
1
1.5
Management step
Opt
imal
val
ue
0 100 200 3000.27
0.28
0.29
0.3
0.31
Depth (cm)
Soi
l Moi
stur
e (c
m3/
cm3)
0 10 20 30 40 500.15
0.2
0.25
0.3
0.35
Management step
Soi
l Moi
stur
e at
2ft
(cm
3/cm
3)
(a) (b)
(c) (d)
Upper Bound
Lower Bound
Threshold 0.3
Soil Moisture ControlFixed Application Rate of Variable Frequency and Duration
…and trying feedback-control in the field
• Center pivot irrigation system• Manually control by changing the
rotational speed (not automated)• 3 speeds to simplify the objective space
Park, Shamma, & Harmon (2009) Environ Modell Softw, in press
0 1 2 3 4 5 6 70.195
0.2
0.205
0.21
0.215
0.22
0.225
management step
soil
moi
stur
e at
5 c
m d
epth
(cm
3 /cm
3 )
threshold
4 min
6 min 8 min(4 minapplied)
8 min6 min
6 min 4 min
0 2 4 6 8 10 12 140.19
0.2
0.21
0.22
0.23
0.24
0.25
time (hr)
soil
mois
ture
at
5 c
m d
epth
(cm3 /c
m3 )
estimated
measured
4 min
6 min
8 min(4 minapplied)
8 min 6 min 6 min
4 min