Hur får man system att
uppföra sig som man vill?
Reglerteknik och intressanta
tillämpningar
Carl-Fredrik Lindberg, 2013-04-25
Single and Double Inverted Pendulum
Movie found on internet http://vimeo.com/2952236
2
Automatic Control (“Reglerteknik” in Swedish)
Modeling the tank dynamics
𝑑
𝐴ℎ =𝑢−𝑞
𝑑𝑡
Tuning the PID controller
𝑡
𝑢 𝑡 = 𝐾𝑝 𝑒(𝑡) + 𝐾𝐼
0
𝑒(𝑠) 𝑑𝑠 + 𝐾𝐷
𝑑
𝑒 𝑡
𝑑𝑡
● Automatic control gives:
●
●
●
●
Stability
Fast disturbance rejection
Small variations in controlled variables
Etc.
3
How a control engineer sees the world
Feedforward
Setpoints
Controller
Measureable
disturbances
Non
measureable
disturbances
Measurement
noise
Control
signals
Process
Σ
Measured
outputs
Feedback
4
Automatic Control
● Modeling
● Physical models, grey-box, black-box, …
● Controller design
● Feedback, feedforward, stability, performance,
robustness, adaptive, …
● Estimation
● States, model parameters, …
● Diagnostics
● Fault detection and isolation, …
● Optimization
5
Project examples
6
EM Stabilizer in Galvanizing Lines
Stabilization of strip gives
reduced zinc over-coating
7
EM Stabilizer in Galvanizing Lines
Improved strip position damping with EM Stabilizer
8
Water model
EM Control
Control of the steel flow in the
mold gives higher steel quality
Continuous casting process
Validation of model with
PIV measurements in
water model
Simulation of the flow
9
Paper Machine Steam Energy Fingerprint
By quantifying steam flows and identifying energy
waste, suggestions on how to improve energy
efficiency are given
Histogram for ton steam / ton dry paper for different grades (during 19 days)
50
Lightest
Light
Heavy
Heaviest
45
40
35
%
30
25
20
15
10
5
0
10
1.8
1.9
2
2.1
2.2
ton steam / ton dry paper
2.3
Arc Furnace Control
By control, optimization and
EMS, the yield, energy efficiency
and productivity is increased
11
SVC Voltage Control with Feedforward
Improved disturbance rejection after TSC switching
12
Wireless Control
● New possibilities with wireless control
● Increased flexibility to install sensors
● Cost for wiring disappears
● Large challenges
●
●
●
●
Reliability
Latency
Safety
Co-existence with other wireless systems
● 90% of the communication could often
be saved by smarter sampling
● Different sampling strategies have been
developed
● Sample as seldom as possible without
losing control performance
● Taking into account that information
packages may be delayed or lost
Step response with event based sampling
2
r
y
u
1.5
1
0.5
0
-0.5
-1
0
50
100
150
200
250
200
250
time [s]
Sampling time
20
15
10
5
0
0
50
100
150
time [s]
13
Diagnostics
Control loop diagnostics
● On-line methods which automatically alarms
for poor control loop performance.
● Examples
● Detection of oscillations and changed signal
variance.
● Control performance measurements, Harris index
etc.
● Classification of some faults, e.g. valve stiction,
external disturbances.
Process section diagnostics
● Faults are both detected and isolated
● Fault examples:
●
●
●
●
Detection of bias in a sensor
Detection of clogging of a valve
Detection of erosion in a pump
Track heat transfer rate in heat
exchanger
● Benefits:
● Early detection gives higher availability
● Reduced maintenance costs.
● Avoids wrong dosing when sensor has
drifted.
Example of fault
isolation. The
red bar indicates
the most
probable faulty
signal.
14
Waste Water Treatment
Some examples from my Ph.D.
Effluent nitrate in line 1 & 2, ammonium and DO set-point in line 1
20
18
16
Adaptive control of external carbon flow rate
14
Nitrate 1
Nitrate 2
Ammonium
DO set-point
mg/l
12
10
8
6
4
2
0
0
1
2
3
4
time [days]
5
6
7
8
Control of dissolved oxygen concentration
Estimation of dissolved oxygen dynamics
𝑦 𝑡 + 1 = 𝑦 𝑡 + ℎ∗
𝑄 𝑡
𝑉
𝑦𝑖𝑛 𝑡 − 𝑦 𝑡
+ 𝐾𝐿 𝑎 𝑢 𝑡
𝑦𝑠𝑎𝑡 − 𝑦 𝑡
− 𝑅(𝑡)
15
Thank you!
Questions?
16