def ETT_data_get(name,
dir_placement=None,
force_download=False,
split_data=True,
include_time_in_u=False,
full_return=False):
url = name
file_name = url.split('/')[-1]
download_size = None
save_dir = cashed_download(url,'beihang', dir_placement=dir_placement,\
download_size=download_size,force_download=force_download,zipped=False)
file_loc = os.path.join(save_dir, file_name)
ETT, target, loads, times, time = load_cor(file_loc)
if full_return:
return ETT, target, loads, times, time
u = loads
if include_time_in_u:
u = np.concatenate([u, times], axis=1)
y = target
sys_data = System_data(u=u, y=y, dt=15 / 60 / 24)
return sys_data.train_test_split(split_fraction=4 /
20) if split_data else sys_data
def EMPS(dir_placement=None,
vir_as_u=True,
force_download=False,
split_data=True):
'''The Electro-Mechanical Positioning System is a standard configuration of a drive system for prismatic joint of robots or machine tools. The main source of nonlinearity is caused by friction effects that are present in the setup. Due to the presence of a pure integrator in the system, the measurements are obtained in a closed-loop setting.
The provided data is described in this link. The provided Electro-Mechanical Positioning System datasets are available for download here. This zip-file contains the system description and available data sets .mat file format.
Please refer to the Electro-Mechanical Positioning System as:
A. Janot, M. Gautier and M. Brunot, Data Set and Reference Models of EMPS, 2019 Workshop on Nonlinear System Identification Benchmarks, Eindhoven, The Netherlands, April 10-12, 2019.
Special thanks to Alexandre Janot for making this dataset available.'''
#q_cur current measured position
#q_ref target/reference potion
#non-linear due to singed friction force Fc ~ sing(dq/dt)
#t time
#vir applied the vector of motor force expressed in the load side i.e. in N;
# url = 'http://www.nonlinearbenchmark.org/FILES/BENCHMARKS/EMPS/EMPS.zip'
url = 'https://drive.google.com/file/d/1zwoXYa9-3f8NQ0ohzmjpF7UxbNgRTHkS/view'
download_size = 1949929
save_dir = cashed_download(url,
'EMPS',
zip_name='EMPS.zip',
dir_placement=dir_placement,
download_size=download_size,
force_download=force_download)
matfile = loadmat(os.path.join(save_dir, 'DATA_EMPS.mat'))
q_cur, q_ref, t, vir = [
matfile[a][:, 0] for a in ['qm', 'qg', 't', 'vir']
] #qg is reference, either, q_ref is input or vir is input
out_data = System_data(u=vir, y=q_cur) if vir_as_u else System_data(
u=q_ref, y=q_cur)
return out_data.train_test_split() if split_data else out_data
def flexible_structure(dir_placement=None,
force_download=False,
split_data=True):
'''Contributed by:
Maher ABDELGHANI
IRISA-INRIA
Campus de Beaulieu
35042 Rennes cedex
FRANCE
[email protected]
Description:
Experiment on a Steel Subframe Flexible
structure performed at LMS-International,
Leuven-Belgium.
-Structure suspended with flexible rubber bands.
-2 shakers at 2 locations were used for force input signals.
- 28 accelerometers around the structure were used for measurements.
- The 30 channels were simulataneously measured using the LMS-CadaX
Data Acquisition Module.
Sampling:
1/1024 (s)
Number:
8523 samples/channel
Inputs:
2 inputs:
u1= White noise Force
u2=White noise force.
Outputs:
28 outputs:
Accelerations
References:
1. M.Abdelghani, M.Basseville, A.Benvensite,"In-Operation Damage
Monitoring and Diagnosis of Vibrating Structures, with Application to
Offshore Structures and Rotating Machinery", IMAC-XV Feb.3-6 1997,
Fl. USA.
2. M.Abdelghani, C.T.Chou, M. Verhaegen, "Using Subspace Methods for the
Identification and Modal Analysis of Structures", IMAC-XV,
Feb.3-6 1997, Fl.USA.
Properties:
Frequency Range: 10-512 Hz.
Columns:
colomn1= input1 (u1)
colomn2=input2 (u2)
colomns3--30: outputs1--28
Category:
Mechanical Structure
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/mechanical/flexible_structure.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0], y=data[:, 1])
return data.train_test_split() if split_data else data
def steamgen(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Jairo Espinosa
ESAT-SISTA KULEUVEN
Kardinaal Mercierlaan 94
B-3001 Heverlee Belgium
[email protected]
Description:
The data comes from a model of a Steam Generator at Abbott Power Plant in Champaign IL.
The model is described in the paper of Pellegrineti [1].
Sampling:
3 sec
Number:
9600
Inputs:
u1: Fuel scaled 0-1
u2: Air scaled 0-1
u3: Reference level inches
u4: Disturbance defined by the load level
Outputs:
y1: Drum pressure PSI
y2: Excess Oxygen in exhaust gases %
y3: Level of water in the drum
y4: Steam Flow Kg./s
References:
[1] G. Pellegrinetti and J. Benstman, Nonlinear Control Oriented Boiler Modeling -A Benchamrk Problem for Controller Design, IEEE Tran. Control Systems Tech. Vol.4No.1 Jan.1996
[2] J. Espinosa and J. Vandewalle Predictive Control Using Fuzzy Models Applied to a Steam Generating Unit, Submitted to FLINS 98 3rd. International Workshop on Fuzzy Logic Systems and Intelligent Technologies for Nuclear Science and Industry
Properties:
To make possible the open loop identification the wter level was
stabilized by appliying to the water flow input a feedforward action proportional to the steam flow
with value 0.0403 and a PI action with values Kp=0.258 Ti=1.1026e-4 the reference of this controller
is the input u3.
Columns:
Column 1: time-steps
Column 2: input fuel
Column 3: input air
Column 4: input level ref.
Column 5: input disturbance
Column 6: output drum pressure
Column 7: output excess oxygen
Column 8: output water level
Column 9: output steam flow
Category:
Process industry systems
Where:
ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/espinosa/datasets/powplant.dat
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/steamgen.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1:5], y=data[:, 5:])
return data.train_test_split() if split_data else data
def internet_traffic(dir_placement=None,
force_download=False,
split_data=True):
'''Contributed by:
Katrien De C**k
K.U.Leuven, ESAT-SISTA
Kardinaal Mercierlaan 94
3001 Heverlee
[email protected]
Description: one hour of internet traffic between the Lawrence Berkeley Laboratory and the rest of the world
Sampling:
Number: 99999
Inputs:
Output: number of packets per time unit
References:
Katrien De C**k and Bart De Moor, Identification of the first order parameters of a circulant modulated Poisson process. Accepted for publication in the proceedings of the International Conference on Telecommunication (ICT '98)
V. Paxson and S. Floyd, Wide-area traffic: The failure of Poisson modeling, IEEE/ACM Transactions on Networking, 1995
Properties:
Columns:
Column 1: time-steps
Column 2: output y
Category: Time series
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/timeseries/internet_traffic.dat.gz'
dir_name = 'DaISy_data'
save_dir = cashed_download(url,
dir_name,
dir_placement=dir_placement,
download_size=None,
force_download=force_download)
file = os.path.join(save_dir, url.split('/')[-1][:-3])
with open(file) as f:
splitted = '\n'.join(f.read().split('\n')[:-5]) #weird shit
with tempfile.TemporaryFile() as fp:
fp.write(bytes(splitted, 'utf-8'))
fp.seek(0)
data = np.loadtxt(fp)
data = System_data(u=None, y=data[:, 1])
return data.train_test_split() if split_data else data
def powerplant(dir_placement=None, force_download=False, split_data=True):
'''This file describes the data in the powerplant.dat file.
1. Contributed by:
Peter Van Overschee
K.U.Leuven - ESAT - SISTA
K. Mercierlaan 94
3001 Heverlee
[email protected]
2. Process/Description:
data of a power plant (Pont-sur-Sambre (France)) of 120 MW
3. Sampling time
1228.8 sec
4. Number of samples:
200 samples
5. Inputs:
1. gas flow
2. turbine valves opening
3. super heater spray flow
4. gas dampers
5. air flow
6. Outputs:
1. steam pressure
2. main stem temperature
3. reheat steam temperature
7. References:
a. R.P. Guidorzi, P. Rossi, Identification of a power plant from normal
operating records. Automatic control theory and applications (Canada,
Vol 2, pp 63-67, sept 1974.
b. Moonen M., De Moor B., Vandenberghe L., Vandewalle J., On- and
off-line identification of linear state-space models, International
Journal of Control, Vol. 49, Jan. 1989, pp.219-232
8. Known properties/peculiarities
9. Some MATLAB-code to retrieve the data
!gunzip powerplant.dat.Z
load powerplant.dat
U=powerplant(:,1:5);
Y=powerplant(:,6:8);
Yr=powerplant(:,9:11);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/powerplant.dat.gz'
powerplant = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
U = powerplant[:, 0:5]
Y = powerplant[:, 5:8]
Yr = powerplant[:, 8:11]
data = System_data(u=U, y=Y)
return data.train_test_split() if split_data else data
def pHdata(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Jairo Espinosa
K.U.Leuven ESAT-SISTA
K.Mercierlaan 94
B3001 Heverlee
[email protected]
Description:
Simulation data of a pH neutralization process in a constant volume
stirring tank.
Volume of the tank 1100 liters
Concentration of the acid solution (HAC) 0.0032 Mol/l
Concentration of the base solution (NaOH) 0,05 Mol/l
Sampling:
10 sec
Number:
2001
Inputs:
u1: Acid solution flow in liters
u2: Base solution flow in liters
Outputs:
y: pH of the solution in the tank
References:
T.J. Mc Avoy, E.Hsu and S.Lowenthal, Dynamics of pH in controlled
stirred tank reactor, Ind.Eng.Chem.Process Des.Develop.11(1972)
71-78
Properties:
Highly non-linear system.
Columns:
Column 1: time-steps
Column 2: input u1
Column 3: input u2
Column 4: output y
Category:
Process industry systems
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/pHdata.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1:3], y=data[:, 3])
return data.train_test_split() if split_data else data
def evaporator(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
A four-stage evaporator to reduce the water content of a product,
for example milk. The 3 inputs are feed flow, vapor flow to the
first evaporator stage and cooling water flow. The three outputs
are the dry matter content, the flow and the temperature of the
outcoming product.
Sampling:
Number:
6305
Inputs:
u1: feed flow to the first evaporator stage
u2: vapor flow to the first evaporator stage
u3: cooling water flow
Outputs:
y1: dry matter content
y2: flow of the outcoming product
y3: temperature of the outcoming product
References:
- Zhu Y., Van Overschee P., De Moor B., Ljung L., Comparison of
three classes of identification methods. Proc. of SYSID '94,
Vol. 1, 4-6 July, Copenhagen, Denmark, pp.~175-180, 1994.
Properties:
Columns:
Column 1: input u1
Column 2: input u2
Column 3: input u3
Column 4: output y1
Column 5: output y2
Column 6: output y3
Category:
Thermic systems
Where:
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/evaporator.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1:4], y=data[:, 4:7])
return data.train_test_split() if split_data else data
def robot_arm(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
Data from a flexible robot arm. The arm is installed on an electrical
motor. We have modeled the transfer function from the measured reaction
torque of the structure on the ground to the acceleration of the
flexible arm. The applied input is a periodic sine sweep.
Sampling:
Number:
1024
Inputs:
u: reaction torque of the structure
Outputs:
y: accelaration of the flexible arm
References:
We are grateful to Hendrik Van Brussel and Jan Swevers of the laboratory
of Production Manufacturing and Automation of the Katholieke
Universiteit Leuven, who provided us with these data, which were
obtained in the framework of the Belgian Programme on
Interuniversity Attraction Poles (IUAP-nr.50) initiated by the
Belgian State - Prime Minister's Office - Science Policy
Programming.
Properties:
Columns:
Column 1: input u
Column 2: output y
Category:
mechanical systems
Where:
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/mechanical/robot_arm.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0], y=data[:, 1])
return data.train_test_split() if split_data else data
def dryer2(dir_placement=None, force_download=False, split_data=True):
'''
This file describes the data in the dryer.dat file.
1. Contributed by:
Jan Maciejowski
Cambridge University, Engineering Department
Trumpington Street, Cambridge
CB2 1PZ, England.
[email protected]
2. Process/Description:
Data from an industrial dryer (by Cambridge Control Ltd)
3. Sampling time:
10 sec
4. Number of samples:
867 samples
5. Inputs:
a. fuel flow rate
b. hot gas exhaust fan speed
c. rate of flow of raw material
6. Outputs:
a. dry bulb temperature
b. wet bulb temperature
c. moisture content of raw material
7. References:
a. Maciejowski J.M., Parameter estimation of multivariable
systems using balanced realizations, in:
Bittanti,S. (ed), Identification,
Adaptation, and Learning, Springer (NATO ASI Series), 1996.
b. Chou C.T., Maciejowski J.M., System Identification Using
Balanced Parametrizations, IEEE Transactions on Automatic Control,
vol. 42, no. 7, July 1997, pp. 956-974.
8. Known properties/peculiarities:
9. Some MATLAB-code to retrieve the data
!gunzip dryer.dat.Z
load dryer.dat
U=dryer(:,2:4);
Y=dryer(:,5:7);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/dryer2.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1:4], y=data[:, 4:7])
return data.train_test_split() if split_data else data
def glassfurnace(dir_placement=None, force_download=False, split_data=True):
'''This file describes the data in the glassfurnace.dat file.
1. Contributed by:
Peter Van Overschee
K.U.Leuven - ESAT - SISTA
K. Mercierlaan 94
3001 Heverlee
[email protected]
2. Process/Description:
Data of a glassfurnace (Philips)
3. Sampling time
4. Number of samples:
1247 samples
5. Inputs:
a. heating input
b. cooling input
c. heating input
6. Outputs:
a. 6 outputs from temperature sensors in a cross section of the
furnace
7. References:
a. Van Overschee P., De Moor B., N4SID : Subspace Algorithms for
the Identification of Combined Deterministic-Stochastic Systems,
Automatica, Special Issue on Statistical Signal Processing and Control,
Vol. 30, No. 1, 1994, pp. 75-93
b. Van Overschee P., "Subspace identification : Theory,
Implementation, Application" , Ph.D. Thesis, K.U.Leuven, February 1995.
8. Known properties/peculiarities
9. Some MATLAB-code to retrieve the data
!gunzip glassfurnace.dat.Z
load glassfurnace.dat
T=glassfurnace(:,1);
U=glassfurnace(:,2:4);
Y=glassfurnace(:,5:10);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/glassfurnace.dat.gz'
glassfurnace = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
U = glassfurnace[:, 1:4]
Y = glassfurnace[:, 4:10]
data = System_data(u=U, y=Y)
return data.train_test_split() if split_data else data
def thermic_res_wall(dir_placement=None,
force_download=False,
split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
Heat flow density through a two layer wall (brick and insulation
layer). The inputs are the internal and external temperature of
the wall. The output is the heat flow density through the wall.
Sampling:
Number:
1680
Inputs:
u1: internal wall temperature
u2: external wall temperature
Outputs:
y: heat flow density through the wall
References:
- System Identification Competition, Benchmark tests for estimation
methods of thermal characteristics of buildings and building
components. Organization: J. Bloem, Joint Research Centre,
Ispra, Italy, 1994.
Properties:
Columns:
Column 1: input u1
Column 2: input u2
Column 3: output y
Category:
thermic systems
Where:
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/thermic/thermic_res_wall.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0:2], y=data[:, 2])
return data.train_test_split() if split_data else data
def cstr(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Jairo ESPINOSA
ESAT-SISTA KULEUVEN
Kardinaal Mercierlaan 94
B-3001 Heverlee Belgium
[email protected]
Description:
The Process is a model of a Continuous
Stirring Tank Reactor, where the reaction
is exothermic and the concentration is
controlled by regulating the coolant
flow.
Sampling:
0.1 min
Number:
7500
Inputs:
q: Coolant Flow l/min
Outputs:
Ca: Concentration mol/l
T: Temperature Kelvin degrees
References:
J.D. Morningred, B.E.Paden, D.E. Seborg and D.A. Mellichamp "An adaptive nonlinear predictive controller" in. Proc. of the A.C.C. vol.2 1990 pp.1614-1619
G.Lightbody and G.W.Irwin. Nonlinear Control Structures Based on Embedded Neural System Models, IEEE Tran. on Neural Networks Vol.8 No.3 pp.553-567
J.Espinosa and J. Vandewalle, Predictive Control Using Fuzzy Models, Submitted to the 3rd. On-Line World Conference on Soft Computing in Engineering Design and Manufacturing.
Properties:
Columns:
Column 1: time-steps
Column 2: input q
Column 3: output Ca
Column 4: output T
Category:
Process Industry Systems
Where:
ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/espinosa/datasets/cstr.dat
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/cstr.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1], y=data[:, 2:4])
return data.train_test_split() if split_data else data
def foetal_ecg(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Lieven De Lathauwer
[email protected]
Description:
cutaneous potential recordings of a pregnant woman (8 channels)
Sampling:
10 sec
Number:
2500 x 8
Inputs:
Outputs:
1-5: abdominal
6,7,8: thoracic
References:
Dirk Callaerts,
"Signal Separation Methods based on Singular Value Decomposition
and their Application to the Real-Time Extraction of the
Fetal Electrocardiogram from Cutaneous Recordings",
Ph.D. Thesis, K.U.Leuven - E.E. Dept., Dec. 1989.
L. De Lathauwer, B. De Moor, J. Vandewalle, ``Fetal
Electrocardiogram Extraction by Blind Source Subspace Separation'',
IEEE Trans. Biomedical Engineering, Vol. 47, No. 5, May 2000,
Special Topic Section on Advances in Statistical Signal Processing
for Biomedicine, pp. 567-572.
Properties:
Columns:
Column 1: time-steps
Column 2-9: observations
Category:
4
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/biomedical/foetal_ecg.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=None, y=data[:, 1:9])
return data.train_test_split() if split_data else data
def flutter(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
Wing flutter data. Due to industrial secrecy agreements we are
not allowed to reveal more details. Important to know is that
the input is highly colored.
Sampling:
Number:
1024
Inputs:
u:
Outputs:
y:
References:
Feron E., Brenner M., Paduano J. and Turevskiy A.. "Time-frequency
analysis for transfer function estimation and application to flutter
clearance", in AIAA J. on Guidance, Control & Dynamics, vol. 21,
no. 3, pp. 375-382, May-June, 1998.
Properties:
Columns:
Column 1: input u
Column 2: output y
Category:
mechanical systems
Where:
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/mechanical/flutter.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0], y=data[:, 1])
return data.train_test_split() if split_data else data
def Silverbox(dir_placement=None,force_download=False, split_data=True):
'''The Silverbox system can be seen as an electronic implementation of the Duffing oscillator. It is build as a
2nd order linear time-invariant system with a 3rd degree polynomial static nonlinearity around it in feedback.
This type of dynamics are, for instance, often encountered in mechanical systems.
The provided data is part of a previously published ECC paper available online. A technical note describing the
Silverbox benchmark can be found here. All the provided data (.mat file format) on the Silverbox system is available
for download here. This .zip file contains the Silverbox dataset as specified in the benchmark document (V1 is the
input record, while V2 is the measured output), extended with .csv version of the same data and an extra data record
containing a Schroeder phase multisine measurement.
Please refer to the Silverbox benchmark as:
T. Wigren and J. Schoukens. Three free data sets for development and benchmarking in nonlinear system identification.
2013 European Control Conference (ECC), pp.2933-2938 July 17-19, 2013, Zurich, Switzerland.
Previously published results on the Silverbox benchmark are listed in the history section of this webpage.
Special thanks to Johan Schoukens for creating this benchmark, and to Torbjörn Wigren for hosting this benchmark.
'''
# url = 'http://www.nonlinearbenchmark.org/FILES/BENCHMARKS/SILVERBOX/SilverboxFiles.zip' #old
url = 'https://drive.google.com/file/d/17iS-6oBUUgrmiAcrZoG9S5sOaljZnDSy/view'
download_size=5793999
save_dir = cashed_download(url, 'Silverbox', zip_name='SilverboxFiles.zip',\
dir_placement=dir_placement, download_size=download_size, force_download=force_download)
save_dir = os.path.join(save_dir,'SilverboxFiles') #matfiles location
out = loadmat(os.path.join(save_dir,'Schroeder80mV.mat'))
u,y = out['V1'][0], out['V2'][0]
data1 = System_data(u=u,y=y)
out = loadmat(os.path.join(save_dir,'SNLS80mV.mat')) #train test
u,y = out['V1'][0], out['V2'][0]
data2 = System_data(u=u,y=y)
if split_data:
data_out = System_data(u=data2.u[40650:127400],y=data2.y[40650:127400])
return data_out.train_test_split()
return System_data_list([data1, data2])
def dryer(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
Laboratory setup acting like a hair dryer. Air is fanned through a tube
and heated at the inlet. The air temperature is measured by a
thermocouple at the output. The input is the voltage over the heating
device (a mesh of resistor wires).
Sampling:
Number:
1000
Inputs:
u: voltage of the heating device
Outputs:
y: output air temperature
References:
- Ljung L. System identification - Theory for the
User. Prentice Hall, Englewood Cliffs, NJ, 1987.
- Ljung. L. System Identification Toolbox. For Use
with Matlab. The Mathworks Inc., Mass., U.S.A., 1991.
Properties:
Columns:
Column 1: input u
Column 2: output y
Category:
mechanical systems
Where:
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/mechanical/dryer.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0], y=data[:, 1])
return data.train_test_split() if split_data else data
def sun_spot_data(dir_placement=None, force_download=False, split_data=True):
url = 'http://www.sidc.be/silso/DATA/SN_y_tot_V2.0.txt'
download_size = None
save_dir = cashed_download(url,
'sun_spot_data',
dir_placement=dir_placement,
download_size=download_size,
force_download=force_download,
zipped=False)
with open(os.path.join(save_dir, 'SN_y_tot_V2.0.txt'), 'r') as f:
data = f.read()[:-2]
fixed_name = os.path.join(save_dir, 'SN_y_tot_V2.0_fix.txt')
with open(fixed_name, 'w') as f:
f.write(data)
data = np.loadtxt(fixed_name)
yEst = data[:, 1]
datasets = System_data(u=None, y=yEst)
return datasets.train_test_split(
split_fraction=0.4) if split_data else datasets #is already splitted
def ballbeam(dir_placement=None, force_download=False, split_data=True):
'''This file describes the data in the ballbeam.dat file.
1. Contributed by:
Peter Van Overschee
K.U.Leuven - ESAT - SISTA
K. Mercierlaan 94
3001 Heverlee
[email protected]
2. Process/Description:
Data of a the ball and beam practicum at ESAT-SISTA.
3. Sampling time
0.1 sec.
4. Number of samples:
1000 samples
5. Inputs:
a. angle of the beam
6. Outputs:
a. position of the ball
7. References:
a. Van Overschee P., "Subspace identification : Theory,
Implementation, Application" , Ph.D. Thesis, K.U.Leuven, February
1995, pp. 200-206
8. Known properties/peculiarities
9. Some MATLAB-code to retrieve the data
!gunzip ballbeam.dat.Z
load ballbeam.dat
U=ballbeam(:,1);
Y=ballbeam(:,2);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/mechanical/ballbeam.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0], y=data[:, 1])
return data.train_test_split() if split_data else data
def erie(dir_placement=None, force_download=False, split_data=True, noise=10):
'''This file describes the data in the erie.dat file.
1. Contributed by:
Peter Van Overschee
K.U.Leuven - ESAT - SISTA
K. Mercierlaan 94
3001 Heverlee
[email protected]
2. Process/Description:
Data of a simulation (not real !) related to the related to the
identification of the western basin of Lake Erie. The series consists
of 4 series:
U_erie, Y_erie: without noise (original series)
U_erie_n10, Y_erie_n10: 10 percent additive white noise
U_erie_n20, Y_erie_n20: 20 percent additive white noise
U_erie_n30, Y_erie_n30: 30 percent additive white noise
3. Sampling time
1 month
4. Number of samples:
57 samples
5. Inputs:
a. water temperature
b. water conductivity
c. water alkalinity
d. NO3
e. total hardness
6. Outputs:
a. dissolved oxigen
b. algae
7. References:
R.P. Guidorzi, M.P. Losito, T. Muratori, On the last eigenvalue
test in the structural identification of linear multivariable
systems, Proceedings of the V European meeting on cybernetics and
systems research, Vienna, april 1980.
8. Known properties/peculiarities
The considered period runs from march 1968 till november 1972.
9. Some MATLAB-code to retrieve the data
!guzip erie.dat.Z
load erie.dat
U=erie(:,1:20);
Y=erie(:,21:28);
U_erie=U(:,1:5);
U_erie_n10=U(:,6:10);
U_erie_n20=U(:,11:15);
U_erie_n30=U(:,16:20);
Y_erie=Y(:,1:2);
Y_erie_n10=Y(:,3:4);
Y_erie_n20=Y(:,5:6);
Y_erie_n30=Y(:,7:8);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/environmental/erie.dat.gz'
erie = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
U = erie[:, :20]
Y = erie[:, 20:]
U_erie = U[:, :5]
U_erie_n10 = U[:, 5:10]
U_erie_n20 = U[:, 10:15]
U_erie_n30 = U[:, 15:20]
Y_erie = Y[:, 0:2] #two outputs
Y_erie_n10 = Y[:, 2:4]
Y_erie_n20 = Y[:, 4:6]
Y_erie_n30 = Y[:, 6:8]
if noise == 0:
data = System_data(u=U_erie, y=Y_erie)
elif noise == 10:
data = System_data(u=U_erie_n10, y=Y_erie_n10)
elif noise == 20:
data = System_data(u=U_erie_n20, y=Y_erie_n20)
elif noise == 30:
data = System_data(u=U_erie_n30, y=Y_erie_n30)
return data.train_test_split() if split_data else data
def destill(dir_placement=None,
force_download=False,
split_data=True,
noise=10):
'''This file describes the data in the destill.dat file.
1. Contributed by:
Peter Van Overschee
K.U.Leuven - ESAT - SISTA
K. Mercierlaan 94
3001 Heverlee
[email protected]
2. Process/Description:
Data of a simulation (not real !) related to the identification
of an ethane-ethylene destillationcolumn. The series consists of 4
series:
U_dest, Y_dest: without noise (original series)
U_dest_n10, Y_dest_n10: 10 percent additive white noise
U_dest_n20, Y_dest_n20: 20 percent additive white noise
U_dest_n30, Y_dest_n30: 30 percent additive white noise
3. Sampling time
15 min.
4. Number of samples:
90 samples
5. Inputs:
a. ratio between the reboiler duty and the feed flow
b. ratio between the reflux rate and the feed flow
c. ratio between the distillate and the feed flow
d. input ethane composition
e. top pressure
6. Outputs:
a. top ethane composition
b. bottom ethylene composition
c. top-bottom differential pressure.
7. References:
R.P. Guidorzi, M.P. Losito, T. Muratori, The range error test in the
structural identification of linear multivariable systems,
IEEE transactions on automatic control, Vol AC-27, pp 1044-1054, oct.
1982.
8. Known properties/peculiarities
9. Some MATLAB-code to retrieve the data
!gunzip destill.dat.Z
load destill.dat
U=destill(:,1:20);
Y=destill(:,21:32);
U_dest=U(:,1:5);
U_dest_n10=U(:,6:10);
U_dest_n20=U(:,11:15);
U_dest_n30=U(:,16:20);
Y_dest=Y(:,1:3);
Y_dest_n10=Y(:,4:6);
Y_dest_n20=Y(:,7:9);
Y_dest_n30=Y(:,10:12);
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/destill.dat.gz'
destill = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
U = destill[:, :20]
Y = destill[:, 20:]
U_dest = U[:, :5]
U_dest_n10 = U[:, 5:10]
U_dest_n20 = U[:, 10:15]
U_dest_n30 = U[:, 15:20]
Y_dest = Y[:, 0:3]
Y_dest_n10 = Y[:, 3:6]
Y_dest_n20 = Y[:, 6:9]
Y_dest_n30 = Y[:, 9:12]
if noise == 0:
data = System_data(u=U_dest, y=Y_dest)
elif noise == 10:
data = System_data(u=U_dest_n10, y=Y_dest_n10)
elif noise == 20:
data = System_data(u=U_dest_n20, y=Y_dest_n20)
elif noise == 30:
data = System_data(u=U_dest_n30, y=Y_dest_n30)
return data.train_test_split() if split_data else data
def exchanger(dir_placement=None, force_download=False, split_data=True):
'''This file describes the data in exchanger.dat
1. Contributed by:
Sergio Bittanti
Politecnico di Milano
Dipartimento di Elettronica e Informazione,
Politecnico di Milano,
Piazza Leonardo da Vinci 32, 20133 MILANO (Italy)
[email protected]
2. Process/Description:
The process is a liquid-satured steam heat exchanger, where water is
heated by pressurized saturated steam through a copper tube. The
output variable is the outlet liquid temperature. The input variables
are the liquid flow rate, the steam temperature, and the inlet liquid
temperature. In this experiment the steam temperature and the inlet
liquid temperature are kept constant to their nominal values.
3. Sampling time:
1 s
4. Number of samples:
4000
5. Inputs:
q: liquid flow rate
6. Outputs:
th: outlet liquid temperature
7. References:
S. Bittanti and L. Piroddi, "Nonlinear identification and control of a
heat exchanger: a neural network approach", Journal of the Franklin
Institute, 1996. L. Piroddi, Neural Networks for Nonlinear Predictive
Control. Ph.D. Thesis, Politecnico di Milano (in Italian), 1995.
8. Known properties/peculiarities:
The heat exchanger process is a significant benchmark for nonlinear
control design purposes, since it is characterized by a non minimum
phase behaviour. In the references cited above the control problem of
regulating the output temperature of the liquid-satured steam heat
exchanger by acting on the liquid flow rate is addressed, and both
direct and inverse identifications of the data are performed.
Columns:
Column 1: time-steps
Column 2: input q
Column 3: output th
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/exchanger.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 1], y=data[:, 2])
return data.train_test_split() if split_data else data
def winding(dir_placement=None, force_download=False, split_data=True):
'''Contributed by:
Favoreel
KULeuven
Departement Electrotechniek ESAT/SISTA
Kardinaal Mercierlaan 94
B-3001 Leuven
Belgium
[email protected]
Description:
The process is a test setup of an industrial winding process.
The main part of the plant is composed of a plastic web that
is unwinded from first reel (unwinding reel), goes over the
traction reel and is finally rewinded on the the rewinding reel.
Reel 1 and 3 are coupled with a DC-motor that is controlled with
input setpoint currents I1* and I3*. The angular speed of
each reel (S1, S2 and S3) and the tensions in the web between
reel 1 and 2 (T1) and between reel 2 and 3 (T3) are measured
by dynamo tachometers and tension meters.
We thank Th. Bastogne from the University of Nancy for
providing us with these data.
We are grateful to Thierry Bastogne of the Universite Henri Point Care, who
provided us with these data.
Sampling: 0.1 Sec
Number: 2500
Inputs: u1: The angular speed of reel 1 (S1)
u2: The angular speed of reel 2 (S2)
u3: The angular speed of reel 3 (S3)
u4: The setpoint current at motor 1 (I1*)
u5: The setpoint current at motor 2 (I3*)
Outputs: y1: Tension in the web between reel 1 and 2 (T1)
y2: Tension in the web between reel 2 and 3 (T3)
References:
- Bastogne T., Identification des systemes multivariables par
les methodes des sous-espaces. Application a un systeme
d'entrainement de bande. PhD thesis. These de doctorat
de l'Universite Henri Poincare, Nancy 1.
- Bastogne T., Noura H., Richard A., Hittinger J.M.,
Application of subspace methods to the identification of a
winding process. In: Proc. of the 4th European Control
Conference, Vol. 5, Brussels.
Properties:
Columns:
Column 1: input u1
Column 2: input u2
Column 3: input u3
Column 4: input u4
Column 5: input u5
Column 6: output y1
Column 7: output y2
Category:
Industrial test setup
'''
url = 'ftp://ftp.esat.kuleuven.ac.be/pub/SISTA/data/process_industry/winding.dat.gz'
data = daisydata_download(url,
dir_placement=dir_placement,
force_download=force_download)
data = System_data(u=data[:, 0:5], y=data[:, 5:7])
return data.train_test_split() if split_data else data