Moving Horizon Estimation

Main.Estimation History

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December 28, 2019, at 12:55 AM by 136.36.211.159 -
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See also [[https://apmonitor.com/do/index.php/Main/DynamicEstimation|MHE Introduction]], [[https://apmonitor.com/do/index.php/Main/MovingHorizonEstimation|CSTR MHE]], [[https://apmonitor.com/do/index.php/Main/EstimatorTypes|MHE with MPC]]
to:
See also [[https://apmonitor.com/do/index.php/Main/DynamicEstimation|MHE Introduction]], [[https://apmonitor.com/do/index.php/Main/MovingHorizonEstimation|CSTR MHE]], [[https://apmonitor.com/do/index.php/Main/EstimatorTypes|MHE with MPC]], [[https://apmonitor.com/wiki/index.php/Main/GekkoPythonOptimization|MHE with Python Gekko (see example #16)]]
November 04, 2019, at 01:36 PM by 136.36.211.159 -
Changed line 40 from:
See also [[MHE Introduction|https://apmonitor.com/do/index.php/Main/DynamicEstimation]], [[https://apmonitor.com/do/index.php/Main/MovingHorizonEstimation|CSTR MHE]], [[https://apmonitor.com/do/index.php/Main/EstimatorTypes|MHE with MPC]]
to:
See also [[https://apmonitor.com/do/index.php/Main/DynamicEstimation|MHE Introduction]], [[https://apmonitor.com/do/index.php/Main/MovingHorizonEstimation|CSTR MHE]], [[https://apmonitor.com/do/index.php/Main/EstimatorTypes|MHE with MPC]]
November 04, 2019, at 01:35 PM by 136.36.211.159 -
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 % MATLAB example
to:
 % APM MATLAB
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 # Python example
to:
 # APM Python
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 # Python Gekko
 m.options.IMODE = 8

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Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.
to:
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.

See also [[MHE Introduction|https://apmonitor.com/do/index.php/Main/DynamicEstimation]], [[https://apmonitor.com/do/index.php/Main/MovingHorizonEstimation|CSTR MHE]], [[https://apmonitor.com/do/index.php/Main/EstimatorTypes|MHE with MPC]]
June 09, 2017, at 01:00 AM by 10.5.113.159 -
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 nlc.imode = 5 (simultaneous dynamic estimation)
 nlc.imode = 8 (sequential dynamic estimation)
to:
 apm.imode = 5 (simultaneous dynamic estimation)
 apm.imode = 8 (sequential dynamic estimation)
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 apm_option(server,app,'nlc.imode',5);
to:
 apm_option(server,app,'apm.imode',5);
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 apm_option(server,app,'nlc.imode',8)
to:
 apm_option(server,app,'apm.imode',8)
June 16, 2015, at 07:00 PM by 45.56.3.184 -
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* ''NLC.imode = 5 (simultaneous approach)''
* ''NLC
.imode = 8 (sequential approach)''
to:
 nlc.imode = 5 (simultaneous dynamic estimation)
 nlc.imode = 8 (sequential dynamic estimation)

 % MATLAB example
 apm_option(server,app,
'nlc.imode',5);

 # Python example
 apm_option(server,app,'nlc.imode',8)
June 08, 2015, at 03:21 PM by 45.56.3.184 -
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The DBS file parameter ''imode'' is used to control the simulation mode.  This option is set to ''5'' for dynamic parameter estimation or MHE.

''NLC.imode = 5''
to:
The DBS file parameter ''imode'' is used to control the simulation mode.  This option is set to ''5'' or ''8'' for dynamic parameter estimation or MHE.

* ''NLC.imode = 5 (simultaneous approach)''
* ''NLC.imode = 8 (sequential approach)
''
May 25, 2013, at 06:48 AM by 69.169.188.188 -
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!!! MHE Tutorial in Simulink / MATLAB
to:
!!! MHE with Simulink and MATLAB
May 25, 2013, at 06:47 AM by 69.169.188.188 -
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Youtube video to be posted soon
to:
(:html:)<iframe width="560" height="315" src="https://www.youtube.com/embed/ZVUtVf8wOkg?rel=0" frameborder="0" allowfullscreen></iframe>(:htmlend:)
May 25, 2013, at 06:14 AM by 69.169.188.188 -
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* [[Attach:mhe_simulink.zip|Download MHE Simulink / MATLAB Files (zip)]]
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!!! MHE mode in APM
to:
!!! MHE in APMonitor
May 25, 2013, at 06:05 AM by 69.169.188.188 -
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!!! Tutorial on Implementing MHE in Simulink / MATLAB
to:
!!! MHE Tutorial in Simulink / MATLAB
May 25, 2013, at 06:04 AM by 69.169.188.188 -
Deleted lines 3-4:

!! Moving Horizon Estimation
May 25, 2013, at 06:03 AM by 69.169.188.188 -
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(:title Moving Horizon Estimation:)
(:keywords nonlinear, model, predictive control, moving horizon, differential, algebraic, modeling language:)
(:description Tutorial in Simulink / MATLAB for implementing Moving Horizon Estimation for linear or nonlinear systems.:)

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The DBS file parameter ''imode'' is used to control the simulation mode.  This option is set to ''5'' for dynamic parameter estimation.
to:
Moving Horizon Estimation (MHE) is an optimization approach that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables or parameters. Unlike deterministic approaches like the Kalman filter, MHE requires an iterative approach that relies on linear programming or nonlinear programming solvers to find a solution.

!!! Tutorial on Implementing MHE in Simulink / MATLAB

Youtube video to be posted soon

!!! MHE mode in APM

The DBS file parameter ''imode'' is used to control the simulation mode.  This option is set to ''5'' for dynamic parameter estimation or MHE
.
Changed line 28 from:
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.
to:
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.
May 25, 2013, at 05:55 AM by 69.169.188.188 -
Deleted lines 16-17:

[[Attach:mhe.gif]]
September 12, 2010, at 03:17 AM by 206.180.155.75 -
Changed lines 16-18 from:
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.
to:
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.

[[Attach:mhe.gif]]
September 30, 2008, at 03:21 PM by 158.35.225.227 -
Changed lines 1-7 from:
APMonitor is commercially available software that brings estimation into an optimization framework.  With the APMonitor Modeling Language, nonlinear dynamic models are rapidly prototyped and deployedThe APMonitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements in an approach termed Moving Horizon Estimation (MHE).  MHE is desireable for problems with:
to:
!! Moving Horizon Estimation

The DBS file parameter ''imode'' is used to control the simulation mode.  This option is set to ''5'' for dynamic parameter estimation
.

''NLC.imode = 5''

Moving horizon estimation is optimization of model parameters based on a time series of data
measurements.  The %blue%A%red%P%black%Monitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements.  This approach is desireable for problems with:
Changed lines 9-14 from:
Constraints
Nonlinear Models
Infrequent Measurements
Explicit Measurement Ranking
Rejection of Statistically Insignificant Noise and Outliers
Reliable real-time solutions
to:
* Constraints
* Nonlinear Models
* Infrequent Measurements
* Explicit Measurement Ranking
* Rejection of Statistically Insignificant Noise and Outliers
* Reliable real-time solutions
September 29, 2008, at 06:51 PM by 158.35.225.229 -
Added lines 1-10:
APMonitor is commercially available software that brings estimation into an optimization framework.  With the APMonitor Modeling Language, nonlinear dynamic models are rapidly prototyped and deployed.  The APMonitor solution engine uses sparse large-scale nonlinear solvers to reconcile the model to available measurements in an approach termed Moving Horizon Estimation (MHE).  MHE is desireable for problems with:
 
Constraints
Nonlinear Models
Infrequent Measurements
Explicit Measurement Ranking
Rejection of Statistically Insignificant Noise and Outliers
Reliable real-time solutions
 
Moving horizon estimation uses a moving window of previous model predictions and process measurements.  As additional measurements arrive, the model is updated with the new information.