Main
~~* ''NLC~~.imode = 5 (simultaneous ~~approach~~)~~''~~

* ''NLC.imode = 8 (sequential ~~approach~~)''
~~Youtube video to be posted soon~~
~~The DBS file parameter ''imode'' ~~is ~~used to control the simulation mode. This option is set to ''5'' for dynamic parameter estimation~~.

~~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:

## Moving Horizon Estimation

## Main.Estimation History

Hide minor edits - Show changes to output

Changed lines 17-18 from:

* ''NLC

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)

nlc.imode = 8 (sequential dynamic estimation)

% MATLAB example

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

# Python example

apm_option(server,app,'nlc.imode',8)

Changed lines 15-17 from:

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''

''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)''

* ''NLC.imode = 5 (simultaneous approach)''

* ''NLC.imode = 8 (sequential approach)''

Changed line 7 from:

!!! MHE ~~Tutorial in ~~Simulink ~~/~~ MATLAB

to:

!!! MHE with Simulink and MATLAB

Changed line 11 from:

to:

(:html:)<iframe width="560" height="315" src="http://www.youtube.com/embed/ZVUtVf8wOkg?rel=0" frameborder="0" allowfullscreen></iframe>(:htmlend:)

Added lines 9-10:

* [[Attach:mhe_simulink.zip|Download MHE Simulink / MATLAB Files (zip)]]

Changed line 13 from:

!!! MHE ~~mode ~~in ~~APM~~

to:

!!! MHE in APMonitor

Changed line 7 from:

!!! Tutorial ~~on Implementing MHE in Simulink / MATLAB~~

to:

!!! MHE Tutorial in Simulink / MATLAB

Added lines 1-4:

(: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.:)

(: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.:)

Changed lines 7-8 from:

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.

!!! 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.

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]]

[[Attach:mhe.gif]]

Changed lines 1-7 from:

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:

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

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

* Nonlinear Models

* Infrequent Measurements

* Explicit Measurement Ranking

* Rejection of Statistically Insignificant Noise and Outliers

* Reliable real-time solutions

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.

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.