Moving Horizon Estimation
Main.Estimation History
Show minor edits - Show changes to output
Changed line 40 from:
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)]]
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]]
Changed line 20 from:
% MATLAB example
to:
% APM MATLAB
Changed line 23 from:
# Python example
to:
# APM Python
Added lines 26-28:
# Python Gekko
m.options.IMODE = 8
m.options.IMODE = 8
Changed lines 38-40 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.
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]]
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]]
Changed lines 17-19 from:
to:
apm.imode = 5 (simultaneous dynamic estimation)
apm.imode = 8 (sequential dynamic estimation)
apm.imode = 8 (sequential dynamic estimation)
Changed lines 21-22 from:
apm_option(server,app,'nlc.imode',5);
to:
apm_option(server,app,'apm.imode',5);
Changed line 24 from:
apm_option(server,app,'nlc.imode',8)
to:
apm_option(server,app,'apm.imode',8)
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="https://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.