Stima online
Gli algoritmi di stima online aggiornano i parametri del modello e le stime dello stato quando sono disponibili nuovi dati. È possibile eseguire la stima online dei parametri e degli stati utilizzando i blocchi Simulink® e dalla riga di comando. È possibile generare codice C/C++ e distribuire il codice su un target embedded. Per saperne di più sulla stima online, vedere What Is Online Estimation?
Categorie
- Stima online dei parametri
Stimare i parametri del modello utilizzando algoritmi ricorsivi dalla riga di comando e in Simulink
- Stima dello stato online
Stimare i parametri del modello utilizzando i filtri di Kalman lineari e non lineari dalla riga di comando e in Simulink
Esempi in primo piano
Online State Estimation Using Identified Linear Models
Generate the state-transition and measurement functions for online state and output estimation using linear identification techniques.
Online State Estimation Using Identified Nonlinear Models
Generate the state-transition and measurement functions for online state and output estimation using nonlinear system identification techniques.
Online ARMAX Polynomial Model Estimation
Implement an online polynomial model estimator. You estimate two ARMAX models for a nonlinear chemical reaction process. These models capture the behavior of the process at two operating conditions. The model behavior is identified online and used to adjust the gains of an adaptive PI controller during system operation.
Line Fitting with Online Recursive Least Squares Estimation
Perform online parameter estimation for line-fitting using recursive estimation algorithms at the MATLAB® command line. You capture the time-varying input-output behavior of the hydraulic valve of a continuously variable transmission.
State Estimation Using Time-Varying Kalman Filter
Estimate states of linear systems using time-varying Kalman filters in Simulink.
Parameter and State Estimation in Simulink Using Particle Filter Block
Demonstrates the use of the Particle Filter block in System Identification Toolbox™. A discrete-time transfer function parameter estimation problem is reformulated and recursively solved as a state estimation problem.
