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Online Estimation

Estimate model parameters and states during system operation, generate code and deploy to embedded targets

Online estimation algorithms update model parameters and state estimates when new data is available. You can perform online parameter estimation and online state estimation using Simulink® blocks and at the command line. You can generate C/C++ code and deploy your code to an embedded target. To learn more about online estimation, see What Is Online Estimation?

Featured Examples

Online ARMAX Polynomial Model Estimation

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.

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Line Fitting with Online Recursive Least Squares Estimation

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.

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State Estimation Using Time-Varying Kalman Filter

State Estimation Using Time-Varying Kalman Filter

Estimate states of linear systems using time-varying Kalman filters in Simulink.

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Parameter and State Estimation in Simulink Using Particle Filter Block

Parameter and State Estimation in Simulink Using Particle Filter Block

Demonstrates the use of 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.

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System Identification Toolbox Documentation

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