Simulink Design Optimization™ gives functions, interactive tools, and blocks for analyzing and tuning model parameters. Users can find the model’s sensitivity, fit the model to test data, and tune it to meet requirements. Using techniques like Monte Carlo simulation and Design of Experiments, one can can explore your design space and calculate parameter influence on model behavior. Simulink Design Optimization helps you increase model accuracy. It can preprocess test data, automatically estimate model parameters such as friction and aerodynamic coefficients, and validate the estimation results.
With Simulink Design Optimization, users can tune Simulink model parameters to meet time-domain requirements, frequency-domain requirements, or both simultaneously. With the help of Design Optimization tool in Simulink Design Optimization, you can add and edit design requirements graphically or by entering tabular data. The graphical tool also lets users to monitor optimization progress. It presents plots for each requirement as well as the optimization status in a single view. As with parameter estimation, users can simultaneously optimize multiple model parameters, including scalars, vectors, matrices, or fields of structured variables defined in the MATLAB or Simulink model workspace or in a Simulink data dictionary.
Fundamentals of MATLAB Classes
Simulink Design Optimization can utilize measured input-output data from hardware to estimate and validate the parameters of a Simulink® model. This help to import measured data from the MATLAB® workspace, as well as from MATLAB, Microsoft® Excel®, ASCII, and CSV files. Measured data oftenly has offsets, outliers, missing values, and other anomalies that can lead to inaccurate parameter estimation. Simulink Design Optimization lets you preprocess the data to remove these sources of error. It enables to:
- • Remove data drift and offset
- • Filter noise and band-limited disturbances
- • Fill in missing values
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