Simulink Design Optimization in Matlab Programming
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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