get started with simulink design optimization -凯发k8网页登录

simulink^® design optimization™ provides functions, interactive tools, and blocks for analyzing and tuning model parameters. you can determine 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, you can explore your design space and calculate parameter influence on model behavior.

simulink design optimization helps you increase model accuracy. you can preprocess test data, automatically estimate model parameters such as friction and aerodynamic coefficients, and validate the estimation results.

to improve system design characteristics such as response time, bandwidth, and energy consumption, you can jointly optimize physical plant parameters and algorithmic or controller gains. these parameters can be tuned to meet time-domain and frequency-domain requirements, such as overshoot and phase margin, and custom requirements.

tutorials

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  import input-output data, extract estimation data, remove outliers, and filter the data.

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  use experimental data to estimate model parameter values in the app.

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  estimate parameters of a single-input/single-output (siso) simulink model using the parameter estimator.

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  use experimental data to estimate model parameter values at the command line.

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  optimize controller parameters to meet step response requirements using response optimizer.

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  optimize controller parameters at the command line.

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  optimize parameters without adding signal constraint blocks to the model.

• identify key parameters for estimation (gui)
  

  this example shows how to use sensitivity analysis to narrow down the number of parameters that you need to estimate when fitting a model.

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  this example shows how to use sensitivity analysis to narrow down the number of parameters that you need to estimate to fit a model.

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  design a linear controller using optimization-based tuning in the control system designer app.

about design optimization

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  optimize simulink models that invoke third-party simulation tools or contain legacy simulation code.

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  use parallel computing, fast restart, and accelerator simulation model to speed up parameter estimation, response optimization, and sensitivity analysis tasks.

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  when you optimize parameters of a simulink model to meet design requirements, simulink design optimization software automatically converts the requirements into a constrained optimization problem and then solves the problem using optimization techniques.

• how the software formulates parameter estimation as an optimization problem
  

  estimation parameters are tuned to minimize the difference between the simulated and measured model responses.