Get Started with Simulink Design Optimization

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

• Prepare Data for Parameter Estimation
  

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

• Estimate Model Parameter Values (GUI)
  

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

• Estimate Parameters from Measured Data
  

  Estimate parameters of a single-input/single-output (SISO) Simulink model using the Parameter Estimator.

• Estimate Model Parameter Values (Code)
  

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

• Design Optimization to Meet Step Response Requirements (GUI)
  

  Optimize controller parameters to meet step response requirements using Response Optimizer.

• Design Optimization to Meet Step Response Requirements (Code)
  

  Optimize controller parameters at the command line.

• Design Optimization to Track Reference Signal (GUI)
  

  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.

• Identify Key Parameters for Estimation (Code)
  

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

• Design Optimization-Based PID Controller for Linearized Simulink Model (GUI)
  

  Design a linear controller using optimization-based tuning in the Control System Designer app.

About Design Optimization

• Optimization Support for Simulink Models Using Third-Party Applications
  

  Optimize Simulink models that invoke third-party simulation tools or contain legacy simulation code.

• Ways to Speed Up Design Optimization Tasks
  

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

• How the Optimization Algorithm Formulates Minimization Problems
  

  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.