Simulink Design Optimization
Description
- Introduction and Key Features
- Estimating Model Parameters from Test Data
- Optimizing Time-Domain and Frequency-Domain Responses of Simulink Models
- Meeting Requirements Specified by Model Verification Blocks
- Using Custom Constraints and Cost Functions
- Programmatically Specifying Response Optimization Problems
- Accounting for Parameter Variation or Uncertainty
Accounting for Parameter Variation or Uncertainty
Simulink Design Optimization lets you test the robustness of your design against variations in model parameters. You can use Monte Carlo simulations to improve the robustness of designs involving uncertain parameters. Simulink Design Optimization lets you set nominal and bounding values for each uncertain parameter in the model.
Using Simulink Design Optimization, you can check the effects of parameter variations and uncertainty on system response and account for these effects during optimization.
Tuning the parameters associated with a PID Controller block (top, blue) in the presence of parameter uncertainty (bottom left) in a Plant block (top, pink). The step response and reference tracking plots (bottom right) show nominal response (solid blue line) and response with uncertainty (dashed blue lines).
