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.
Learn the basics of Simulink Design Optimization
Estimate model parameters and initial states from data, calibrate models
Optimize model response to satisfy design requirements, test model robustness
Analyze cost function sensitivity to model parameters using design of experiments (DOE), Monte Carlo, and correlation techniques
Design controllers using numerical optimization techniques
Tune table data and adaptive lookup tables