Simulink Design Optimization
Analyze model sensitivity and tune model parameters
Have questions? Contact sales.
Have questions? Contact sales.
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.
Interactively import and preprocess your measured data, select model parameters to estimate, perform estimation, and compare and validate estimation results. You can generate MATLAB code from the app to automate the entire process.
Choose from a variety of derivative-based and global optimization solvers. You can also set parameter ranges, initialize models at steady-state operating points, and accelerate the parameter estimation process using Parallel Computing Toolbox™.
Automatically update the parameters of a deployed digital twin model to match the current asset condition. Deploy the parameter estimation workflow using Simulink Compiler™.
Interactively setup and run optimization problems to tune Simulink model parameters. You can graphically specify multiple design requirements, choose model parameters to optimize, and generate MATLAB code from the app to automate the entire process.
Choose time and frequency-domain requirements such as step-response characteristics, reference signals to track, and Bode magnitude bounds. For frequency-domain requirements the model is linearized using Simulink Control Design. You can also define custom requirements and constraints.
Improve design robustness by accounting for uncertainty in your model parameters. You can choose optimization solvers, set parameter ranges, initialize models at steady-state operating points, and accelerate the response optimization process using Parallel Computing Toolbox™.
Tune lookup tables for applications such as gain-scheduled controllers. You can impose constraints such as monotonicity and smoothness on the lookup table values. Use adaptive lookup tables for solving calibration problems.
Interactively create a set of parameter values by sampling probability distributions and perform global sensitivity analysis. Visualize and analyze the results to identify key model parameters. Generate MATLAB code from the app to automate the process.
Analyze your model’s design space using Monte Carlo simulations and design of experiments. This lets you check the robustness of your design, and also determine the impact key model parameters can have on cost functions and design requirements.
Select parameter values that can be good initial conditions for your Parameter Estimator and Response Optimizer app sessions directly from the Sensitivity Analyzer app by visualizing the results of your sensitivity analysis.