Main Content

Optimize Model Response

Specify design variables, progress plots and methods, speed up optimization using parallel computing and fast restart, incorporate parameter uncertainty for robustness testing

Apps

Response OptimizerOptimize model response to satisfy design requirements, test model robustness

Functions

expand all

sdo.SimulationTestSimulation scenario description
sdo.setValueInModelSet design variable value in model
sdo.getValueFromModelGet design variable value from model
sdo.optimizeDesign optimization problem solution
sdo.OptimizeOptionsOptimization options
sdo.OperatingPointSetupSet up steady-state operating point computation
sdo.getParameterFromModelDesign variable for optimization
sdo.getModelDependenciesList of model file and path dependencies

Topics

Optimization Basics

Design Optimization to Meet Step Response Requirements (GUI)

Optimize controller parameters using the 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.

Design Optimization to Meet Frequency-Domain Requirements (GUI)

This example shows how to tune model parameters to meet frequency-domain requirements using the Response Optimizer.

Design Optimization to Meet Frequency-Domain Requirements (Code)

This example shows how to tune model parameters to meet frequency-domain requirements, using the sdo.optimize command.

Design Optimization Using Frequency-Domain Check Blocks (GUI)

Optimize model parameters to meet frequency-domain design requirements using the Response Optimizer.

Design Optimization to Meet Time- and Frequency-Domain Requirements (GUI)

This example shows how to tune a controller to satisfy time-domain and frequency-domain design requirements using the Response Optimizer.

Write a Cost Function

Write a cost function for parameter estimation, response optimization, or sensitivity analysis. The cost function evaluates your design requirements using design variable values.

Design Optimization Tuning Parameters in Referenced Models (GUI)

This example shows how to tune parameters in referenced models, using the Response Optimizer.

Design Optimization Tuning Parameters in Referenced Models (Code)

This example shows how to tune parameters in referenced models, using the sdo.optimize command.

Steady-State Optimization

Specify Steady-State Operating Point for Response Optimization

An operating point of a dynamic system defines the states and root-level input signals of the model at a specific time.

Custom Objectives

Design Optimization to Meet a Custom Objective (GUI)

This example shows how to optimize a design to meet a custom objective using the Response Optimizer.

Design Optimization to Meet a Custom Objective (Code)

This example shows how to optimize a design to meet custom objective using sdo.optimize.

Design Optimization to Meet Custom Signal Requirements (GUI)

Specify a custom requirement on a model signal in the Response Optimizer.

Specify Custom Signal Objective with Uncertain Variable (GUI)

This example shows how to specify a custom objective function for a model signal.

Uncertain Variables

Optimizing Parameters for Robustness

Incorporate the parameter uncertainty to test the robustness of your design.

Specify Custom Signal Objective with Uncertain Variable (GUI)

This example shows how to specify a custom objective function for a model signal.

Design Optimization with Uncertain Variables (Code)

This example shows how to optimize a design when there are uncertain variables.

Speed Up Optimization

Skip Model Simulation Based on Parameter Constraint Violation (GUI)

This example shows how to optimize a design and specify parameter-only constraints that prevent the model from being evaluated in an invalid solution space.

Speed Up Response Optimization Using Parallel Computing

Scenarios when you can speed up optimization using parallel computing, and how the speedup happens.

Use Parallel Computing for Response Optimization

Use parallel computing for response optimization in the app, or at the command line.

Optimizing Time-Domain Response of Simulink® Models Using Parallel Computing

This example shows how to use parallel computing to optimize the time-domain response of a Simulink® model.

Use Fast Restart Mode During Response Optimization

This topic shows how to speed up response optimization using Simulink® fast restart.

Use Accelerator Mode During Simulations

Simulink Design Optimization™ software supports Normal and Accelerator simulation modes.

Improving Optimization Performance Using Parallel Computing

This example shows how to improve optimization performance using the Parallel Computing Toolbox™.

Response Optimizer Tasks

Specify Design Variables

This topic shows how to specify design variables for optimization.

Specify Signals to Log

Specify signals to log in the Response Optimizer.

Create Linearization I/O Sets

Create linearization input/output sets in the Response Optimizer or Sensitivity Analyzer.

Specify Optimization Options

This topic shows how to specify optimization options in the Response Optimizer, after you have configured the design variables and design requirements.

Interact with Plots

This topic shows how to interact with plots in the Response Optimizer.

Compare Requirements and Design Variables Using Spider Plot

This example shows how to use a spider plot to compare requirement evaluations before and after optimizing the response.

Save Design Variable Values for Specific Iteration

This example shows how to save the design variable values for specific optimization iterations.

Update Model with Design Variables Set

This example shows how to update a model with a set of design variables.

Save and Load Optimization Sessions

Structure of an optimization session, and saving and loading sessions in the Response Optimizer.

Code Generation

Generate MATLAB Code for Design Optimization Problems (GUI)

This example shows how to automatically generate a MATLAB function to solve a Design Optimization problem.

Troubleshooting

Optimization Does Not Make Progress

What to do if the optimization stalls or no changes are seen in parameters values.

Optimization Convergence

What to do if the optimization does not satisfy design requirements or takes a long time to converge near a solution, or if the system response becomes unstable.

Optimization Speed and Parallel Computing

What to do if no speedup is seen with parallel computing, if the results are different, or if the optimization stalls.

Undesirable Parameter Values

What to do if optimization gives undesirable parameter values or violates bounds on values.

Reverting to Initial Parameter Values

How to quit optimizing and revert to original values.

Featured Examples