Simulink Design Optimization
Analyze model sensitivity and tune model parameters
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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.
Design Optimization Apps
Use built-in apps to interactively set up and solve design optimization problems in Simulink, including specification of design requirements, decision variables, and optimization options. Generate MATLAB code from within the apps for deployment or additional customizations.
Parameter Estimation
Build accurate plant models by automatically estimating the parameters and states of your Simulink model from test data, either interactively with the Parameter Estimator app or command-line functions.
Response Optimization
Automatically optimize model parameters to satisfy time-domain and frequency-domain design requirements using the Response Optimizer app or command-line functions. Compute Pareto-optimal designs for multiobjective optimization problems, analyze design tradeoffs using visualization plots and select the design that best fits your performance needs.
Sensitivity Analysis
Identify which parameters have the greatest impact on your model's behavior using the Sensitivity Analyzer app. Select better initial conditions for parameter estimation and design optimization. Analyze your model's design space by performing multiobjective tradeoff analysis or using Monte Carlo simulations to check the robustness of your design.
Co-Optimization of Plant and Controller Parameters
Jointly optimize physical plant parameters and algorithmic or controller gains to improve system design characteristics such as response time, bandwidth, and energy consumption.
Digital Twin Tuning
Automatically update the parameters of a deployed digital twin model to match the current asset condition. Deploy the parameter estimation workflow using Simulink Compiler.
Lookup Table Tuning
Tune lookup tables for applications such as battery characterization or gain-scheduled controllers. Impose constraints such as monotonicity and smoothness on the lookup table values. Use adaptive lookup tables for solving calibration problems.
Design Optimization Speed-Up
Speed up parameter estimation, response optimization, and sensitivity analysis by running multiple simulations of a model in parallel using Parallel Computing Toolbox. Speed up design optimization tasks using the fast restart feature and the accelerator simulation mode of Simulink.
Optimization Solvers
Solve a variety of optimization problems including mixed-integer, derivative-based and derivative-free using a selection of available solvers such as surrogate, fmincon, and pattern search from Optimization Toolbox and Global Optimization Toolbox.
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Simulink Design Optimization FAQs
Simulink Design Optimization provides functions, interactive tools, and blocks for analyzing and tuning model parameters, allowing you to determine model sensitivity, fit models to test data, and tune them to meet design requirements.
You can optimize design parameters across Simulink and Simscape models to meet engineering performance goals such as efficiency, energy usage, robustness, dynamic response, and system-level requirements. Simulink Design Optimization supports tuning against time-domain specifications, frequency-domain metrics, and custom requirements derived from simulations, measurements, or test data.
Parameter estimation automatically estimates model parameters such as friction and aerodynamic coefficients from test data using the Parameter Estimator app or command-line functions to increase model accuracy.
Response optimization automatically optimizes model parameters to satisfy time-domain and frequency-domain design requirements using the Response Optimizer app or command-line functions.
The Sensitivity Analyzer app identifies which parameters have the greatest impact on model behavior using techniques like Monte Carlo simulation and Design of Experiments to analyze design space and check design robustness.
Yes, you can jointly optimize physical plant parameters and algorithmic or controller gains to improve system design characteristics.
You can speed up parameter estimation, response optimization, and sensitivity analysis by running multiple simulations in parallel using Parallel Computing Toolbox, or by using fast restart and accelerator simulation mode.
Yes, you can tune lookup tables for applications like battery characterization or gain-scheduled controllers, with the ability to impose constraints such as monotonicity and smoothness on lookup table values.
Simulink Design Optimization provides integrated handling of simulation execution, parameter management, requirement evaluation, and optimization setup for Simulink and Simscape models. It automates repeated simulation evaluations, supports parallel execution for scalable optimization studies, and provides interactive apps for configuring design optimization and parameter estimation workflows, eliminating the need to build custom infrastructure.
Simulink Design Optimization supports a variety of solvers including surrogate optimization, fmincon, and pattern search from Optimization Toolbox and Global Optimization Toolbox. This covers derivative-based, derivative-free, and mixed-integer optimization problems, letting you select the solver best suited to your problem characteristics.
Yes. Compute Pareto-optimal designs for multiobjective optimization problems, visualize trade-offs between competing performance objectives, and select the design that best fits your needs. This is useful for balancing requirements like performance vs. efficiency, ride comfort vs. handling, or accuracy vs. energy consumption.
Yes. You can estimate parameters, optimize responses, and perform sensitivity analysis on Simscape models (mechanical, electrical, thermal, hydraulic, and multidomain). The toolbox handles simulation execution and parameter updates within Simscape the same way it does for Simulink models.
Use the Parameter Estimator app or command-line functions to automatically adjust model parameters so that simulation outputs match your measured experimental or operational data. This supports calibration of engine models, battery models, actuator dynamics, multibody systems, and any physics-based Simulink or Simscape model.
Yes. Automatically update the parameters of a deployed digital twin model to match current asset conditions using parameter estimation. Deploy the parameter estimation workflow using Simulink Compiler so that the digital twin stays calibrated as the physical asset evolves over time.
Use sensitivity analysis and multiobjective optimization to explore and optimize physical design parameters using simulation-based evaluation. Identify which parameters most influence system behavior, evaluate multiple design concepts, and select parameter values that balance performance objectives — all before committing to hardware prototypes.
Yes. Generate MATLAB code directly from the Parameter Estimator, Response Optimizer, and Sensitivity Analyzer apps. This lets you customize workflows, automate repeated optimization tasks, integrate with CI/CD pipelines, or deploy estimation routines using Simulink Compiler.
