Design Optimization

Evaluate design trade-offs and find optimal designs

Design optimization is the process of finding the best design parameters that satisfy project requirements.  Engineers typically use design of experiments (DOE), statistics, and optimization techniques to evaluate trade-offs and determine the best design. Design optimization often involves working in multiple design environments in order to evaluate the effects that design parameters have across interrelated physical domains.

MATLAB^® lets you import design data from a wide variety of file formats such as spreadsheets, text files, binary files, and other applications.  You can perform sensitivity analysis, parameter tuning, and design optimization from MATLAB and Simulink^®. Simulink is integrated with MATLAB, and provides tools for modeling, simulating, and optimizing multidomain dynamic systems.

MATLAB and Simulink add-on products further extend design optimization capabilities:

• Perform design of experiments to specify test plans, generate random numbers for Monte Carlo simulations, use sensitivity analysis to determine the robustness of your results, and create response surface models with Statistics Toolbox^™.
• Optimize single and multiple design objectives with Optimization Toolbox^™ and Global Optimization Toolbox.
• Define test plans, develop statistical models, and generate optimal calibrations and lookup tables for complex powertrain systems with Model-Based Calibration Toolbox^™.
• Tune design parameters in a Simulink model to meet objectives such as improved system performance and minimized energy consumption with Simulink Design Optimization^™.  Using design optimization techniques, you can meet both time-domain and frequency-domain constraints such as overshoot and phase margin.  You can also jointly optimize physical plant parameters and algorithmic or controller gains to maximize overall system performance.

Examples and How To

MATLAB Examples

• Data Analysis with Statistics and Curve Fitting Toolboxes (Webinar)
• Optimal Component Selection Using the Mixed-Integer Genetic Algorithm 5:25 (Video)
• Generating Optimal Engine Calibrations Using Model-Based Calibration Toolbox (Webinar)
• Multi-Objective Goal Attainment Optimization (Example)

Simulink Examples

• Optimizing a Flight Control System 4:53 (Video)
• Optimizing Suspension System Performance 6:41 (Video)
• Optimizing a Wind Turbine Blade Pitch Control System 4:32 (Video)

Software Reference

• Supported File Formats (MATLAB User's Guide)
• External Interfaces (MATLAB User's Guide)
• Choosing the Right Solver for Your Problem (Optimization Toolbox User's Guide)
• Mixed Integer Optimization (Global Optimization Toolbox User's Guide)
• Response Optimization (Simulink Design Optimization User's Guide)

See also: multiobjective optimization, nonlinear programming, design of experiments, parameter estimation