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 and Machine Learning 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
See also: multiobjective optimization, nonlinear programming, quadratic programming, genetic algorithm, design of experiments, parameter estimation, design optimization videos, integer programming, Convex Optimization, Surrogate Optimization