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optimize model response to satisfy design requirements, test model robustness

specify design requirements and optimize simulink^® model parameters in the response optimizer, or at the command line. you can optimize time-domain and frequency-domain design requirements simultaneously, using model verification blocks, or custom constraints and cost functions. you can also incorporate parameter uncertainty to test the robustness of your design. the software optimizes model parameters by formulating the design requirements as a constrained minimization problem, and then solves the problem using optimization techniques.

you can generate matlab^® code from the app, and accelerate response optimization using parallel computing and simulink fast restart.

response optimization basics

featured examples

engine design and cost tradeoffs

use simulink® design optimization™ to optimize a design for performance and cost. in this example, you tune an automotive engine speed controller while reducing controller costs by tuning sensor accuracy and actuator response time.

power converter tuning

use simulink® design optimization™ to optimize the current controller parameters of a 3-phase thyristor converter. the model uses blocks from simscape™ and simscape electrical™.

tuning of airframe autopilot gains

apply simulink® design optimization™ to optimize the autopilot gains of an airframe to control its fin deflection. the model uses blocks from aerospace blockset™.