Response Optimization

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
  • Specify Design Requirements
    Add and edit design requirements, such as lower and upper amplitude bounds, step response characteristics, reference signals, and custom requirements
  • 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

Featured Examples

PID Tuning with Reference Tracking and Plant Uncertainty

PID Tuning with Reference Tracking and Plant Uncertainty

Use Simulink® Design Optimization™ to track a reference signal and optimize the response with uncertainties in the plant model. The plant model consists of the plant transfer function and includes a Saturation block and a Rate Limits block. To view the results, use the following steps.

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Magnetic Levitation Controller Tuning

Magnetic Levitation Controller Tuning

Use numerical optimization to tuning the controller parameters of a nonlinear system. In this example, we model a CE 152 Magnetic Levitation system where the controller is used to position a freely levitating ball in a magnetic field. The control structure for this model is fixed and the required controller performance can be specified in terms of an idealized time response.

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Engine Design and Cost Tradeoffs

Engine Design and Cost Tradeoffs

Use Simulink Design Optimization™ to optimize a design for performance and cost. In this example, we tune an automotive engine speed controller while reducing controller costs by tuning sensor accuracy and actuator response time.

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Power Converter Tuning

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™.

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Distillation Controller Tuning

Distillation Controller Tuning

Use Simulink® Design Optimization™ to optimize the multi-loop controller parameters of a distillation column. The Distillation column produces methanol and is represented as a linear model with delays. The digital multi-loop controller consists a decoupling matrix and two single-loop PI controllers. The parameters of both the single-loop controllers are tuned simultaneously to satisfy a 14 percent overshoot and 13 minute rise-time step response characteristics.

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Tuning of Airframe Autopilot Gains

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™.

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Simulink Design Optimization Documentation

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