Simulink Design Optimization
Simulink Design Optimization lets you configure, manipulate, and run parameter estimations. It provides a graphical tool that lets you:
Simulink Design Optimization can use measured input-output data from hardware to estimate and validate the parameters of a Simulink model. The product lets you import measured data from the MATLAB® workspace, as well as from MATLAB, Microsoft® Excel®,ASCII, and CSV files. Measured data often has offsets, outliers, missing values, and other anomalies that can lead to inaccurate parameter estimation.Simulink Design Optimization lets you preprocess your measured data to remove these sources of error. You can:
Simulink Design Optimization lets you estimate parameters for Simulink models that include nonlinear effects, multiple sampling rates, and fixed-point calculations. Models built using any blocks from Simulink and related products are supported.
You can estimate multiple model parameters at the same time. The parameters can be scalars, vectors, matrices, or fields of structured variables defined in the MATLAB or Simulink model workspace. For each parameter, you can specify minimum and maximum values that are not to be exceeded during estimation.
Simulink Design Optimization provides a variety of optimization algorithms that can be used for parameter estimation, including gradient descent, nonlinear least squares, simplex search, and, with Global Optimization Toolbox, pattern search. You can fine-tune optimization performance by adjusting optimization algorithm settings, such as convergence tolerances and number of iterations. You can accelerate the parameter estimation process using Simulink Design Optimization with Parallel Computing Toolbox™.
Estimating DC Motor Parameters
Automatically estimate parameters of a DC motor from measured input-output data using Simulink Design Optimization™.
Simulink Design Optimization lets you set up and maintain multiple estimation tasks. For each task, you can specify the model parameters and initial conditions to estimate and the measured data to use. This approach lets you estimate parameters for one section of your model using one combination of data sets and independently estimate parameters for other model sections using different combinations of data sets. You can refine the parameter-tuning process by using parameter values from previous estimation tasks as initial values for subsequent estimations or by setting ranges for estimated parameters.
Estimating the Parameters of a Hydraulic System
Automatically tune parameters until simulation results match measurement data. Optimization algorithms are used to obtain realistic parameter values for a hydraulic system.
In addition to estimating model parameters, Simulink Design Optimization estimates static lookup table values and provides a Simulink block for implementing adaptive lookup tables. You can connect your adaptive lookup table directly to a physical system by compiling your Simulink model and implementing the code using an appropriate host, such as Simulink Real-Time™.
Simulink Design Optimization can generate comparative plots of estimation results to help you determine which model parameter values result in the best model and measured data fit. Plots include views of parameter sensitivity, measured versus simulated model outputs, and residual values.
Validation involves comparing the model output with an independent set of measured data to determine whether the calibrated model accurately captures the system dynamics. Simulink Design Optimization lets you compare multiple model outputs against the validation data set to select the best estimation and parameter sets.
With Simulink Design Optimization, you can tune Simulink model parameters to meet time-domain requirements, frequency-domain requirements, or both simultaneously. Using the Design Optimization tool in Simulink Design Optimization, you can add and edit design requirements graphically or by entering tabular data, and then run the optimization. The graphical tool also lets you monitor optimization progress. It shows plots for each requirement as well as the optimization status in a single view.
As with parameter estimation, you can simultaneously optimize multiple model parameters, including scalars, vectors, matrices, or fields of structured variables defined in the MATLAB or Simulink model workspace. You can also specify minimum and maximum values for each parameter.
You can choose from a variety of optimization algorithms, such as gradient descent, nonlinear least squares, simplex search, and, with Global Optimization Toolbox, pattern search. You can adjust optimization algorithm settings, such as convergence tolerances and number of iterations, to improve optimization performance. To accelerate the process by performing the optimization on multiple cores or processors, you can use Parallel Computing Toolbox with Simulink Design Optimization.
You can add a new time-domain design requirement by selecting a requirement type and specifying the model signals to use for evaluating the requirement. Simulink Design Optimization lets you specify time-domain design requirements on a signal by:
You can edit requirements graphically or by entering numeric values. For example, to edit a step response envelope requirement, you can graphically adjust the bounds or enter values for rise time, overshoot, settling time, and other parameters that define step response characteristics.
You can set up the optimization to meet time-domain design requirements directly from the graphical tool, without adding any blocks to the model. You can also use several design requirements simultaneously to optimize multiple design criteria.
During the optimization, the product updates the plots for each design requirement so you can visually monitor optimization progress in one window.
For frequency-domain optimization, you can use Simulink Design Optimization with Simulink Control Design to linearize a Simulink model and use the resulting linear model to evaluate the following requirements:
You can optimize not only the frequency-domain characteristics of the control system, but also the frequency response of the plant model.
Simulink Design Optimization lets you manage tradeoffs among requirements, such as stability, robustness, and performance, as you fine-tune your design.
Optimizing a Flight Control System
Optimize the parameters of a flight control system to simultaneously meet time-domain and frequency-domain design requirements.
You can specify a variety of time-domain and frequency-domain requirements to optimize system performance. Typical requirements include gain and phase margins, damping ratio, minimum bandwidth, high-frequency rolloff, and constraints on the step or impulse responses. You can optimize the poles, zeros, and gains of your compensators, or directly tune the parameters of the corresponding blocks in Simulink. Plots comparing the current response with your design requirements help you monitor progress while the optimization runs.
Simulink Design Optimization supports the optimization of model parameters to meet design requirements specified by Model Verification blocks in the Simulink, Simulink Control Design, and Simulink Design Optimization block libraries.
Model Verification blocks enable you to verify that your design meets time-domain and frequency-domain requirements such as time-dependent upper and lower bounds on signal value, frequency-dependent Bode plot magnitude constraints, step response bounds, and gain and phase margins. Model Verification blocks detect requirement violations. You can configure the blocks to stop the simulation when a violation is detected or log the event for further analysis.
You can use Simulink Design Optimization to automatically tune model parameters to ensure that all design requirements specified by Model Verification blocks are met.
Simulink Design Optimization lets you specify custom constraints and cost functions for optimizing the parameters of your Simulink model. For example, you can minimize the cross-sectional area of a hydraulic cylinder, while ensuring that pressures in the cylinder do not exceed a predetermined limit and that the cylinder piston position meets specified step response characteristics.
Custom requirements can be specified as an objective to be minimized, an equality constraint, or an inequality constraint. Custom requirements can be specified in both time domains and frequency domains. You can also include statistical properties in custom requirements. For example, you can optimize automotive suspension damping to minimize the mean value of suspension displacement for normal passenger weight distribution.
Optimizing Suspension System Performance
Use custom objectives and frequency-domain optimization to optimize the ride quality of a suspension system.
In addition to providing a graphical tool for setting up and solving parameter optimization problems, Simulink Design Optimization lets you formulate and solve optimization problems programmatically. Using this approach, you can:
You can create scripts for documenting your work and running optimizations in batch mode.
Simulink Design Optimization lets you test the robustness of your design against variations in model parameters. You can use Monte Carlo simulations to improve the robustness of designs involving uncertain parameters. Simulink Design Optimization lets you set nominal and bounding values for each uncertain parameter in the model.
Using Simulink Design Optimization, you can check the effects of parameter variations and uncertainty on system response and account for these effects during optimization.