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 from Test Data
Use optimization algorithms to automatically estimate DC motor parameter values from transient test data.
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.