Use Simulink Control Design from command line. The MATLAB functions available in Simulink Control Design software allow for the programmatic specification of the input and output points
Linearize a Simulink® model at the model operating point using the linearize command.
Model computational delay and sampling effect using Simulink Control Design.
Enable custom masked subsystems in Control System Designer. Once configured, you can tune a custom masked subsystem in the same way as any supported blocks in Simulink Control Design. For
Gives a tour of available time-domain requirements for control system tuning with systune or looptune.
Linearize a plant model at a set of design points for tuning of a gain-scheduled controller. The example then uses the resulting linearized models to configure an slTuner interface for
Create and configure an slTuner interface for a Simulink® model. The slTuner interface parameterizes blocks in your model that you designate as tunable and allows you to tune them using
Use Control System Tuner to tune a control system when there are parameter variations in the plant. The control system of this example is an active suspension on a quarter-car model. The
Use systune or looptune to automatically tune control systems modeled in Simulink.
Specify loop shapes and stability margins when tuning control systems with systune or looptune.
Gives a tour of available frequency-domain requirements for control system tuning with systune or looptune.
Constrain the poles of a control system tuned with systune or looptune .
Configure additional attributes of design requirements for use with systune or looptune .
This is Part 3 of the example series on design and tuning of the flight control system for the HL-20 vehicle. This part shows how to tune a classic SISO architecture for controlling the roll,
This is Part 2 of the example series on design and tuning of the flight control system for the HL-20 vehicle. This part deals with closing the inner loops controlling the body angular rates.
Design a PID controller for a power electronics system modeled in Simulink® using Simscape™ Electrical™ Power Systems components.
Use Open-Loop PID Autotuner block to tune a PI controller of an engine speed control system in both simulation and real time.
Model a scalar gain K with a bilinear dependence on two scheduling variables, and V. Suppose that is an angle of incidence that ranges from 0 to 15 degrees, and V is a speed that ranges from 300 to
One of several ways to tune a PID controller for plants that cannot be linearized. In this example, you use the Frequency Response Based PID Tuner to automatically characterize the frequency
Use the Closed-Loop PID Autotuner block to tune a PID controller for a boost converter plant in both simulation and real time.
Tune a PID controller for plants that cannot be linearized. You use PID Tuner to identify a plant for your model. Then tune the PID controller using the identified plant.
Generate an array of LTI models that represent the plant variations of a control system from a Simulink model. This array of models is used in Control System Designer for control design.
Use Simulink® Control Design™ software, using a drum boiler as an example application. Using the operating point search function, the example illustrates model linearization as well as
Design an array of PID controllers for a nonlinear plant in Simulink that operates over a wide range of operating points.
Obtain the frequency response of Simulink models when analytical block-by-block linearization does not provide accurate answer due to event-based dynamics in the linearization path.
Use the frequency response estimation to perform a sinusoidal-input describing function analysis, for a model with a saturation nonlinearity.
Illustrates how to use parallel computing for speeding up frequency response estimation of Simulink models. In some scenarios, the command FRESTIMATE performs multiple Simulink
Use frequency response estimation in order to validate a block-by-block analytical linearization result obtained with the command LINEARIZE on the lightweight airplane model. Note that
Estimate the frequency response of a Simulink® model at the MATLAB® command line.
Obtain a Linear Parameter Varying (LPV) approximation of a Simscape™ Electrical™ Power Systems model of a Boost Converter. The LPV representation allows quick analysis of average
Plot linearization of a Simulink model at particular conditions during simulation. The Simulink Control Design software provides blocks that you can add to Simulink models to compute and
Specify the rate conversion method for the linearization of a multirate model. The choice of rate conversion methodology can affect the resulting linearized model. This example
Use the time based operating point snapshot feature in Simulink Control Design. This example uses a model of the dynamics of filling a cylinder with compressed air.
The process that the command linearize uses when extracting a linear model of a nonlinear multirate Simulink model. To illustrate the concepts, the process is first performed using
Specify the linearization of a Simulink block or subsystem.
Use the slLinearizer interface to batch linearize a Simulink model. You vary model parameter values and obtain multiple open- and closed-loop transfer functions from the model.
Specify the linearization for a model component that does not linearize well using a linear model identified using the System Identification Toolbox™. This example requires Simscape™
Trim and linearize an airframe. We first need to find the elevator deflection and the resulting trimmed body rate (q) that will generate a given incidence value when the airframe is traveling
Use the command LINEARIZE to speed up the batch linearization where a set of block parameters are varied.
The features available in Simulink Control Design for linearizing models containing references to other models with a Model block.
Approximate the nonlinear behavior of a system as an array of interconnected LTI models.
Use the slLinearizer interface to batch linearize a Simulink® model. You linearize a model at multiple operating points and obtain multiple open-loop and closed-loop transfer functions
Use the linearize command to batch linearize a model at varying operating points.
In this example, you vary model parameters and linearize a model at its nominal operating conditions using the linearize command.
Use the slLinearizer interface to batch linearize a Simulink® model. You vary model parameter values and obtain multiple open-loop and closed-loop transfer functions from the model.
If your application includes parameter variations that affect the operating point of the model, you must batch trim the model for the parameter variations before linearization. Use this
Compute a linear model of the combined controller-plant system without the effects of the feedback signal. You can analyze the resulting linear model using, for example, a Bode plot.
Debug the linearization of a Simulink model at the command line using a LinearizationAdvisor object. You can also troubleshoot linearization results interactively. For more
Linearize a plant subsystem in a Simulink® model using the linearize command.
Generate operating points using triggered snapshots.
View and modify the states in a Simulink model using an operating point object.
Obtain multiple operating points for a model by varying parameter values. You can study the controller robustness to plant variations by batch linearizing the model using the trimmed
Batch trim a model when the specified parameter variations affect the known states for trimming.
Find operating points for multiple operating point specifications using the findop command. You can batch linearize the model using the operating points and study the change in model
You can initialize operating point searches with a simulation snapshot when computing operating points using the findop function.
You can compute a steady-state operating point of a Simulink® model by specifying constraints on the model states, outputs, and inputs, and finding a model operating condition that
Compute a steady-state operating point at specified simulation snapshot times.
Find a steady-state operating point for a Simscape™ Multibody™ model using findop with a projection-based optimizer. Results are verified using simulation.
Typically, when computing a steady-state operating point for a Simulink® model using an optimization-based search, you specify known fixed values or bounds to constrain your model
Update an existing operating point specification object with changes in the Simulink® model.