Linearization Techniques for Control Design
Learn how to use Simulink® and Simscape™ tools together to model physical processes and design control systems. Linearizing electrical models is an important step in a wider control design workflow. You can use different techniques to linearize electrical models and tune controllers.
Benefits of Linearization
When you linearize a model, you create a linear approximation of a linear or nonlinear system. This approximation is valid in a small region around a particular operating or trim point, a steady-state condition in which all model states are constant. You need to use linearization to evaluate or design control systems using classical design techniques. By linearizing models, you can:
Use tools intended for linear controller design.
Determine the stability of control systems using Bode plots and other frequency analysis plots.
Create reduced-order models, which have lower computational requirements and run faster than their corresponding nonlinear models.
For more information about linearization, see Linearizing at an Operating Point.
After you generate a linear model, you can optimize controllers for electrical systems so that the controller:
Minimizes reactive power consumed.
Increases disturbance rejection.
Is more robust. For example, a controller that has better gain and phase margins.
For more information about tuning controllers, see Tuning Controllers.
Determine Stability of Control Systems Using Bode Analysis
You can use Bode plots to assess the stability of a control system. A Bode plot describes the frequency response of a linear, time-invariant system.
To generate Bode plots and achieve your desired closed-loop system performance by graphically shaping the open-loop frequency response, you can use these functions and apps:
You can also calculate the magnitude and phase from the state-space representations and generate Bode plots by running a series of MATLAB® commands. To learn how you can use these tools for Bode analysis, see Examples.
Some models are continuous, meaning that they have no abrupt discontinuities such as
saturations or dead-bands. You can linearize continuous models easily. For example, you can
use the steady-state solver to find an operating point and the
linmod function to extract a linear model. To learn how you can linearize
continuous models, see Examples.
linmod function provides basic linearization functionality.
For full linearization functionality, use Simulink
Control Design™ software. For more information about the tools you can use for
linearization, see Choose Linearization Tools (Simulink Control Design).
Discontinuities are common in electrical networks because switches have a binary, on or off, state. For example, circuits driven by pulse-width modulation usually have high-frequency switching components, such as the metal-oxide-semiconductor field-effect transistor (MOSFET). You cannot linearize models with discontinuities exactly.
To find a linear approximation of models with discontinuities, you first need to
approximate the discontinuous signal with a continuous approximation. If your model contains a
converter block, you can use the
Averaged Switch option to simplify your
model so that you can linearize it. For more information about converter blocks which support
Averaged Switch option, see Linearize Models with Converters Using Averaged Switching.
For more complex models with discontinuities, you can estimate a linear model from the results of a dynamic simulation. For more information, see Estimate Linear Models from Simulation Results.
For more information about the tools you can use for linearization, see Choose Linearization Tools (Simulink Control Design).
These examples explore the linearization and analysis techniques that you can apply to
models without discontinuities. These techniques include using the
function and the Model Linearizer app to extract a linear model.
The Linearize an Electronic Circuit example shows how to linearize a model of a nonlinear, bipolar transistor circuit and create a Bode plot for small-signal frequency-domain analysis.
The Linearize a Plant Model for Use in Feedback Control Design example shows how to linearize a hydraulic plant model to support control system stability analysis and design.
MathWorks® provides tools that you can use to tune your controller after you generate a linear model. These examples show two different techniques for tuning the current and velocity loops in a linear electric actuator with saturation limits:
The Control of a Linear Electric Actuator Using Control System Tuner (Simulink Control Design) example shows how to use the Control System Tuner (Control System Toolbox) app.
You can also automatically tune the gains of a proportional-integral-derivative (PID) controller for a single-input-single-output (SISO) plant to achieve a balance between performance and robustness, using the PID Tuner (Control System Toolbox) app. If the PID Tuner app cannot linearize the plant at the operating point you want to use for tuning, you can use the Frequency Response Based PID Tuner app. The Frequency Response Based PID Tuner app finds a linear approximation of your model and tunes your controller by running a dynamic simulation. You can apply this method to models with discontinuities. For more information about the Frequency Response Based PID Tuner app, see Frequency-Response Based Tuning (Simulink Control Design). For more information about advanced techniques for tuning controllers that you can apply to models with discontinuities, see Tune Controllers by Running Dynamic Simulation.
- Model Linearizer (Simulink Control Design)
- Linearize an Electronic Circuit
- Linearize a Plant Model for Use in Feedback Control Design
- Control of a Linear Electric Actuator (Simulink Control Design)
- Control of a Linear Electric Actuator Using Control System Tuner (Simulink Control Design)