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Introduction to Model-Based PID Tuning in Simulink
Car tuning is the modification of a car to optimise it for a different set of performance requirements from those it was originally designed to meet. Most commonly this is higher engine performance and dynamic handling characteristics but cars may also be altered to provide better fuel.
![Tuning Tuning](/uploads/1/2/5/7/125715675/473155130.jpg)
You can use PID Tuner to interactively tune PID gains in a Simulink® model containing a PID Controller, Discrete PID Controller, PID Controller (2DOF), or Discrete PID Controller (2DOF) block. PID Tuner allows you to achieve a good balance between performance and robustness for either one-degree-of-freedom or two-degree-of-freedom PID controllers. When you use PID Tuner, it:
- Automatically computes a linear model of the plant in your model. PID Tuner considers the plant to be the combination of all blocks between the PID controller output and input. Thus, the plant includes all blocks in the control loop, other than the controller itself. See What Plant Does PID Tuner See?.
- Automatically computes an initial PID design with a balance between performance and robustness. PID Tuner bases the initial design upon the open-loop frequency response of the linearized plant. See PID Tuning Algorithm.
- Provides tools and response plots to help you interactively refine the performance of the PID controller to meet your design requirements. See Open PID Tuner.
For plants that do not linearize or that linearize to zero, there are several alternatives for obtaining a plant model for tuning. These alternatives include:
- Design PID Controller from Plant Frequency-Response Data — Use the frequency-response estimation command
frestimate
or the Frequency Response Based PID Tuner to obtain estimated frequency responses of the plant by simulation. - Interactively Estimate Plant from Measured or Simulated Response Data — If you have System Identification Toolbox™, you can use PID Tuner to estimate the parameters of a linear plant model based on time-domain response data. PID Tuner then tunes a PID controller for the resulting estimated model. The response data can be either measured from your real-world system, or obtained by simulating your Simulink® model.Then, download Spectrasonics Keyscape 1.1.1d Patch + Crack for Mac directly to your Mac OS X operating system.3. Also, extract and also open the read me file.4. Keyscape vst free download mac pc.
You can use PID Tuner to design one-degree-of-freedom or two-degree-of-freedom PID controllers. You can often achieve both good setpoint tracking and good disturbance rejection using a one-degree-of-freedom PID controller. However, depending upon the dynamics in your model, using a one-degree-of-freedom PID controller can require a tradeoff between setpoint tracking and disturbance rejection. In such cases, if you need both good setpoint tracking and good disturbance rejection, use a two-degree-of-freedom PID Controller.
![Tuning Tuning](/uploads/1/2/5/7/125715675/487952559.jpg)
For examples of tuning one- and two-degree-of-freedom PID compensators, see:
What Plant Does PID Tuner See?
Cosa E Auto Tuning Parts
PID Tuner considers as the plant all blocks in the loop between the PID Controller block output and input. The blocks in your plant can include nonlinearities. Because automatic tuning requires a linear model, PID Tuner computes a linearized approximation of the plant in your model. This linearized model is an approximation to a nonlinear system, which is valid in a small region around a given operating point of the system.
By default, PID Tuner linearizes your plant using the initial conditions specified in your Simulink model as the operating point. The linearized plant can be of any order and can include any time delays. The PID tuner designs a controller for the linearized plant.
In some circumstances, however, you want to design a PID controller for a different operating point from the one defined by the model initial conditions. For example:
- The Simulink model has not yet reached steady-state at the operating point specified by the model initial conditions, and you want to design a controller for steady-state operation.
- You are designing multiple controllers for a gain-scheduling application and must design each controller for a different operating point.
In such cases, change the operating point used by PID Tuner. See Opening PID Tuner.
For more information about linearization, see Linearize Nonlinear Models.
PID Tuning Algorithm
Typical PID tuning objectives include:
- Closed-loop stability — The closed-loop systemoutput remains bounded for bounded input.
- Adequate performance — The closed-loop systemtracks reference changes and suppresses disturbances as rapidly aspossible. The larger the loop bandwidth (the frequency of unity open-loopgain), the faster the controller responds to changes in the referenceor disturbances in the loop.
- Adequate robustness — The loop design has enoughgain margin and phase margin to allow for modeling errors or variationsin system dynamics.
MathWorks® algorithm for tuning PID controllersmeets these objectives by tuning the PID gains to achieve a good balancebetween performance and robustness. By default, the algorithm choosesa crossover frequency (loop bandwidth) based on the plant dynamics,and designs for a target phase margin of 60°. When you interactivelychange the response time, bandwidth, transient response, or phasemargin using the PID Tuner interface, the algorithm computesnew PID gains.
For a given robustness (minimum phase margin), the tuning algorithmchooses a controller design that balances the two measures of performance,reference tracking and disturbance rejection. You can change the designfocus to favor one of these performance measures. To do so, use the Options dialogbox in PID Tuner.
When you change the design focus, the algorithm attempts toadjust the gains to favor either reference tracking or disturbancerejection, while achieving the same minimum phase margin. The moretunable parameters there are in the system, the more likely it isthat the PID algorithm can achieve the desired design focus withoutsacrificing robustness. For example, setting the design focus is morelikely to be effective for PID controllers than for P or PI controllers.In all cases, fine-tuning the performance of the system depends stronglyon the properties of your plant. For some plants, changing the designfocus has little or no effect.
See Also
Apps
Blocks
- Discrete PID Controller | Discrete PID Controller (2DOF) | PID Controller | PID Controller (2DOF)