Model Predictive Control Toolbox 3.1.1
Product Description
- Introduction and Key Features
- Working with the Model Predictive Control Toolbox
- Defining Internal Plant Models
- Designing Controllers
- Simulating Closed-Loop Behavior
- Deploying Model Predictive Controllers
Defining Internal Plant Models
Model predictive controllers base their control actions on an internal plant model of the process. The internal model lets the controller forecast future process behavior and respect output constraints. The ability to model process interactions makes model predictive control easier to maintain and often better performing than multiple proportional-integral-derivative (PID) control loops, which require individual tuning and other techniques to reduce loop coupling.
Model Predictive Control Toolbox uses linear time invariant (LTI) models, enabling you to use transfer function model structures common to all MathWorks control system design products. You can import multiple LTI models into the toolbox from the MATLAB workspace or a MAT-file. The toolbox also lets you directly import multiple models estimated in System Identification Toolbox.
Using Simulink Control Design™ and Simulink, you can extract a linearized form of the Simulink model that is automatically imported as the internal plant model of the controller.
Plant Model Importer for bringing a model into the toolbox either from the MATLAB workspace or a MAT-file. Click on image to see enlarged view. |
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