Model Predictive Control Toolbox 3.0

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

Designing Controllers

The toolbox lets you design controllers in MATLAB or in Simulink.

Designing Controllers in MATLAB

You can design multiple controllers and use simulation to determine the optimal design. For each controller design, you can select a plant model and specify the following controller parameters:

• Prediction and control horizons
• Constraints on the manipulated and output variables
• Weighting factors on input and output variables
• Models for measurement noise and for unmeasured input and output disturbances

The toolbox supports time-varying constraints and weights, off-diagonal weights, and custom unmeasured disturbance models.

Designing Controllers in Simulink

Model Predictive Control Toolbox, when used with Simulink Control Design, can generate a controller directly in a Simulink model. Using an MPC block and the appropriately connected block inputs and outputs, Simulink Control Design can extract a linearized plant model and generate a controller. Model Predictive Control Toolbox uses the same GUI to specify the controller parameters in Simulink as to design a controller in MATLAB.

You can use the Multiple MPC Controllers block for controlling a nonlinear Simulink model over a wide range of operating conditions. With this block you can design a model predictive controller for each operating point and switch between model predictive controllers at run time. The Multiple MPC Controllers block ensures bumpless control transfer from one model predictive controller to another. You can create linear plant models for controller design at each operating point either by linearizing a Simulink model with Simulink Control Design or by specifying the plant model directly.

Multiple MPC Controllers block (red) for controlling nonlinear models over a wide operating range using multiple model predictive controllers with bumpless control transfer. With this block you can design a model predictive controller for each operating point and switch between model predictive controllers at run time. Click on image to see enlarged view.