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

Simulating Closed-Loop Behavior

You can simulate your controller in MATLAB or Simulink to evaluate its performance.

Simulating in MATLAB

You can use MATLAB functions or the Control and Estimation Tools Manager to run closed-loop simulations of your model predictive controller against linear plant models. The Control and Estimation Tools Manager lets you set up multiple simulation scenarios. For each scenario you can input controller set points and unmeasured disturbances from the following signal profiles:

• Constant
• Step
• Pulse
• Ramp
• Sine
• Gaussian

Configuring and running a simulation to test a controller using the Control and Estimation Tools Manager. Click on image to see enlarged view.

You can compare controller and plant model configurations to judge the effects of model mismatch and different weighting factors on constraints and variables. Constraints can be disabled to evaluate the characteristics of the closed-loop dynamics, such as stability and damping.

Simulating in Simulink

You can use Simulink blocks provided with Model Predictive Control Toolbox to run closed-loop simulation of your model predictive controller against a nonlinear Simulink model.

Setting constraints on manipulated and output variables with the Control and Estimation Tools Manager. Click on image to see enlarged view.