Model Predictive Control Toolbox™ 2.3.1

Product Description

• Introduction and Key Features
• Working with the Model Predictive Control Toolbox
• Defining Plant Models
• Designing Controllers
• Simulating Closed-Loop Behavior
• Deploying Model Predictive Controllers

Introduction

The Model Predictive Control Toolbox lets you design, analyze, and simulate model predictive controllers that are based on plant models created in MATLAB or inferred from a linearized Simulink model. Model predictive controllers help you optimize the performance of multi-input/multi-output control systems that are subject to input and output constraints.

The toolbox provides all major features associated with model predictive control system design. To forecast the effect of input changes on the process outputs, the toolbox computes control actions using an internal plant model. You can estimate this linear time invariant (LTI) model using the System Identification Toolbox (available separately) or express it as a transfer function. The Model Predictive Control Toolbox uses quadratic programming to minimize a user-defined cost function at each time step.

Key Features

• Graphical user interface for designing and simulating model predictive controllers
• Plant models represented as LTI objects
• Block for representing model predictive controllers in Simulink
• One-step controller design using Simulink models
• Prioritized input and output variable constraints and measured and unmeasured disturbances
• Ability to deploy the controller for online applications
• Object-oriented, command-line interface shared with MathWorks control system design products

The Model Predictive Control Toolbox provides a block that lets you simulate a controller in Simulink. Click on image to see enlarged view.