Get Started with Model Predictive Control Toolbox
Model Predictive Control Toolbox™ provides functions, an app, and Simulink® blocks for designing and simulating controllers using linear and nonlinear model predictive control (MPC). The toolbox lets you specify plant and disturbance models, horizons, constraints, and weights. By running closed-loop simulations, you can evaluate controller performance.
You can adjust the behavior of the controller by varying its weights and constraints at run time. The toolbox provides deployable optimization solvers and also enables you to use a custom solver. To control a nonlinear plant, you can implement adaptive, gain-scheduled, and nonlinear MPC controllers. For applications with fast sample rates, the toolbox lets you generate an explicit model predictive controller from a regular controller or implement an approximate solution.
For rapid prototyping and embedded system implementation, including deployment of optimization solvers, the toolbox supports C code and IEC 61131-3 Structured Text generation.
- Design Controller Using MPC Designer
Design a model predictive controller for a continuous stirred-tank reactor (CSTR) using MPC Designer.
- Design MPC Controller in Simulink
Design and simulate a model predictive controller for a Simulink model using MPC Designer.
- Design MPC Controller at the Command Line
Design and simulate a model predictive controller at the MATLAB® command line.
- Model Predictive Control of a Single-Input-Single-Output Plant
Create and simulate a model predictive controller for a SISO plant.
- Model Predictive Control of Multi-Input Single-Output Plant
Create and simulate a model predictive controller for a plant with multiple inputs and a single output.
- Model Predictive Control of a Multi-Input Multi-Output Nonlinear Plant
Create and simulate a model predictive controller for a MIMO plant.
About Model Predictive Control
- What is Model Predictive Control?
Introduction to MPC main concepts.
- MPC Signal Types
Plant inputs are independent variables that affect the plant, and plant outputs are dependent variables that you want to control or monitor.
- MPC Prediction Models
Model predictive controllers use plant, disturbance, and noise models for prediction and state estimation.
- Controller State Estimation
MPC controllers use their current state as the basis for predictions. In general, the controller states are unmeasured and must be estimated.
- Optimization Problem
Model predictive controllers compute optimal manipulated variable control moves by solving a quadratic program at each control interval.
- QP Solvers
The model predictive controller QP solvers convert an MPC optimization problem to a general form quadratic programming problem.