Get Started with Model Predictive Control Toolbox
Model Predictive Control Toolbox™ provides functions, an app, Simulink® blocks, and reference examples for developing model predictive control (MPC). For linear problems, the toolbox supports the design of implicit, explicit, adaptive, and gain-scheduled MPC. For nonlinear problems, you can implement single- and multi-stage nonlinear MPC. The toolbox provides deployable optimization solvers and also enables you to use a custom solver.
You can evaluate controller performance in MATLAB® and Simulink by running closed-loop simulations. For automated driving, you can also use the provided MISRA C®- and ISO 26262-compliant blocks and examples to quickly get started with lane keep assist, path planning, path following, and adaptive cruise control applications.
The toolbox supports C and CUDA® 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.