Model predictive control (MPC) uses the model of a system to predict its future behavior, and it solves an optimization problem to select the best control action. Watch this video to learn about the benefits of using model predictive control (MPC).

Watch all of the videos in this series about Understanding Model Predictive Control:    • Understanding Model Predictive Control  

Download this handson MPC virtual lab to practice design of model predictive controllers for an autonomous vehicle steering system: https://bit.ly/MPCvirtuallab

Learn how model predictive control (MPC) works:
Model Predictive Control Toolbox: http://bit.ly/2xgwWvN
What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M
Getting Started with Model Predictive Control Toolbox: http://bit.ly/2GskEY4

Related Resources:
How to Design an MPC Controller with Simulink and Model Predictive Control Toolbox: http://bit.ly/2Gvv0qe
Adaptive MPC Design with Simulink and Model Predictive Control Toolbox: http://bit.ly/2GsL5Nu

MPC can handle multiinput multioutput (MIMO) systems that have interactions between their inputs and outputs. Due to these interactions, it is often challenging to design MIMO systems using traditional controllers such as PID. However, MPC can simultaneously control all the outputs while taking into account inputoutput interactions.

MPC can also handle constraints. Constraints are important, as violating them may lead to undesired consequences. MPC has preview capabilities (similar to feedforward control). If set point changes are known in advance, the controller can better react to those changes and improve its performance.

Engineers have used MPC controllers in process industries since the 1980s. With the increasing computing power of microprocessors, its use has spread to many other fields including the automotive and aerospace industries.


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