Understanding Model Predictive Control, Part 1: Why Use MPC?

Learn about the benefits of using model predictive control (MPC). - Model Predictive Control Toolbox: http://bit.ly/2xgwWvN - What Is Model Predictive Control Toolbox?: http://bit.ly/2xfEe2M MPC uses the model of a system to predict its future behavior, and it solves an optimization problem to select the best control action. - Getting Started with Model Predictive Control Toolbox: http://bit.ly/2GskEY4 - 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 multi-input multi-output (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 input-output interactions. MPC can also handle constraints. Constraints are important, as violating them may lead to undesired consequences. MPC has preview capabilities (similar to feed-forward 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.