Introduction to Model Predictive Control

Model predictive control (MPC) is an important advancement in control theory and is notable for its ability to effectively handle strict constraints on controls and states while achieving near-optimal performance according to an objective function specified by the designer. It has been widely used in various fields such as mechatronics, robotics, power electronics, automotive systems, and smart infrastructures. Its popularity continues to grow with increasing power and affordability of computing resources. This article provides a brief overview of classic MPC schemes and the fundamental theoretical findings that form the basis for designing and implementing MPC methods. The review covers traditional MPC, as well as stochastic and economic MPC formulations. Throughout the article, we use a vehicle motion control example to help the practitioner better understand the different formulations. The complexity of the example gradually increases in accordance with the different MPC schemes. Finally, we suggest future research directions from various perspectives to further advance MPC for a wide range of applications in the future.