This article provides a tutorial overview of model predictive control (MPC), aimed at practitioners with control expertise. It introduces the concepts of MPC, discusses its framework, and highlights how it allows practitioners to address trade-offs in control technology. The article reviews the MPC research literature, noting that while it is large, several review articles and books have been published. It also discusses the use of linear and nonlinear models in MPC, emphasizing the importance of process models in forecasting and control. The essence of MPC is to optimize forecasts of process behavior using a process model. Linear models are often used in early industrial applications, and they are typically represented in state-space form. The article discusses the advantages of using state-space models, including their ease of generalization to multivariable systems and their compatibility with linear systems theory. It also addresses the challenges of incorporating constraints into MPC, which leads to nonlinear control and new control technologies. Nonlinear models are used in MPC to improve control performance by enhancing the quality of forecasts. The article discusses the use of nonlinear models in MPC, noting that they can provide better performance for processes operated over large regions of the state space. The article also addresses the challenges of identifying nonlinear models and the importance of state estimation in MPC. The article discusses the implementation of MPC with linear models, focusing on formulating MPC as an infinite horizon optimal control strategy with a quadratic performance criterion. It describes the steps involved in the MPC algorithm, including obtaining state and disturbance estimates, determining steady-state targets, solving the regulation problem, and repeating the process for subsequent time steps. The article also discusses the multiobjective nature of infeasibility problems in MPC, highlighting the trade-offs between the size and duration of constraint violations. It presents examples of how different controllers can resolve infeasibility problems, emphasizing the importance of choosing the appropriate controller for the specific application. Finally, the article discusses the importance of state estimation in MPC, noting that accurate state estimates are crucial for effective control. It also addresses the challenges of implementing nonlinear MPC, emphasizing the need for good state estimates and the importance of theoretical and computational considerations in the design of MPC systems.This article provides a tutorial overview of model predictive control (MPC), aimed at practitioners with control expertise. It introduces the concepts of MPC, discusses its framework, and highlights how it allows practitioners to address trade-offs in control technology. The article reviews the MPC research literature, noting that while it is large, several review articles and books have been published. It also discusses the use of linear and nonlinear models in MPC, emphasizing the importance of process models in forecasting and control. The essence of MPC is to optimize forecasts of process behavior using a process model. Linear models are often used in early industrial applications, and they are typically represented in state-space form. The article discusses the advantages of using state-space models, including their ease of generalization to multivariable systems and their compatibility with linear systems theory. It also addresses the challenges of incorporating constraints into MPC, which leads to nonlinear control and new control technologies. Nonlinear models are used in MPC to improve control performance by enhancing the quality of forecasts. The article discusses the use of nonlinear models in MPC, noting that they can provide better performance for processes operated over large regions of the state space. The article also addresses the challenges of identifying nonlinear models and the importance of state estimation in MPC. The article discusses the implementation of MPC with linear models, focusing on formulating MPC as an infinite horizon optimal control strategy with a quadratic performance criterion. It describes the steps involved in the MPC algorithm, including obtaining state and disturbance estimates, determining steady-state targets, solving the regulation problem, and repeating the process for subsequent time steps. The article also discusses the multiobjective nature of infeasibility problems in MPC, highlighting the trade-offs between the size and duration of constraint violations. It presents examples of how different controllers can resolve infeasibility problems, emphasizing the importance of choosing the appropriate controller for the specific application. Finally, the article discusses the importance of state estimation in MPC, noting that accurate state estimates are crucial for effective control. It also addresses the challenges of implementing nonlinear MPC, emphasizing the need for good state estimates and the importance of theoretical and computational considerations in the design of MPC systems.