This article provides a comprehensive tutorial on Model Predictive Control (MPC) for readers with control expertise, particularly practitioners. It introduces the fundamental concepts, presents a framework for analyzing critical issues, and highlights how MPC helps practitioners address trade-offs in implementing control technology. The article covers both linear and nonlinear models, discussing their advantages and challenges. For linear models, it focuses on state-space representation and the use of linear quadratic regulators. For nonlinear models, it explores the challenges of state estimation and the trade-offs between constraint satisfaction and output performance. The article also discusses the practical implementation of MPC, including state estimation, target calculation, and the resolution of infeasibility problems. It emphasizes the importance of good state estimates and the need for reliable, predictable, and robust control in industrial applications.This article provides a comprehensive tutorial on Model Predictive Control (MPC) for readers with control expertise, particularly practitioners. It introduces the fundamental concepts, presents a framework for analyzing critical issues, and highlights how MPC helps practitioners address trade-offs in implementing control technology. The article covers both linear and nonlinear models, discussing their advantages and challenges. For linear models, it focuses on state-space representation and the use of linear quadratic regulators. For nonlinear models, it explores the challenges of state estimation and the trade-offs between constraint satisfaction and output performance. The article also discusses the practical implementation of MPC, including state estimation, target calculation, and the resolution of infeasibility problems. It emphasizes the importance of good state estimates and the need for reliable, predictable, and robust control in industrial applications.