The goal of most current advanced control systems is to guide a process to a target setpoint rapidly and reliably. Model predictive control has become a popular technology in many applications because it can handle large, multivariable systems subject to hard constraints on states and inputs. The optimal steady-state setpoint is usually provided by some other information management system that determines, among all steady states, which is the most profitable. For an increasing number of applications, however, this hierarchical separation of information and purpose is no longer optimal or desirable. A recently proposed alternative to the hierarchical decomposition is to take the economic objective directly as the objective function of the control system. In this approach, known as economic MPC, the controller optimizes directly in real time the economic performance of the process, rather than tracking to a setpoint. The purpose of this tutorial is to explain how to design these kinds of control systems and what kinds of closed-loop properties one can achieve with them. We cover the following issues: asymptotic average performance; closed-loop stability and convergence, strong duality and dissipativity; designing terminal costs, terminal regions, and terminal periodic constraints. Several examples are included to illustrate these results.