Model predictive control (MPC), also called receding horizon control, is a very successful and widely applied modern control technology. Its basic idea is as follows: at each sampling instant, the future behavior of the system is predicted over some finite horizon using some prediction model, and an open-loop optimal control problem is solved to determine the optimal input trajectory over this time horizon. Then, the first part of this optimal input is applied to the system until the next sampling instant, at which the horizon is shifted and the whole procedure is repeated again. The main advantages of MPC and the reasons for its widespread success include (i) guarantees for closed- loop satisfaction of hard input and state constraints, (ii) the possibility to directly include the optimization of some performance criterion in the controller design, and (iii) its applicability to nonlinear systems with possibly multiple inputs. In recent years, significant progress has been made in establishing various guarantees of nonlinear model predictive controllers such as closed-loop stability, robustness, and performance. The goal of this course is to give an introduction to the field of nonlinear model predictive control, covering both basic results as well as current research topics such as economic and distributed MPC. Also, purely data-driven predictive control schemes that do not use a (parametric) model of the system will be discussed. The lectures will be accompanied by programming exercises. Topics- Stability in MPC with terminal constraints - Stability and performance in MPC without terminal constraints - Robust MPC - Economic MPC - Data-driven MPC