Nonlinear Model Predictive Control: Introduction and Current Topics

Linear model predictive control is popular since the 70s of the past century and by now widely employed in practice. The 90s have witnessed a steadily increasing attention from control theoreticians as well as control practitioners in the area of nonlinear model predictive control (NMPC) and over the past decade significant theoretical as well as implementational advances in the area of NMPC have been achieved. The focus of this tutorial is twofold. Besides an in depth introduction to the basic ideas and principles of (nonlinear) predictive control current application and research issues in NMPC spanning from stability and robustness, output-feedback, efficient numerical solution as well as implementation aspects are discussed. For this purpose the course is split up in six parts. The first part provides an introduction as well as a historical review of (nonlinear) predictive control, often also referred to as receding horizon control or moving horizon control. Part two focuses on how to achieve nominal stability of the closed-loop using NMPC. In part three the robustness as well as the robust design of NMPC are discusses. Part four provides an overview on output-feedback in conjunction with NMPC. The efficient numerical solution and implementation of NMPC is discussed in depth in part five. Part six discusses existing applications as well as application aspects of NMPC. The tutorial is concluded by a short wrap up, summary and outlook.