Model Predictive Control (MPC) is a conceptually simple yet powerful methodology to control power converters, electric drives and large systems, such as electrical power grids. MPC provides many advantages over traditional controllers including the capability to intuitively handle a large variety of control problems by considering different modes of operation and directly incorporating system constraints and additional requirements. The underlying concepts are intuitive, the resulting controllers are inherently stable and, once calculated, easy to implement. The advances in processing power of digital signal processors have recently promoted MPC into the first commercial applications, which opened a door toward improved performance and efficiency of power converters, drives and power grids.
The goal of this tutorial is to provide working knowledge on the development and implementation of MPC in different application fields. The introduction teaches the basic MPC principles, including mathematical techniques and optimization methods necessary to formulate and solve the control problem. The subsequent application-specific presentations address practical challenges in MPC of drives, grid connected converters, photovoltaic inverters and electrical power grids. Case studies demonstrate practical MPC controller designs, and evaluate and discuss their results. The embedded implementation section provides practical implementation guidelines by addressing hardware, software, programming methods and suitable design tools. Finally, in the last session, advanced MPC concepts are explained and demonstrated on examples.