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Prof. Ralph M. Kennel IEEE Fellow, IET Fellow Technical University of Munich |
Biography
Ralph M. Kennel received his Dr.-Ing. (Ph.D.) degree from the University of Kaiserslautern in 1984. From 1983 to 1999 he worked for Robert BOSCH GmbH (Germany). From 1999 to 2008 he was Professor for Electrical Machines and Drives at Wuppertal University (Germany). Since 2008 he has been Professor for Electrical Drive Systems and Power Electronics at Technische Universitaet Muenchen (Germany). Dr. Kennel is a Senior Member of IEEE, a Fellow of IET (former IEE) and a Chartered Engineer in the UK. Within IEEE he is Treasurer of the Germany Section. In 2018 Dr. Kennel received the Doctoral degree honoris causa from Universitatea Stefan cel Mare in Suceava (Romania). Dr. Kennel has received in 2013 the Harry Owen Distinguished Service Award from IEEE-PELS, the EPE Association Distinguished Service Award in 2015 as well as the EPE Outstanding Achievement Award in 2019. Dr. Kennel was appointed “Extraordinary Professor” by the University of Stellenbosch (South Africa) from 2016 to 2019 and as “Visiting Professor” at the Haixi Institute by the Chinese Academy of Sciences from 2016 to 2021. There he was appointed as "Jiaxi Lu Overseas Guest Professor" in 2017. In 2018 Dr. Kennel was appointed Guest Professor at Harbin Institute of Technology (HIT), Harbin, China. In 2019 Dr. Kennel was appointed "Honorary Chair Professor" ("distinguished visiting professor") at Shandong University in Jinan, China.
Title
Predictive Control of Power Converters and Drives – Control Concept for the Future?
Abstract
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 in comparison to 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. Furthermore, there is no need to linearize the models – whatever is known about the system to be controlled, can be used for the model – even tables. The underlying concepts are intuitive, the resulting controllers are inherently stable and, once calculated, easy to implement. Research works have demonstrated that it is possible to use Predictive Control to control electrical energy with the use of power converters, without using modulators and linear controllers. This keynote will introduce the basic principles of MPC, and it is going to point out in which areas further progress of MPC can be expected. Meanwhile, the circumstances under which MPC is superior to conventional (linear) control and the possibilities for the future of MPC will also be discussed.