University of California
Model Predictive Control (MPC) is a powerful approach for optimal control of multi-variable systems with constraints. Its concept is to repeatedly solve a Constrained Finite-Horizon Optimal Control Problem (CFHOCP) on-line by numerical optimization. The CFHOCP requires predictions of the controlled system and the environment to be made. In reality, these predictions are subject to uncertainty, resulting from model errors, disturbances, and the uncertain evolution of the environment. In this context, both robust optimization (Robust MPC) and stochastic optimization (Stochastic MPC) have been well explored for handling this uncertainty. However, both approaches have a variety of potential drawbacks, as discussed in this talk. Instead, we explore a much less treaded path that has proven to be very successful in estimation: particles, or in the context of MPC: scenarios. We will discuss the practical aspects of Scenario-Based MPC by looking at two different examples. We will also show that decision making based on scenarios leads into a deep mathematical theory. Recently, great advances were made in this field, but many open questions remain.
Georg Schildbach obtained his Masters degrees in Applied Mechanics (Dipl.-Ing.) and Industrial Engineering (Dipl. Wirtsch.-Ing.) from the Technical University of Darmstadt. He worked in various positions in the field of quantitative finance and investment banking for two years. Then he joined the Automatic Control Laboratory at ETH Zurich in 2010, where obtained his Ph.D. degree in the field of control and optimization in 2014. He currently holds the position of an Associate Director at the Hyundai of Excellence at UC Berkeley. His research interests evolve around algorithms for optimal and constrained control in uncertain environments, and their application to real-life problems in engineering and finance. His current research focus lies on developing new control algorithms for autonomous driving and intelligent transportation systems.
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