S
Sergio Lucia
Researcher at Technical University of Dortmund
Publications - 111
Citations - 1979
Sergio Lucia is an academic researcher from Technical University of Dortmund. The author has contributed to research in topics: Model predictive control & Computer science. The author has an hindex of 18, co-authored 93 publications receiving 1330 citations. Previous affiliations of Sergio Lucia include Otto-von-Guericke University Magdeburg & Technical University of Berlin.
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Multi-stage nonlinear model predictive control applied to a semi-batch polymerization reactor under uncertainty
TL;DR: In this paper, the authors present a robust non-conservative nonlinear model predictive control (MPC) approach based on the representation of the evolution of the uncertainty by a scenario tree, and leads to a non-ervative robust control of the uncertain plant because the adaptation of future inputs to new information is taken into account.
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Efficient Representation and Approximation of Model Predictive Control Laws via Deep Learning
Benjamin Karg,Sergio Lucia +1 more
TL;DR: It is shown that artificial neural networks with rectifier units as activation functions can exactly represent the piecewise affine function that results from the formulation of model predictive control (MPC) of linear time-invariant systems.
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Handling uncertainty in economic nonlinear model predictive control: A comparative case study
TL;DR: In this paper, a multi-stage scenario-based nonlinear model predictive control (MPC) approach is proposed to deal with uncertainties in the context of economic NMPC, and a novel algorithm inspired by tube-based MPC is proposed in order to achieve a trade-off between the variability of the controlled system and the economic performance under uncertainty.
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Rapid development of modular and sustainable nonlinear model predictive control solutions
TL;DR: In this paper, the authors propose a modularization of the NMPC implementations that facilitates the comparison of different solutions and the transition from simulation to online application, and the proposed platform supports the multi-stage robust NMPC approach to deal with uncertainty.
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Optimized FPGA Implementation of Model Predictive Control for Embedded Systems Using High-Level Synthesis Tool
TL;DR: It is argued that the implementation of MPC on field programmable gate arrays (FPGAs) using automatic tools is nowadays possible, achieving cost-effective successful applications on fast or resource-constrained systems.