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TU Berlin

Inhalt des Dokuments


You can find below a list of my publications.

You can also visit my google scholar profile [link]

Journal publications

Lindhorst, H., Lucia, S., Findeisen, R. and Waldherr, S. (2019). Modeling Enzyme Controlled Metabolic Networks in Rapidly Changing Environments by Robust Optimization. IEEE Control Systems Letters, 248–253.

Lucia, S., Navarro, D., Lucia, O., Zometa, P. and Findeisen, R. (2018). Optimized FPGA Implementation of Model Predictive Control Using High Level Synthesis Tools. IEEE Transactions on Industrial Informatics, 137–145.

Thangavel, S., Lucia, S., Paulen, R. and Engell, S. (2018). Dual Robust Nonlinear Model Predictive Control: A Multi-stage Approach. Journal of Process Control, 39–51.

Lucia, S., Navarro, D., Karg, B., Sarnago, H. and Lucia, O. (2018). Deep Learning-based Model Predictive Controlfor Resonant Power Converters. IEEE Transactions on Industrial Informatics (submitted), 137–145.

Karg, B. and Lucia, S. (2018). Efficient representation and approximation of model predictive control laws via deep learning. arXiv preprint arXiv:1703.02702

Lucia, S., Tatulea-Codrean, A., Schoppmeyer, C. and Engell, S. (2017). Rapid Development of Modular and Sustainable Nonlinear Model Predictive Control Solutions. Control Engineering Practice, 51-62.

Lucia, S., Kögel, M., Zometa, P., Quevedo, D. E. and Findeisen, R. (2016). Predictive control, embedded cyberphysical systems and systems of systems – A perspective. Annual Reviews in Control, 193–207.

Marti, R., Lucia, S., Sarabia, D., Paulen, R., Engell, S. and de Prada, C. (2015). Improving scenario decomposition algorithms for robust nonlinear model predictive control. Computers & Chemical Engineering, 30 - 45.

Lucia, S., Andersson, J., Brandt, H., Diehl, M. and Engell, S. (2014). Handling Uncertainty in Economic Nonlinear Model Predictive Control: a Comparative Case-study. Journal of Process Control, 1247-1259.

Finkler, T., Lucia, S., Dogru, M. and Engell, S. (2013). Simple Control Scheme for Batch Time Minimization of Exothermic Semibatch Polymerizations. Industrial & Engineering Chemistry Research, 5906-5920.

Lucia, S., Finkler, T. and Engell, S. (2013). Multi-stage Nonlinear Model Predictive Control Applied to a Semi-batch Polymerization Reactor under Uncertainty. Journal of Process Control, 1306-1319.

Conference publications

Contract-based Predictive Control of Distributed Systems with Plug and Play Capabilities
Citation key lucia2015_contracts
Author Lucia, S. and M. Kögel and R. Findeisen
Title of Book Proc. of the 5th IFAC Conference on Nonlinear Model Predictive Control
Pages 205 - 211
Year 2015
Abstract We address the problemof controlling interconnected, possibly large-scale systems of systems using distributed model predictive control. We consider the case in which the nonlinear subsystems can be coupled physically and can have shared constraints. The presented approach is based on the transmission of contracts between neighboring subsystems. Contracts are guaranteed sequences of possible future trajectories or trajectory sets of the coupling variables of the subsystems. We derive for the approach sufficient conditions for guaranteeing recursive feasibility and Input-to-State stability. Furthermore, we discuss the case of the so-called Plug & Play operations in which a subsystem in the network is replaced by a new, possibly different, one and when a subsystem is removed or added to the network.
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You can download a copy of my Dissertation, entitled Robust Multi-stage Nonlinear Model Predictive Control, clicking here

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