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TU Berlin

Inhalt des Dokuments

Publications

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

Potential and Limitations of Multi-stage Nonlinear Model Predictive Control
Citation key lucia2015_adchem
Author Lucia, S. and Engell, S.
Title of Book Proc. of the 9th IFAC Symposium on Advanced Control of Chemical Processes
Pages 1015 - 1020
Year 2015
ISSN 2405-8963
Abstract Multi-stage Nonlinear Model Predictive Control (NMPC) is a promising strategy for the design of robust NMPC controllers which is based on describing the evolution of the uncertainty as a scenario tree. The scenario tree makes it possible to consider explicitly that the future control inputs can be adapted to the future information (measurements), thus reducing the conservativeness of the robust approach. This paper reviews the multi-stage approach and illustrates its main advantages using a nonlinear CSTR example. We also provide guidelines for possible multi-stage NMPC users that could help to identify the problems where the use of multi-stage NMPC can result in a significant improvement with respect to standard NMPC or other robust NMPC approaches. Finally, we summarize the different modifications that can be done to the multi-stage approach to enhance its performance. The possible enhancements include: improved performance using parameter estimation, rigorous guarantee of constraint satisfaction, and stability guarantees for the case of discrete-valued uncertainties.
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Dissertation

You can download a copy of my Dissertation, entitled Robust Multi-stage Nonlinear Model Predictive Control, clicking here

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