direkt zum Inhalt springen

direkt zum Hauptnavigationsmenü

Sie sind hier

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

Robust output feedback NMPC with guaranteed constraint satisfaction
Citation key subramanian2015_rocond
Author S. Subramanian and Lucia, S. and R. Paulen S. and Engell
Title of Book Proc. of the 8th IFAC Symposium on Robust Control Design
Pages 326 - 331
Year 2015
Abstract Nonlinear Model Predictive Control (NMPC) employs a plant model to compute a sequence of optimal control inputs for a finite horizon. As, in reality, there always exists a plant-model mismatch and not all states of the plant can be measured, the NMPC scheme must be robust to plant uncertainties and to estimation errors. Different robust NMPC strategies have been proposed to deal with these uncertainties. Among them, a multi-stage NMPC, which is based upon a scenario tree of future plant evolutions, is less conservative compared to worst-case open-loop approaches because the presence of feedback at future sampling instants is explicitly considered. In multi-stage output feedback NMPC, additional scenarios are created by sampling the innovations that are used to estimate the future states of the plant along the scenario tree. In this paper, we refine our previously published approach to include state estimation errors. Moreover we extend the scheme to guarantee robust constraint satisfaction by calculating reachable sets using Taylor models. The method is demonstrated for a nonlinear chemical process example.
Download Bibtex entry

Dissertation

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

Zusatzinformationen / Extras

Quick Access:

Schnellnavigation zur Seite über Nummerneingabe

Auxiliary Functions

This site uses Matomo for anonymized webanalysis. Visit Data Privacy for more information and opt-out options.

We are part of: