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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

Improved Design of Nonlinear Model Predictive Controllers
Citation key lucia2015_nmpc_krener
Author Lucia, S. and Rumschinski, P. and Krener, A.J. and Findeisen, R.
Title of Book Proc. of the 5th IFAC Conference on Nonlinear Model Predictive Control
Pages 254 - 259
Year 2015
Abstract One way to ensure recursive feasibility, stability and performance of Nonlinear Model Predictive Control is the combined use of a terminal region and a terminal cost. However, finding suitable combinations of the terminal cost and terminal region that guarantee closed-loop stability for nonlinear systems is in general challenging. Most existing methods are either based on the linearized system dynamics and a linear feedback, or assume that a control Lyapunov function for the system close to the origin is know. This paper proposes the use of higher order approximations of the optimal feedback and optimal cost of the infinite horizon problem via AlbrekhtMethod to determine a suitable terminal region for polynomial systems. To do so, the stability conditions are reformulated in terms of a sumof-squares problem which is iteratively used to determine the terminal region. For a nonlinear chemical reactor example it is shown that the proposed approach leads to a larger terminal region and an improved performance compared to existing approaches.
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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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