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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

A Set-Based Optimal Control Approach for Pharmacokinetic / Pharmacodynamic Drug Dosage Design
Citation key lucia2016_dycops
Author Lucia, S. and M. Schliemann-Bullinger and R. Findeisen and E. Bullinger
Title of Book Proc. of the 11th IFAC Symposium on Dynamics and Control of Process Systems Including Biosystems
Pages 797 - 802
Year 2016
Abstract Abstract Robust optimal control of pharmacokinetic/pharmacodynamic (PK/PD) models allows for optimal drug dosage design under model uncertainties. Typical PK/PD models are highly uncertain and therefore a robust design of the drug dosage is necessary to guarantee that important health-related constraints are satisfied for all the possible values of the uncertainty. This paper shows that the responses of a simple class of PK/PD models are monotonous in the parameters and in the input. This greatly simplifies the design of a scenario-based nonlinear model predictive controller as just two scenarios are necessary to fully bound the system responses. This is illustrated with the drug dosage design for a model of erythropoietin injection, in which we also show the potential benefit of having intermediate measurements during the application of the therapy.
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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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