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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

Adaptive Multi-stage Output Feedback NMPC using the Extended Kalman Filter for time varying uncertainties applied to a CSTR
Citation key subramanian2015_nmpc
Author S. Subramanian and Lucia, S. and S. Engell
Title of Book Proc. of the 5th IFAC Conference on Nonlinear Model Predictive Control
Pages 242 - 247
Year 2015
ISSN 2405-8963
Abstract Nonlinear Model Predictive control (NMPC) is one of the advanced control strategies for multi-dimensional nonlinear systems with constraints. With uncertainties present in the model, robust NMPC strategies are proposed in order to counteract the effects of the uncertainties and have a safe operation of the plant.Multi-stage NMPC offers a non-conservative alternative as it models the feedback information explicitly in the problem formulation by means of a scenario tree. In order to be robust to both the model uncertainties and the estimation error, we formulate a multi-stage output feedback NMPC strategy by creating additional scenarios by sampling the innovations and use observer equations to predict the future evolution of the plant. Since the observers such as the Extended Kalman Filter (EKF) can be used to estimate the uncertain parameters along with the states, the output feedback NMPC strategy is improved to be adaptive with respect to time varying uncertain parameters and the performance of the controller is improved. We demonstrate the advantages of the proposed adaptive scheme using a nonlinear Continuous Stirred Tank Reactor (CSTR) example.
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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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