Dr. Pablo Segovia


Pablo Segovia received his Industrial Engineering degree (equivalent to BSc+MSc) from Universitat Politècnica de Catalunya (Barcelona, Spain) in 2015. In 2019, he received the PhD degree in Automatic Control from Universitat Politècnica de Catalunya and IMT Lille Douai (Lille, France). He then spent one year (Sept. 2019 – Aug. 2020) as a postdoctoral researcher at IMT Lille Douai. He then joined the Maritime & Transport Technology department at the Delft University of Technology, (Sept. 2020May 2023), where he worked as a postdoctoral researcher in the H2020 NOVIMOVE project.

Since June 2023, he is with Universitat Politècnica de Catalunya.

Research Interests

Large-scale systems management (non-centralized control and system partitioning), model predictive control, moving horizon estimation.

Application to water systems and transportation networks.

Featured Publications

Journal publications (selected)

[1] A Castelletti, A. Ficchì, A. Cominola, P. Segovia, M Giuliani, W. Wu, S. Lucia, C. Ocampo-Martinez, B. De Schutter and J. M. Maestre. Model Predictive Control of Water Resources Systems: A Review and Research Agenda. Annual Reviews in Control, 2023.

[2] P. Segovia, M. Pesselse, T. van den Boom and V. Reppa. Scheduling inland waterway transport vessels and locks using a switching max-plus-linear systems approach. IEEE Open Journal of Intelligent Transportation Systems, vol. 3, pp. 748-762, 2022.

[3] F. Karimi Pour, P. Segovia, E. Duviella and V. Puig. A two-layer control architecture for operational management and hydroelectricity production maximization in inland waterways using model predictive control. Control Engineering Practice, 124, 105172.

[4]  P. Segovia, V. Puig and E. Duviella. Set-membership-based distributed moving horizon estimation of large-scale systems. ISA Transactions, vol. 128, pp.402–413, 2022.

[5] P. Segovia, V. Puig, E. Duviella and L. Etienne. Distributed model predictive control using optimality condition decomposition and community detection. Journal of Process Control, 99, 54–68, 2021.

Conference papers (selected)

[6] P. Segovia, V. Puig and V. Reppa. A model predictive scheduling strategy for coordinated inland vessel navigation and bridge operation. Accepted for presentation at the 2023 7th IEEE Conference on Control Technology and Applications, 2023.

[7] N. Dann, P. Segovia and V. Reppa. Adaptive learning of inland ship power propulsion under environmental disturbances. IFAC-PapersOnLine, 55(31), 1–6, 2022. 14th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles (CAMS 2022).

[8] P. Segovia, R.R. Negenborn and V. Reppa. Vessel passage scheduling through cascaded bridges using mixed-integer programming. IFAC-PapersOnLine, 55(16), 248–253, 2022. 18th IFAC Workshop on Control Applications of Optimization (CAO 2022).

[9] P. Segovia, E. Duviella and V. Puig. Multi-layer model predictive control of inland waterways with continuous and discrete actuators. IFAC-PapersOnLine, 53(2): 16624–16629, 2020. 21st IFAC World Congress.

[10] P. Segovia, L. Rajaoarisoa, F. Nejjari, E. Duviella and V. Puig, A communication-based distributed model predictive control approach for large-scale systems. In 2019 IEEE 58th Conference on Decision and Control (CDC), Nice, France, 2019, pp. 8366–8371.