Dr. Pablo Segovia


Pablo received his BS/MS degree in Industrial Engineering in 2015 from Universitat Politècnica de Catalunya (UPC), Spain, and a joint PhD degree in Automatic Control, Robotics and Vision in 2019 from UPC and IMT Lille Douai, France. He has held postdoctoral research appointments at IMT Lille Douai (2019–2020), Delft University of Technology, the Netherlands (2020–2023), and UPC (2023).

He is a Beatriz Galindo fellow in the Department of Automatic Control at UPC since September 2023, and a member of the Advanced Control Systems (SAC) research group of the Research Center for Supervision, Safety and Automatic Control (CS2AC).

Research Interests

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

Application to water systems, intelligent transportation systems and energy systems.

Featured Publications

Journal publications (selected)

[1] P. Segovia, V. Puig and E. Duviella. A multi-layer control strategy for the Calais canal, in IEEE Transactions on Control Systems Technology, 2023.

[2] 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.

[3] 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.

[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. 2023 IEEE Conference on Control Technology and Applications (CCTA),  2023, pp. 847–852.

[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.