Dynamical Systems, Control and Optimization

Space Greenhouse

Picture : schematic view of the space greenhouse

The Dynamical Systems, Control and Optimization group gathers about a dozen professors and over 30 PhD students and postdoctoral researchers.

Principal Investigators :

Pierre-Antoine AbsilVincent Blondel, Frédéric Crevecoeur, Jean-Charles Delvenne, Yves Deville, Denis DochainFrançois Glineur, Julien Hendrickx, Raphaël Jungers, Philippe Lefèvre, Yurii Nesterov, Pierre Schaus, Vincent Wertz

Research Areas :

Identification of dynamical systems is one of the first steps in the study of dynamical systems, since it addresses the issue of finding an appropriate model for its input/output behavior. Much of our work on identification has focused on understanding the connections between, identifiability, informative experiments, the information matrix and the minimization of a prediction error criterion.

Several new multi-agent models have been proposed and studied with behavior reminiscent of the partial entrainment behavior of the Kuramoto-Sakaguchi model, but with a greater potential for analysis and with applications to systems not related to coupled oscillators. The main emphasis on these dynamic models is to analyze the asymptotic clustering behavior. The analysis of such models is relevant in the study of opinion formation, interconnected water basins, platoon formation in cycling races, and the minimum cost flow problem.

We study fundamental issues in modeling, control design and stability analysis of physical networks described by hyperbolic systems of conservation laws and by distributed parameter systems modeling e.g. tubular reactors. We also study problems related to optimal prediction of nonlinear systems, such as the flow in channels (modeled by Saint-Venant equations), the modeling of the water level in water basins in order to prevent flooding and the prediction and control of traffic jams.

Optimization techniques play a fundamental role in the area of dynamical systems and they are being developed and analyzed at several levels, depending on the type of variables one wishes to optimize. Variables can be discrete (as in graph theoretic problems) or continuous (as in parametric optimization), but can also be infinite dimensional (as in optimal control over function spaces) and constrained (as in optimization on manifolds or on cones). The group has activities in each of these areas and also develops special purpose numerical techniques for dealing efficiently with such problems.

The activities here include microbial ecology and the modeling of wastewater treatment, including applications to various biological wastewater systems. We developed population balance models covering a large spectrum of applications in the industry of polymer production, crystallization, biotechnology or any process in which the size distribution of particles is essential for process quality. We also study the design and application of observers converging in finite time for a class of fed-batch processes.

We combine theoretical and experimental approaches to investigate the neural control of movement and its interactions with our environment. The mathematical models that are developed are based on experimental results from both normal and pathological subjects (clinical studies) and focus on the interaction between different types of eye movements and on eye/hand coordination. Our main research objective is to gain further insight into the nature and characteristics of high-level perceptual and motor representations in the human brain. 

Most recent publications

Below are listed the 10 most recent journal articles and conference papers produced in this research area. You also can access all publications by following this link : see all publications.

Journal Articles

1. Pinto, Samuel. C; Andersson, Sean.B; Hendrickx, Julien; Cassandras, Christos.G. Multi-Agent Persistent Monitoring of Targets with Uncertain States. In: IEEE Transactions on Automatic Control, (2022). (Accepté/Sous presse). http://hdl.handle.net/2078.1/257423

2. Monnoyer De Galland De Carnières, Charles; Hendrickx, Julien. Fundamental Performance Limitations for Average Consensus in Open Multi-Agent Systems. In: IEEE Transactions on Automatic Control, (2022). (Accepté/Sous presse). http://hdl.handle.net/2078.1/257419

3. Van Hoorebeeck, Loïc; Absil, Pierre-Antoine; Papavasiliou, Anthony. Solving non-convex economic dispatch with valve-point effects and losses with guaranteed accuracy. In: International Journal of Electrical Power & Energy Systems, Vol. 134, p. 107143 (2022). doi:10.1016/j.ijepes.2021.107143. http://hdl.handle.net/2078.1/249814

4. Smolak, Kamil; Siła-Nowicka, Katarzyna; Delvenne, Jean-Charles; Wierzbiński, Michał; Rohm, Witold. The impact of human mobility data scales and processing on movement predictability. In: Scientific Reports, Vol. 11, no.1 (2021). doi:10.1038/s41598-021-94102-x. http://hdl.handle.net/2078.1/257411

5. Faccin, Mauro; Schaub, Michael. T; Delvenne, Jean-Charles. State Aggregations in Markov Chains and Block Models of Networks. In: Physical Review Letters, Vol. 127, no.7 (2021). doi:10.1103/physrevlett.127.078301. http://hdl.handle.net/2078.1/257408

6. Freitas, Nahuel; Delvenne, Jean-Charles; Esposito, Massimiliano. Stochastic Thermodynamics of Nonlinear Electronic Circuits: A Realistic Framework for Computing Around kT. In: Physical Review X, Vol. 11, no.3 (2021). doi:10.1103/physrevx.11.031064. http://hdl.handle.net/2078.1/257406

7. Nguyen, Thanh Son; Absil, Pierre-Antoine; Gao, Bin; Stykel, Tatjana. Symplectic eigenvalue problem via trace minimization and Riemannian optimization. In: SIAM Journal on Matrix Analysis and Applications, Vol. 42, no.4, p. 1732-1757 (2021). doi:10.1137/21m1390621. http://hdl.handle.net/2078.1/255487

8. Gao, Bin; Absil, Pierre-Antoine. A Riemannian rank-adaptive method for low-rank matrix completion. In: Computational Optimization and Applications, (2021). doi:10.1007/s10589-021-00328-w. http://hdl.handle.net/2078.1/253601

9. Shi, Mingming. Distributed estimation of time varying bias in relative state measurements. In: IEEE Transactions on Automatic Control, , p. 1-1 (2021). doi:10.1109/tac.2021.3115429 (Accepté/Sous presse). http://hdl.handle.net/2078.1/252081

10. Wang, Lei; Gao, Bin; Liu, Xin. Multipliers Correction Methods for Optimization Problems over the Stiefel Manifold. In: CSIAM Transactions on Applied Mathematics, Vol. 2, no.3, p. 508-531 (2021). doi:10.4208/csiam-am.so-2020-0008. http://hdl.handle.net/2078.1/250709

Conference Papers

1. Hamaide, Valentin; Glineur, François. Transfer learning in Bayesian optimization for the calibration of a beam line in proton therapy. In: ESANN 2021 proceedings, 2021, 978287587082-7 xxx. http://hdl.handle.net/2078.1/254712

2. Hautecoeur, Cécile; Glineur, François; De Lathauwer, Lieven. Hierarchical alternating nonlinear least squares for nonnegative matrix factorization using rational functions. In: 2021 29th European Signal Processing Conference (EUSIPCO), IEEE Xplore, 2021, 978-9-0827-9706-0 xxx. doi:10.23919/eusipco54536.2021.9615922. http://hdl.handle.net/2078.1/254704

3. Gao, Bin; Nguyen, Thanh Son; Absil, Pierre-Antoine; Stykel, Tatjana. Geometry of the Symplectic Stiefel Manifold Endowed with the Euclidean Metric. In: Lecture Notes in Computer Science : Geometric Science of Information, Springer International Publishing,2021, 2021, 978-3-030-80208-0, p. 789-796 xxx. doi:10.1007/978-3-030-80209-7_85. http://hdl.handle.net/2078.1/249751

4. Pinto, Samuel C; Andersson, Sean B; Hendrickx, Julien; Cassandras, Christos G. Optimal Minimax Mobile Sensor Scheduling Over a Network. 2021 xxx. http://hdl.handle.net/2078.1/249179

5. Mathew, James; Lefèvre, Philippe; Crevecoeur, Frédéric. Savings in human reaching is linked to feedback adaptation. 2021 xxx. http://hdl.handle.net/2078.1/246405

6. Crevecoeur, Frédéric; Mathew, James; Lefèvre, Philippe. Force cues flexibly separate motor memories in human reaching adaptation. 2021 xxx. http://hdl.handle.net/2078.1/242195

7. Legat, Antoine; Hendrickx, Julien. Local Network Identifiability with Partial Excitation and Measurement. In: Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2020). p. 4342-4347 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249174

8. Hendrickx, Julien; Rabbat, Michael G. Stability of Decentralized Gradient Descent in Open Multi-Agent Systems. In: Proceedings of the 57th IEEE Conference on Decision and Control (CDC 2020). p. 4885-4890 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249172

9. Hendrickx, Julien; Olshevsky, Alex; Saligrama, Venkatesh. Minimax Rate for Learning From Pairwise Comparisons in the BTL Model. In: PMLR Proceedings of Machine Learning Research. Vol. 119, p. 4193-4202 (2020). 2020 xxx. http://hdl.handle.net/2078.1/249156

10. Debauche, Virginie; Jungers, Raphaël M.. On Path-Complete Lyapunov Functions : comparison between a graph and its expansion. 2020 xxx. http://hdl.handle.net/2078.1/242844