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I'm broadly interested in the mathematical theory of control and dynamical systems, more specifically:
Numerical Optimal Transport (see the text --- PeyCut)
Optimal Control (see the excellent introductory articles -- OptCon1, OptCon2)
Approximation theory (a nice introductory text -- CheLig:AppTheo)
Model predictive control (a nice introductory text -- GruPan:MPC)
Differential Geometry (Lee's standard texts)
Probability and learning theory (see Francis Bach's notes on learning theory)
Robust maximum hands-off optimal control: necessary conditions, equivalence, and numerical algorithms
We developed a robust version of the well-known maximum hands-off control principle for sparse optimal control. For a class of uncertain systems, we leverage results from a robust Pontryagin-type maximum principle to theoretically demonstrate that the robust L^0 and robust L^1 problems are equivalent. Moreover, by leveraging tools from robust optimization, we provide a numerical algorithm for their efficient and tractable solutions.
Publications:
Robust maximum hands-off control: existence, robust Pontryagin maximum principle, and equivalence
Under review, Automatica.
S. Ganguly, A. Aravind, S. Das, M. Nagahara, D. Chatterjee
Sparse robust optimal control: theory and numerics
Under review, IEEE Transactions on Automatic Control.
Minmax density transportation of PDE-constrained optimal control problems
Publications:
Minmax density transportation for parabolic PDEs: a direct optimal control perspective.
Under review, IEEE Transactions on Automatic Control.
Data-driven Gromov-Wasserstein density transportation
We devised a data-driven density transportation algorithm tailored for unknown linear systems, aiming to morph an initial density into a prescribed terminal form while preserving its intrinsic structure. To achieve this, we harness the power of the Gromov-Wasserstein optimal transport distance in concert with state-of-the-art data-driven methodologies, ensuring precision and adaptability.
Publications:
H. Nakashima, S. Ganguly, K. Kashima
(extended arXiv version) Data-driven Gromov-Wasserstein Density Steering
To be presented at the IEEE CDC, Rio de Janeiro, 2025.
Formation shape control via controlled optimal transport
Given a set of agents (for example a swarm of robots) sometimes a relevant objective is to achieve a specific shape irrelevant of the distance, rotation, or angle between the agents. Only the shape is important. Employing the Gromov-Wasserstein optimal transport metric, in this work, we developed a controlled optimal transport-driven algorithm to control the shape of a group of agents in a formation.
Publications:
H. Nakashima, S. Ganguly, K. Morimoto, K. Kashima
(arXiv version) Formation shape control using the Gromov-Wasserstein metric.
Learning for dynamics and control conference (L4DC), Proceedings of the Machine Learning Research (PMLR), Michigan, USA, 2025.
Data-driven distributionally robust MPC via semi-definite semi-infinite programming
Publications:
S. Das, S. Ganguly, A. Aravind, D. Chatterjee
(doi, extended arXiv version) Data-driven distributionally robust MPC via semi-infinite semidefinite programming: an application to finance
Mathematical Theory of Networked Systems (MTNS), 2024, Aug, 19-23, Cambridge
Fast and explicit solutions to robust model predictive control problems
Publications:
Journals:
(doi, extended arXiv version) Explicit feedback synthesis driven by quasi-interpolation for nonlinear robust model predictive control.
IEEE Transactions on Automatic Control,.
(doi) Exact solutions to minmax optimal control problems for constrained noisy linear systems
IEEE Control Systems Letters (LCSS).
S. Ganguly, S. Gupta, D. Chatterjee
Data-driven learning of constrained feedbacks for explicit robust predictive control: an approximation theoretic view.
Under review.
QuITO: Constrained trajectory optimization for ODE-constrained nonlinear optimal control problems.
Publications:
Journals:
(arXiv version) QuITO v.2: Numerical solutions with Uniform Error Guarantees to Optimal Control Problems under Path Constraints
To appear in IEEE Transactions on Automatic Control, 2025.
S. Ganguly, N. Randad, R.A. D'Silva, Mukesh. Raj. S, D. Chatterjee
(doi) QuITO: Numerical software for constrained nonlinear optimal control problems.
SoftwareX, Vol 24, 2023,
Conferences:
S. Das, S. Ganguly, A. Muthyala, D. Chatterjee
(doi) Towards continuous-time constrained MPC: a novel trajectory optimization algorithm
IEEE Conference on Decision & Control (CDC-2023), Dec 13-15, Singapore, extended arXiv version.
(doi) Constrained trajectory synthesis via quasi-interpolation.
IEEE conference on decision and control (CDC-2022), Dec 6-9, Cancun, Mexico.
Patent granted:
Method and trajectory management controller for constrained trajectory optimization
Patent no 430622, application no. 202221040362, The Patent Office Journal No. 18/2023, Dated 05/05/2023.
Software packages
QuITO v.1: a MATLAB-based numerical package with a GUI for solving direct trajectory optimization and constrained nonlinear optimal control problems.
GitHub Repository (written jointly with Nakul Randad, Rihan Aaron D'Silva, and Mukesh S.).
QuITO v.2: new transcription algorithm with automatic change point detection and mesh refinement modules.
GitHub Repository (written jointly with Rihan Aaron D'Silva).
Rate constrained Pontryagins maximum principle on Euclidean spaces
Publications:
Journal:
(doi, extended arXiv version) Discrete-time Pontryagin maximum principle under rate constraints: Necessary conditions for optimality.
Asian Journal of Control, 2024,
Conference:
(doi) Rate constrained discrete-time maximum principle
7th IFAC Workshop on Lagrangian and Hamiltonian Methods for Nonlinear Control, Berlin, Germany, Oct 2021.
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