Zobrazeno 1 - 10
of 181
pro vyhledávání: '"Chakrabarty, Ankush"'
Novelty search (NS) refers to a class of exploration algorithms that automatically uncover diverse system behaviors through simulations or experiments. Systematically obtaining diverse outcomes is a key component in many real-world design problems su
Externí odkaz:
http://arxiv.org/abs/2406.03616
In this paper, we consider the problem of reference tracking in uncertain nonlinear systems. A neural State-Space Model (NSSM) is used to approximate the nonlinear system, where a deep encoder network learns the nonlinearity from data, and a state-sp
Externí odkaz:
http://arxiv.org/abs/2404.12097
Autor:
Safaoui, Sleiman, Vinod, Abraham P., Chakrabarty, Ankush, Quirynen, Rien, Yoshikawa, Nobuyuki, Di Cairano, Stefano
We consider the problem of safe multi-agent motion planning for drones in uncertain, cluttered workspaces. For this problem, we present a tractable motion planner that builds upon the strengths of reinforcement learning and constrained-control-based
Externí odkaz:
http://arxiv.org/abs/2311.00063
Autor:
Nghiem, Truong X., Drgoňa, Ján, Jones, Colin, Nagy, Zoltan, Schwan, Roland, Dey, Biswadip, Chakrabarty, Ankush, Di Cairano, Stefano, Paulson, Joel A., Carron, Andrea, Zeilinger, Melanie N., Cortez, Wenceslao Shaw, Vrabie, Draguna L.
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As oppos
Externí odkaz:
http://arxiv.org/abs/2306.13867
Autor:
Xu, Wenjie, Jones, Colin N, Svetozarevic, Bratislav, Laughman, Christopher R., Chakrabarty, Ankush
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated to be effective for improving closed-loop performance by automatically tuning controller gains o
Externí odkaz:
http://arxiv.org/abs/2301.12099
Deep neural state-space models (SSMs) provide a powerful tool for modeling dynamical systems solely using operational data. Typically, neural SSMs are trained using data collected from the actual system under consideration, despite the likely existen
Externí odkaz:
http://arxiv.org/abs/2211.07768
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach
Autor:
Chakrabarty, Ankush
Bayesian optimization (BO) has demonstrated potential for optimizing control performance in data-limited settings, especially for systems with unknown dynamics or unmodeled performance objectives. The BO algorithm efficiently trades-off exploration a
Externí odkaz:
http://arxiv.org/abs/2211.00077
Traditional control and monitoring of water quality in drinking water distribution networks (WDN) rely on mostly model- or toolbox-driven approaches, where the network topology and parameters are assumed to be known. In contrast, system identificatio
Externí odkaz:
http://arxiv.org/abs/2207.05983
Autor:
Xu, Wenjie, Jones, Colin N, Svetozarevic, Bratislav, Laughman, Christopher R., Chakrabarty, Ankush
We study the problem of performance optimization of closed-loop control systems with unmodeled dynamics. Bayesian optimization (BO) has been demonstrated effective for improving closed-loop performance by automatically tuning controller gains or refe
Externí odkaz:
http://arxiv.org/abs/2110.07479
Autor:
Xu, Wenjie, Jones, Colin N., Svetozarevic, Bratislav, Laughman, Christopher R., Chakrabarty, Ankush
Publikováno v:
In Journal of Process Control June 2024 138