9 papers
Robust Peak-cost Constrained Reinforcement Learning
Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar +3
We study robust peak-cost constrained reinforcement learning (RP-CRL), where the objective is to maximize expected reward while controlling the maximum cost encountered along a tra…
Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
Ananda Chakrabarti, Haitham H. Saleh, Indranil Nayak +3
We present parameter-interpolated dynamic mode decomposition (piDMD), a parametric reduced-order modeling framework that embeds known parameter-affine structure directly into the D…
On Data-Driven Koopman Representations of Nonlinear Delay Differential Equations
Santosh Mohan Rajkumar, Dibyasri Barman, Kumar Vikram Singh +1
This work establishes a rigorous bridge between infinite-dimensional delay dynamics and finite-dimensional Koopman learning, with explicit and interpretable error guarantees. While…
Real-Time Linear MPC for Quadrotors on SE(3): An Analytical Koopman-based Realization
Santosh M. Rajkumar, Chengyu Yang, Yuliang Gu +3
This letter presents an analytical linear parameter-varying (LPV) representation of quadrotor dynamics utilizing Koopman theory, facilitating computationally efficient linear model…
Data to Certificate: Guaranteed Cost Control with Quantization-Aware System Identification
Shahab Ataei, Dipankar Maity, Debdipta Goswami
Cloud-assisted system identification and control have emerged as practical solutions for low-power, resource-constrained control systems such as micro-UAVs. In a typical cloud-assi…
Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems
Indranil Nayak, Ananda Chakrabarty, Mrinal Kumar +2
Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) har…