9 citations · 14 across the 17 of their papers we have counts for
11 papers · 1 filter
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…
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…
QSID-MPC: Model Predictive Control with System Identification from Quantized Data
Shahab Ataei, Dipankar Maity, Debdipta Goswami
Least-square system identification is widely used for data-driven model-predictive control (MPC) of unknown or partially known systems. This letter investigates how the system iden…
Temporally-Consistent Bilinearly Recurrent Autoencoders for Control Systems
Ananda Chakrabarti, Indranil Nayak, Debdipta Goswami
This paper introduces the temporally-consistent bilinearly recurrent autoencoder (tcBLRAN), a Koopman operator based neural network architecture for modeling a control-affine nonli…
Koopman Meets Limited Bandwidth: Effect of Quantization on Data-Driven Linear Prediction and Control of Nonlinear Systems
Shahab Ataei, Dipankar Maity, Debdipta Goswami
Koopman-based lifted linear identification have been widely used for data-driven prediction and model predictive control (MPC) of nonlinear systems. It has found applications in fl…
On the Effect of Quantization on Extended Dynamic Mode Decomposition
Dipankar Maity, Debdipta Goswami
Extended Dynamic Mode Decomposition (EDMD) is a widely used data-driven algorithm for estimating the Koopman Operator. EDMD extends Dynamic Mode Decomposition (DMD) by lifting the…
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…
On the Effect of Quantization on Dynamic Mode Decomposition
Dipankar Maity, Debdipta Goswami, Sriram Narayanan
Dynamic Mode Decomposition (DMD) is a widely used data-driven algorithm for estimating the Koopman Operator.This paper investigates how the estimation process is affected when the…
Sequential Learning from Noisy Data: Data-Assimilation Meets Echo-State Network
Debdipta Goswami
This paper explores the problem of training a recurrent neural network from noisy data. While neural network based dynamic predictors perform well with noise-free training data, pr…
Delay Embedded Echo-State Network: A Predictor for Partially Observed Systems
Debdipta Goswami
This paper considers the problem of data-driven prediction of partially observed systems using a recurrent neural network. While neural network based dynamic predictors perform wel…