9 citations · 14 across the 17 of their papers we have counts for
18 papers
Robust Peak-cost Constrained Reinforcement Learning
Shilpa Mukhopadhyay, Sourav Ganguly, Santosh Mohan Rajkumar +3
We study robust peak-cost constrained reinforcement learning (\ours), where the objective is to maximize expected reward while controlling the maximum cost encountered along a traj…
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…
Feature-Based Echo-State Networks: A Step Towards Interpretability and Minimalism in Reservoir Computer
Debdipta Goswami
This paper proposes a novel and interpretable recurrent neural-network structure using the echo-state network (ESN) paradigm for time-series prediction. While the traditional ESNs…
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…
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…
Optimal Control with Learning on the Fly: System with Unknown Drift
Daniel Gurevich, Debdipta Goswami, Charles L. Fefferman +1
This paper derives an optimal control strategy for a simple stochastic dynamical system with constant drift and an additive control input. Motivated by the example of a physical sy…
Feature-rich bifurcations in a simple electronic circuit
Debdipta Goswami, Subhankar Ray
A simple electronic circuit with a voltage controlled current source is investigated. The circuit exhibits rich dynamics upon varying the circuit elements such as L,C and R, and th…
Investigation of A Collective Decision Making System of Different Neighbourhood-Size Based on Hyper-Geometric Distribution
Debdipta Goswami, Heiko Hamann
The study of collective decision making system has become the central part of the Swarm- Intelligence Related research in recent years. The most challenging task of modelling a col…
Multi-Agent Shape Formation and Tracking Inspired from a Social Foraging Dynamics
Debdipta Goswami, Chiranjib Saha, Kunal Pal +1
Principle of Swarm Intelligence has recently found widespread application in formation control and automated tracking by the automated multi-agent system. This article proposes an…