20 citations · 39 across the 14 of their papers we have counts for
15 papers
On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
Vedang M. Deshpande, Raktim Bhattacharya
Polynomial chaos expansions provide surrogate models for stochastic systems, with coefficients typically derived using Galerkin projection, stochastic collocation, or least squares…
Motion Planning for Autonomous Vehicles: When Model Predictive Control Meets Ensemble Kalman Smoothing
Iman Askari, Yebin Wang, Vedeng M. Deshpande +1
Safe and efficient motion planning is of fundamental importance for autonomous vehicles. This paper investigates motion planning based on nonlinear model predictive control (NMPC)…
Meta-Learning for Physically-Constrained Neural System Identification
Ankush Chakrabarty, Gordon Wichern, Vedang M. Deshpande +3
We present a gradient-based meta-learning framework for rapid adaptation of neural state-space models (NSSMs) for black-box system identification. When applicable, we also incorpor…
Optimal Control with Sparse Sensing and Actuation
Vedang M. Deshpande, Raktim Bhattacharya
In this paper, we present novel convex optimization formulations for designing full-state and output-feedback controllers with sparse actuation that achieve user-specified $\mathca…
Sensor Placement with Optimal Precision for Temperature Estimation of Battery Systems
Vedang M. Deshpande, Raktim Bhattacharya, Kamesh Subbarao
The temperature distribution in the battery significantly impacts the short-term and long-term performance of battery systems. Therefore, efficient, safe, and reliable battery syst…
Sensor Selection and Optimal Precision in Estimation Framework: Theory and Algorithms
Vedang M. Deshpande, Raktim Bhattacharya
We consider the problem of sensor selection for designing observer and filter for continuous linear time invariant systems such that the sensor precisions are minimized, and the es…
Sparse Sensing Architectures with Optimal Precision for Tracking Multi-agent Systems in Sensing-denied Environments
Vedang M. Deshpande, Raktim Bhattacharya
In this paper the tracking problem of multi-agent systems, in a particular scenario where a segment of agents entering a sensing-denied environment or behaving as non-cooperative t…
Sparse Sensing and Optimal Precision: Robust Optimal Observer Design with Model Uncertainty
Vedang M. Deshpande, Raktim Bhattacharya
We present a framework which incorporates three aspects of the estimation problem, namely, sparse sensor configuration, optimal precision, and robustness in the presence of model u…
Data-driven Solution of Stochastic Differential Equations Using Maximum Entropy Basis Functions
Vedang M. Deshpande, Raktim Bhattacharya
In this paper we present a data-driven approach for uncertainty propagation. In particular, we consider stochastic differential equations with parametric uncertainty. Solution of t…
H2 Optimized PID Control of Quad-Copter Platform with Wind Disturbance
Sunsoo Kim, Vedang Deshpande, Raktim Bhattacharya
Proportional-Integral-Derivative (PID) scheme is the most commonly used algorithm for designing the controllers for unmanned aerial vehicles (UAVs). However, tuning PID gains is a…
Kalman Filtering with Probabilistic Uncertainty in System Parameters
Sunsoo Kim, Vedang M. Deshpande, Raktim Bhattacharya
In this paper, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete time systems,…
Sparse Sensing and Optimal Precision: An Integrated Framework for Optimal Observer Design
Vedang M. Deshpande, Raktim Bhattacharya
In this paper, we simultaneously determine the optimal sensor precision and the observer gain, which achieves the specified accuracy in the state estimates. Along with the unknown…
A unified framework to generate optimized compact finite difference schemes
Vedang M. Deshpande, Raktim Bhattacharya, Diego A. Donzis
A unified framework to derive optimized compact schemes for a uniform grid is presented. The optimal scheme coefficients are determined analytically by solving an optimization prob…
Surrogate Modeling of Dynamics From Sparse Data Using Maximum Entropy Basis Functions
Vedang M. Deshpande, Raktim Bhattacharya
In this paper we present a data driven approach for approximating dynamical systems. A dynamics is approximated using basis functions, which are derived from maximization of the in…
On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
Vedang M. Deshpande, Raktim Bhattacharya
Polynomial chaos expansion is a popular way to develop surrogate models for stochastic systems with arbitrary random variables. Standard techniques such as Galerkin projection, sto…