16 citations · 89 across the 50 of their papers we have counts for
18 papers · 1 filter
A Two-Stage, Model-Based Reinforcement Learning Approach for Active Flow Control of Bluff Body Wakes
Aayushman Sharma, Suman Chakravorty
This paper develops a data-driven, output-feedback approach to the infinite-horizon optimal control of high-dimensional nonlinear systems with unknown and unstable equilibria, usin…
State Forecasting in an Estimation Framework with Surrogate Sensor Modeling
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Ishan Paranjape +3
In recent years, computational power and data availability breakthroughs have revolutionized our ability to analyze complex physical systems through the inverse problem approach. D…
Sensor Fusion Methods for Gaussian Mixture Models
Ishan Paranjape, Islam Hussein, Jeremy Murray-Krezan +2
Consensus is a popular technique for distributed state estimation. This formulation allows networks of connected agents or sensors to exchange information about the distribution of…
A Reduced Order Iterative Linear Quadratic Regulator (ILQR) Technique for the Optimal Control of Nonlinear Partial Differential Equations
Aayushman Sharma, Suman Chakravorty
In this paper, we introduce a reduced order model-based reinforcement learning (MBRL) approach, utilizing the Iterative Linear Quadratic Regulator (ILQR) algorithm for the optimal…
Time-delayed Dynamic Mode Decomposition for families of periodic trajectories in Cislunar Space
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Indranil Nayak +2
In recent years, the development of the Lunar Gateway and Artemis missions has renewed interest in lunar exploration, including both manned and unmanned missions. This interest nec…
On the Predictive Capability of Dynamic Mode Decomposition for Nonlinear Periodic Systems with Focus on Orbital Mechanics
Sriram Narayanan, Mohamed Naveed Gul Mohamed, Indranil Nayak +2
This paper discusses the predictive capability of Dynamic Mode Decomposition (DMD) in the context of orbital mechanics. The focus is specifically on the Hankel variant of DMD which…
An Information-State Based Approach to Linear Time Varying System Identification and Control
Mohamed Naveed Gul Mohamed, Raman Goyal, Suman Chakravorty +1
This paper considers the problem of system identification for linear time varying systems. We propose a new system realization approach that uses an "information-state" as the stat…
Optimal Control of Material Micro-Structures
Aayushman Sharma, Zirui Mao, Haiying Yang +3
In this paper, we consider the optimal control of material micro-structures. Such material micro-structures are modeled by the so-called phase field model. We study the underlying…
On the Feedback Law in Stochastic Optimal Nonlinear Control
Mohamed Naveed Gul Mohamed, Suman Chakravorty, Raman Goyal +1
We consider the problem of nonlinear stochastic optimal control. This problem is thought to be fundamentally intractable owing to Bellman's "curse of dimensionality". We present a…
A Belief Space Perspective of RFS based Multi-Target Tracking and its Relationship to MHT
S. Chakravorty, W. R. Faber, Islam I. Hussein +1
In this paper, we establish a connection between Reid{'}s HOMHT and the modern Random Finite Set (RFS)/ Finite Set Statistics (FISST) based methods for Multi-Target Tracking. We st…
Decoupling stochastic optimal control problems for efficient solution: insights from experiments across a wide range of noise regimes
Mohamed Naveed Gul Mohamed, Suman Chakravorty, Dylan A. Shell
We consider the problem of robotic planning under uncertainty in this paper. This problem may be posed as a stochastic optimal control problem, a solution to which is fundamentally…
T-PFC: A Trajectory-Optimized Perturbation Feedback Control Approach
Karthikeya S Parunandi, Suman Chakravorty
Traditional stochastic optimal control methods that attempt to obtain an optimal feedback policy for nonlinear systems are computationally intractable. In this paper, we derive a d…
A Decoupled Data Based Approach to Stochastic Optimal Control Problems
Dan Yu, Mohammandhussen Rafieisakhaei, Suman Chakravorty
This paper studies the stochastic optimal control problem for systems with unknown dynamics. A novel decoupled data based control (D2C) approach is proposed, which solves the probl…
A Separation-Based Design to Data-Driven Control for Large-Scale Partially Observed Systems
Dan Yu, Mohammadhussein Rafieisakhaei, Suman Chakravorty
This paper studies the partially observed stochastic optimal control problem for systems with state dynamics governed by Partial Differential Equations (PDEs) that leads to an extr…
Stochastic Feedback Control of Systems with Unknown Nonlinear Dynamics
Dan Yu, Mohammadhussein Rafieisakhaei, Suman Chakravorty
This paper studies the stochastic optimal control problem for systems with unknown dynamics. First, an open-loop deterministic trajectory optimization problem is solved without kno…
Multi-Object Tracking with Multiple Birth, Death, and Spawn Scenarios Using A Randomized Hypothesis Generation Technique (R-FISST)
W. Faber, S. Chakravorty, Islam I. Hussein
In multi-object tracking one may encounter situations were at any time step the number of possible hypotheses is too large to generate exhaustively. These situations generally occu…
Particle Gaussian Mixture (PGM) Filters
Dilshad Raihan Akkam Veettil, Suman Chakravorty
Recursive estimation of nonlinear dynamical systems is an important problem that arises in several engineering applications. Consistent and accurate propagation of uncertainties is…
Decentralized State Estimation via a Hybrid of Consensus and Covariance intersection
Amirhossein Tamjidi, Suman Chakravorty, Dylan Shell
This paper presents a new recursive information consensus filter for decentralized dynamic-state estimation. No structure is assumed about the topology of the network and local est…