Estimation and control with guarantees under uncertainty, from theory to flight.
We develop the theory of estimation and control for uncertain dynamical systems, and carry it to aerospace vehicles: spacecraft in cislunar space, aircraft and UAVs, and teams of cooperating agents.
People
Raktim BhattacharyaProfessor
Tanay KumarPh.D. Student
Jae-son RiveraM.Eng. Student
Research
Uncertainty Quantification
Propagating uncertainty through nonlinear dynamics.
Estimation & Data Assimilation
State estimation for nonlinear, non-Gaussian systems.
Robust Control
Control and filtering with guaranteed performance.
Optimal Control & Trajectory Generation
Trajectories and controllers that remain optimal under uncertainty.
Sensor & Actuator Architecture
The sparsest, least precise sensing that still meets performance.
Scientific Machine Learning
Machine learning for control design and analysis.
Control of Computation
Control theory for numerical and parallel algorithms.
Optimal Transport
Wasserstein geometry for filtering and robustness.
Verification & Model Validation
Certificates for stability, invariance, and model fidelity.
Recent publications
Improving D-Optimal Sensor Placement for Bearing-Only Localization via Maximum-Entropy Reweighting
Sensor Design for Accuracy-Bounded Estimation via Maximum-Entropy Likelihood Synthesis
Robust Cislunar Navigation via LFT-Based Filtering with Bearing-Only Measurements
Robust Attitude Control of Nonlinear Multi-Rotor Dynamics with LFT Models and Performance
On Improved Statistical Accuracy of Low-Order Polynomial Chaos Approximations
Spectral Koopman Approach for Reconstructing State-space Geometry of Cislunar Restricted 3-Body Problem
Sparse Actuation for LPV Systems with Full-State Feedback in Framework