14 citations · 42 across the 22 of their papers we have counts for
7 papers · 1 filter
Set-based Optimal, Robust, and Resilient Control with Applications to Autonomous Precision Landing
Abhinav G. Kamath, Abraham P. Vinod, Purnanand Elango +2
We present a real-time-capable set-based framework for closed-loop predictive control of autonomous systems using tools from computational geometry, dynamic programming, and convex…
Multi-Vehicle Guidance for Formation Flight on Libration Point Orbits
Yuri Shimane, Purnanand Elango, Avishai Weiss
The multiple spacecraft guidance problem for proximity flight in libration point orbit is considered. A nonlinear optimal control problem with continuous-time path constraints enfo…
Onboard Dual Quaternion Guidance for Rocket Landing
Abhinav G. Kamath, Javier A. Doll, Purnanand Elango +8
The dual quaternion guidance (DQG) algorithm was selected as the candidate 6-DoF powered-descent guidance algorithm for NASA's Safe and Precise Landing -- Integrated Capabilities E…
Successive Convexification for Passively-Safe Spacecraft Rendezvous on Near Rectilinear Halo Orbit
Purnanand Elango, Abraham P. Vinod, Kenji Kitamura +3
We present an optimization-based approach for fuel-efficient spacecraft rendezvous to the Gateway, a space station that will be deployed on a near rectilinear halo orbit (NRHO) aro…
Deferred-Decision Trajectory Optimization
Purnanand Elango, Selahattin Burak Sarsilmaz, Behcet Acikmese
We present DDTO--deferred-decision trajectory optimization--a framework for trajectory generation with resilience to unmodeled uncertainties and contingencies. The key idea is to e…
Impulsive Relative Motion Control with Continuous-Time Constraint Satisfaction for Cislunar Space Missions
Fabio Spada, Purnanand Elango, Behçet Açıkmeşe
Recent investments in cislunar applications open new frontiers for space missions within highly nonlinear dynamical regimes. In this paper, we propose a method based on Sequential…
Optimal Preconditioning for Online Quadratic Cone Programming
Abhinav G. Kamath, Purnanand Elango, Behçet Açıkmeşe
First-order conic optimization solvers are sensitive to problem conditioning and typically perform poorly in the face of ill-conditioned problem data. To mitigate this, we propose…