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20202026
most citedSuccessive Convexification for Trajectory Optimization with Continuous-Time Constraint Satisfaction

14 citations · 16 across the 14 of their papers we have counts for

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15 papers · 1 filter

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC2025

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…

math.OC20251 cited

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…