14 citations · 19 across the 9 of their papers we have counts for
10 papers · 1 filter
Newton-PIPG: A Fast Hybrid Algorithm for Quadratic Programs in Optimal Control
Dayou Luo, Yue Yu, Maryam Fazel +1
We propose Newton-PIPG, an efficient method for solving quadratic programming (QP) problems arising in optimal control, subject to additional set constraints. Newton-PIPG integrate…
Auto-tuned Primal-dual Successive Convexification for Hypersonic Reentry Guidance
Skye Mceowen, Daniel J. Calderone, Aman Tiwary +4
This paper presents auto-tuned primal-dual successive convexification (Auto-SCvx), an algorithm designed to reliably achieve dynamically-feasible trajectory solutions for constrain…
Fast Monte Carlo Analysis for 6-DoF Powered-Descent Guidance via GPU-Accelerated Sequential Convex Programming
Govind M. Chari, Abhinav G. Kamath, Purnanand Elango +1
We introduce a GPU-accelerated Monte Carlo framework for nonconvex, free-final-time trajectory optimization problems. This framework makes use of the prox-linear method, which belo…
Successive Convexification for Nonlinear Model Predictive Control with Continuous-Time Constraint Satisfaction
Samet Uzun, Purnanand Elango, Abhinav G. Kamath +2
We propose a nonlinear model predictive control (NMPC) framework based on a direct optimal control method that ensures continuous-time constraint satisfaction and accurate evaluati…
Successive Convexification for Trajectory Optimization with Continuous-Time Constraint Satisfaction
Purnanand Elango, Dayou Luo, Abhinav G. Kamath +3
We present successive convexification, a real-time-capable solution method for nonconvex trajectory optimization, with continuous-time constraint satisfaction and guaranteed conver…
Remarks on "Successive Convexification: A Superlinearly Convergent Algorithm for Non-convex Optimal Control Problems"
Dayou Luo, Purnanand Elango, Behcet Acikmese
The purpose of this note is to highlight and address inaccuracies in the convergence guarantees of SCvx, a nonconvex trajectory optimization algorithm proposed by Mao et al. (arXiv…