A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems
Abstract
In this paper, we propose a Feasible Sequential Linear Programming (FSLP) algorithm applied to time-optimal control problems (TOCP) obtained through direct multiple shooting discretization. This method is motivated by TOCP with nonlinear constraints which arise in motion planning of mechatronic systems. The algorithm applies a trust-region globalization strategy ensuring global convergence. For fully determined problems our algorithm provides locally quadratic convergence. Moreover, the algorithm keeps all iterates feasible enabling early termination at suboptimal, feasible solutions. This additional feasibility is achieved by an efficient iterative strategy using evaluations of constraints, i.e., zero-order information. Convergence of the feasibility iterations can be enforced by reduction of the trust-region radius. These feasibility iterations maintain feasibility for general Nonlinear Programs (NLP). Therefore, the algorithm is applicable to general NLPs. We demonstrate our algorithm's efficiency and the feasibility update strategy on a TOCP of an overhead crane motion planning simulation case.
Cite
@article{arxiv.2205.00754,
title = {A Feasible Sequential Linear Programming Algorithm with Application to Time-Optimal Path Planning Problems},
author = {David Kiessling and Andrea Zanelli and Armin Nurkanović and Joris Gillis and Moritz Diehl and Melanie Zeilinger and Goele Pipeleers and Jan Swevers},
journal= {arXiv preprint arXiv:2205.00754},
year = {2024}
}
Comments
Accepted for publication at the IEEE Conference on Decision and Control 2022 (CDC 22)