Generalized Benders Decomposition with Continual Learning for Hybrid Model Predictive Control in Dynamic Environment
Abstract
Hybrid model predictive control (MPC) with both continuous and discrete variables is widely applicable to robotic control tasks, especially those involving contact with the environment. Due to the combinatorial complexity, the solving speed of hybrid MPC can be insufficient for real-time applications. In this paper, we proposed a hybrid MPC solver based on Generalized Benders Decomposition (GBD) with continual learning. The algorithm accumulates cutting planes from the invariant dual space of the subproblems. After a short cold-start phase, the accumulated cuts provide warm-starts for the new problem instances to increase the solving speed. Despite the randomly changing environment that the control is unprepared for, the solving speed maintains. We verified our solver on controlling a cart-pole system with randomly moving soft contact walls and show that the solving speed is 2-3 times faster than the off-the-shelf solver Gurobi.
Cite
@article{arxiv.2310.03344,
title = {Generalized Benders Decomposition with Continual Learning for Hybrid Model Predictive Control in Dynamic Environment},
author = {Xuan Lin},
journal= {arXiv preprint arXiv:2310.03344},
year = {2023}
}
Comments
A correction of the author name in the metadata