動的環境におけるハイブリッドモデル予測制御のための継続学習付き一般化ベンダース分解
Generalized Benders Decomposition with Continual Learning for Hybrid Model Predictive Control in Dynamic Environment
ハイブリッドMPCの求解を高速化するため、一般化ベンダース分解に継続学習を組み合わせ、部分問題の双対空間から得た切断平面を蓄積してウォームスタートに利用する手法を提案し、カートポール系でGurobiより2〜3倍高速なことを示した。
著者: Xuan Lin
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
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.
