动态环境中基于持续学习的广义Benders分解用于混合模型预测控制
Generalized Benders Decomposition with Continual Learning for Hybrid Model Predictive Control in Dynamic Environment
AI总结：
针对动态环境中混合模型预测控制求解慢的问题，提出基于广义Benders分解与持续学习的求解器，通过累积切割平面提供热启动，在倒立摆实验中比Gurobi快2-3倍。
摘要：
混合模型预测控制（MPC）同时包含连续变量和离散变量，广泛应用于机器人控制任务，尤其是涉及与环境接触的任务。由于组合复杂性，混合MPC的求解速度可能不足以满足实时应用的需求。本文提出了一种基于广义Benders分解（GBD）并融合持续学习的混合MPC求解器。该算法从子问题的不变对偶空间中累积切割平面。经过短暂的冷启动阶段后，累积的切割为新的问题实例提供热启动，以提高求解速度。尽管控制面对的环境随机变化且未预先准备，求解速度仍得以保持。我们在控制带有随机移动软接触壁的倒立摆系统上验证了我们的求解器，结果表明其求解速度比现成的求解器Gurobi快2-3倍。
英文摘要：
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.
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