用神经控制屏障函数提升采样型MPC的长期安全性，支持黑箱系统实时控制。
Safe Beyond the Horizon: Efficient Sampling-based MPC with Neural Control Barrier Functions
- 学习离散时间控制屏障函数，融入变分推断MPC框架
- 新采样策略降低控制估计方差，实现CPU上实时规划
- 在仿真与真实硬件中显著提升安全性能，对代价函数不敏感
实际应用中，模型预测控制（MPC）常难以保证预测时域之外的安全性。尽管理论表明通过终端集约束或足够长的预测时域可确保安全，但这些方法难以实施，尤其在一般非线性系统中极少被使用。为此，本文在保证递归可行性、计算可处理性及对“黑箱”动态系统的适用性之间权衡，提出学习近似离散时间控制屏障函数，并将其融入变分推断MPC（VIMPC）这一采样型MPC范式。为处理由此引入的状态约束，进一步设计一种新型采样策略，显著降低最优控制估计的方差，提升样本效率，实现基于CPU的实时规划。所提出的神经屏障-VIMPC（NS-VIMPC）控制器在多种场景下相比现有采样型MPC控制器展现出显著的安全性提升，即使在代价函数设计不佳的情况下依然有效。方法在仿真与真实硬件实验中均得到验证。
原文摘要 · Abstract (English)
A common problem when using model predictive control (MPC) in practice is the satisfaction of safety specifications beyond the prediction horizon. While theoretical works have shown that safety can be guaranteed by enforcing a suitable terminal set constraint or a sufficiently long prediction horizon, these techniques are difficult to apply and thus are rarely used by practitioners, especially in the case of general nonlinear dynamics. To solve this problem, we impose a tradeoff between exact recursive feasibility, computational tractability, and applicability to ``black-box'' dynamics by learning an approximate discrete-time control barrier function and incorporating it into a variational inference MPC (VIMPC), a sampling-based MPC paradigm. To handle the resulting state constraints, we further propose a new sampling strategy that greatly reduces the variance of the estimated optimal control, improving the sample efficiency, and enabling real-time planning on a CPU. The resulting Neural Shield-VIMPC (NS-VIMPC) controller yields substantial safety improvements compared to existing sampling-based MPC controllers, even under badly designed cost functions. We validate our approach in both simulation and real-world hardware experiments. Project website: https://mit-realm.github.io/ns-vimpc/.
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