用神经网络加速安全控制，无需重训即可实时避障。
Safety Meets Speed: Accelerated Neural MPC with Safety Guarantees and No Retraining
- 用神经价值函数降低在线计算量，结合屏障函数保证安全。
- 硬件在环测试显示速度提升200倍，控制误差低于5%。
- 参数变化时自动调整，适合嵌入式设备部署。
尽管模型预测控制（MPC）通过约束实现安全，但其实时执行可能超出嵌入式计算预算。我们提出一种集成屏障函数的自适应神经模型预测控制（BAN-MPC）框架，将神经网络的快速计算与MPC的约束处理能力相结合。为确保严格安全，采用控制屏障函数（CBFs）替代传统欧氏距离进行避障。将离线学习的神经价值函数引入短时域MPC的目标函数，显著降低在线计算复杂度。此外，使用第二个神经网络学习价值函数对系统参数的敏感性，并在参数变化时自适应调整价值函数，无需重训且减少离线计算成本。硬件在环（HIL）实验在Jetson Nano上表明，BAN-MPC求解速度比传统MPC快200倍，在参数变化不超过15%时仍能实现低于5%的控制误差，支持无碰撞导航，是高效的嵌入式MPC替代方案。
原文摘要 · Abstract (English)
While Model Predictive Control (MPC) enforces safety via constraints, its real-time execution can exceed embedded compute budgets. We propose a Barrier-integrated Adaptive Neural Model Predictive Control (BAN-MPC) framework that synergizes neural networks' fast computation with MPC's constraint-handling capability. To ensure strict safety, we replace traditional Euclidean distance with Control Barrier Functions (CBFs) for collision avoidance. We integrate an offline-learned neural value function into the optimization objective of a Short-horizon MPC, substantially reducing online computational complexity. Additionally, we use a second neural network to learn the sensitivity of the value function to system parameters, and adaptively adjust the neural value function based on this neural sensitivity when model parameters change, eliminating the need for retraining and reducing offline computation costs. The hardware in-the-loop (HIL) experiments on Jetson Nano show that BAN-MPC solves 200 times faster than traditional MPC, enabling collision-free navigation with control error below 5\% under model parameter variations within 15\%, making it an effective embedded MPC alternative.
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