用相对阶分解和事件触发安全滤波，让机器人控制更快更安全
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach
- 基于相对阶分解的系统拆分，提升DPC稳定性
- 训练数据生成安全集，实现跨场景安全控制
- 计算量降低千倍，适合资源受限机器人
模型预测控制（MPC）在约束环境下能生成安全控制策略，但计算开销大，尤其对高采样率、算力有限的机器人不适用。不同可微预测控制（DPC）通过离线训练神经网络近似参数化MPC，虽显著降低在线计算成本，但失去安全保证且在病态条件下表现差。本文提出基于相对阶的系统分解方法以克服此问题，并设计一种基于DPC训练数据的安全集生成技术，以及一种事件触发的预测安全滤波器，促进系统收敛至安全集。在四旋翼无人机上的实验证明，该方法的控制性能与最先进的MPC相当，计算时间减少高达三个数量级，且在未训练场景下仍满足安全要求。
原文摘要 · Abstract (English)
Model Predictive Control (MPC) is effective at generating safe control strategies in constrained scenarios, at the cost of computational complexity. This is especially the case in robots that require high sampling rates and have limited computing resources. Differentiable Predictive Control (DPC) trains offline a neural network approximation of the parametric MPC problem leading to computationally efficient online control laws at the cost of losing safety guarantees. DPC requires a differentiable model, and performs poorly when poorly conditioned. In this paper we propose a system decomposition technique based on relative degree to overcome this. We also develop a novel safe set generation technique based on the DPC training dataset and a novel event-triggered predictive safety filter which promotes convergence towards the safe set. Our empirical results on a quadcopter demonstrate that the DPC control laws have comparable performance to the state-of-the-art MPC whilst having up to three orders of magnitude reduction in computation time and satisfy safety requirements in a scenario that DPC was not trained on.
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