ロボティクス向け微分可能予測制御：データ駆動型予測安全フィルタアプローチ
Differentiable Predictive Control for Robotics: A Data-Driven Predictive Safety Filter Approach
微分可能予測制御（DPC）の安全性保証の欠如を解決するため、相対次数に基づくシステム分解と、訓練データから安全集合を生成するイベントトリガ型予測安全フィルタを提案し、クアッドコプタでMPC並みの性能と最大3桁の計算時間削減を実証した。
著者: John Viljoen, Wenceslao Shaw Cortez, Jan Drgona, Sebastian East, Masayoshi Tomizuka, Draguna Vrabie
分類: cs.RO
原文アブストラクト
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.
