離散時間制御バリア関数を用いた安全クリティカルなモデル予測制御
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
離散時間制御バリア関数を組み込んだMPCにより、制約下でも安全性を保証しつつ最適性能を達成する手法を提案し、障害物回避やカーレース例で検証した。
著者: Jun Zeng, Bike Zhang, Koushil Sreenath
分類: eess.SY, cs.RO, cs.SY
原文アブストラクト
The optimal performance of robotic systems is usually achieved near the limit of state and input bounds. Model predictive control (MPC) is a prevalent strategy to handle these operational constraints, however, safety still remains an open challenge for MPC as it needs to guarantee that the system stays within an invariant set. In order to obtain safe optimal performance in the context of set invariance, we present a safety-critical model predictive control strategy utilizing discrete-time control barrier functions (CBFs), which guarantees system safety and accomplishes optimal performance via model predictive control. We analyze the stability and the feasibility properties of our control design. We verify the properties of our method on a 2D double integrator model for obstacle avoidance. We also validate the algorithm numerically using a competitive car racing example, where the ego car is able to overtake other racing cars.
