论文标题
安全 - 关键模型的预测控制具有离散时间控制屏障功能
Safety-Critical Model Predictive Control with Discrete-Time Control Barrier Function
论文作者
论文摘要
机器人系统的最佳性能通常是在状态和输入范围的限制附近实现的。模型预测控制（MPC）是处理这些操作约束的普遍策略，但是，安全仍然是MPC的开放挑战，因为它需要确保该系统停留在不变的集合中。为了在设定不变性的背景下获得安全的最佳性能，我们提出了利用离散时间控制屏障功能（CBF）的安全至关重要的预测控制策略，该策略可确保系统安全性并通过模型预测控制来实现最佳性能。我们分析了控制设计的稳定性和可行性。我们在2D双积分器模型上验证方法的属性，以避免障碍物。我们还使用竞争性的赛车示例来验证算法，在该竞赛中，自我汽车能够超过其他赛车。
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.
