论文标题
基于模型预测控制主管的安全控制架构，用于自动驾驶
A Safe Control Architecture Based on a Model Predictive Control Supervisor for Autonomous Driving
论文作者
论文摘要
本文介绍了一种新颖的安全控制体系结构（SCA），用于控制重要类别的系统：安全至关重要的系统。确保控制决策的安全一直是自动控制的挑战。拟议的SCA旨在通过使用模型预测控制器（MPC）来应对这一挑战，该模型充当操作控制器的主管，因为MPC不断检查操作控制器产生的控制输入的安全性并介入控制输入是否会导致可预见的将来的无风度危险状况。然后可以激活适当的备份方案，例如，降级控制机制，将系统转移到安全状态或发给人类主管的警告信号。为了获得概念证明，提出的SCA应用于自主驾驶场景，在该场景中进行了说明和比较。 SCA的主要挑战在于MPC预测模型与实际系统之间的不匹配，为此探讨了可能的补救措施。
This paper presents a novel, safe control architecture (SCA) for controlling an important class of systems: safety-critical systems. Ensuring the safety of control decisions has always been a challenge in automatic control. The proposed SCA aims to address this challenge by using a Model Predictive Controller (MPC) that acts as a supervisor for the operating controller, in the sense that the MPC constantly checks the safety of the control inputs generated by the operating controller and intervenes if the control input is predicted to lead to a hazardous situation in the foreseeable future invariably. Then an appropriate backup scheme can be activated, e.g., a degraded control mechanism, the transfer of the system to a safe state, or a warning signal issued to a human supervisor. For a proof of concept, the proposed SCA is applied to an autonomous driving scenario, where it is illustrated and compared in different obstacle avoidance scenarios. A major challenge of the SCA lies in the mismatch between the MPC prediction model and the real system, for which possible remedies are explored.
