A Safe Control Architecture Based on Robust Model Predictive Control for Autonomous Driving

作     者：Nezami, Maryam Nguyen, Ngoc Thinh Männel, Georg Abbas, Hossam Seddik Schildbach, Georg 

作者机构：Institute for Electrical Engineering in Medicine University of Lübeck Lübeck Germany Institute for Robotics and Cognitive Systems University of Lübeck Lübeck Germany Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering Fraunhofer IMTE Lübeck Germany 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2022年

核心收录：

主　　题：Controllers 

摘      要：This paper proposes a Robust Safe Control Architecture (RSCA) for safe-decision making. The system to be controlled is a vehicle in the presence of bounded disturbances. The RSCA consists of two parts: a Supervisor MPC and a Controller MPC. Both the Supervisor and the Controller are tube MPCs (TMPCs). The Supervisor MPC provides a safety certificate for an operating controller and a backup control input in every step. After an unsafe action by the operating controller is predicted, the Controller MPC takes over the system. In this paper, a method for the computation of a terminal set is proposed, which is robust against changes in the road curvature and forces the vehicle to reach a safe reference. Moreover, two important proofs are provided in this paper. First, it is shown that the backup control input is safe to be applied to the system to lead the vehicle to a safe state. Next, the recursive feasibility of the RSCA is proven. By simulating some obstacle avoidance scenarios, the effectiveness of the proposed RSCA is confirmed. Copyright © 2022, The Authors. All rights reserved.