Hierarchical Motion Planning and Tracking for Autonomous Vehicles Using Global Heuristic Based Potential Field and Reinforcement Learning Based Predictive Control

The autonomous vehicle is widely applied in various ground operations, in which motion planning and tracking control are becoming the key technologies to achieve autonomous driving. In order to further improve the performance of motion planning and tracking control, an efficient hierarchical framework containing motion planning and tracking...

A new reinforcement learning-based variable speed limit control approach to improve traffic efficiency against freeway jam waves

Conventional reinforcement learning (RL) models of variable speed limit (VSL) control systems (and traffic control systems in general) cannot be trained in real traffic process because new control actions are usually explored randomly, which may result in high costs (delays) due to exploration and learning. For this reason, existing RL-based...

Secure Control for Cyber-Physical Systems under Malicious Attacks

This article investigates the secure control problem for cyber-physical systems when the malicious data are injected into the cyber realm, which directly connects to the actuators. Based on moving target defense (MTD) and reinforcement learning, we propose a novel proactive and reactive defense control scheme. First, the system (A,B) is...

Learning Tracking Control for Cyber-Physical Systems

This paper investigates the problem of optimal tracking control for cyber-physical systems (CPS) when the cyber realm is attacked by denial-of-service (DoS) attacks which can prevent the control signal transmitting to the actuator. Attention is focused on how to design the optimal tracking control scheme without using the system dynamics and...