Applying reinforcement learning (RL) algorithms to control systems design remains a
challenging task due to the potential unsafe exploration and the low sample efficiency. In …

This paper presents a novel model-reference reinforcement learning algorithm for the
intelligent tracking control of uncertain autonomous surface vehicles with collision …

Guaranteed safety and performance under various circumstances remain technically critical
and practically challenging for the wide deployment of autonomous vehicles. Safety-critical …

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 …

Model Predictive Controller (MPC) is a capable technique for designing Path Tracking
Controller (PTC) of Autonomous Vehicles (AVs). The performance of MPC can be …

Planning safe trajectories under uncertain and dynamic conditions makes the autonomous
driving problem significantly complex. Current heuristic-based algorithms such as the slot …

To further improve learning efficiency and performance of reinforcement learning (RL), a
novel uncertainty-aware model-based RL method is proposed and validated in autonomous …

Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …

In this paper, subject to completely unknown system dynamics and input constraints, a
reinforcement learning-based finite-time trajectory tracking control (RLFTC) scheme is …

This paper proposes two different event-triggered nonlinear model predictive controls
(NMPC) for autonomous vehicle path tracking. The difference between the two event …