论文标题
用于自动表面车辆的无碰撞跟踪控制的模型引用增强学习
Model-Reference Reinforcement Learning for Collision-Free Tracking Control of Autonomous Surface Vehicles
论文作者
论文摘要
本文介绍了一种新型的模型引用增强学习算法，用于避免碰撞的智能跟踪控制不确定的自动表面车辆的控制。拟议的控制算法将传统的控制方法与增强学习结合在一起，以增强控制精度和智力。在拟议的控制设计中，考虑使用常规控制方法的基线跟踪控制器的设计标称系统。名义系统还定义了在无障碍环境中不确定的自动表面车辆的所需行为。多亏了强化学习，整体跟踪控制器能够在有障碍的环境中同时弥补模型的不确定性并避免碰撞。与传统的深入学习方法相比，我们提出的基于学习的控制可以提供稳定性保证和更好的样本效率。我们使用自动表面车辆的示例来证明新算法的性能。
This paper presents a novel model-reference reinforcement learning algorithm for the intelligent tracking control of uncertain autonomous surface vehicles with collision avoidance. The proposed control algorithm combines a conventional control method with reinforcement learning to enhance control accuracy and intelligence. In the proposed control design, a nominal system is considered for the design of a baseline tracking controller using a conventional control approach. The nominal system also defines the desired behaviour of uncertain autonomous surface vehicles in an obstacle-free environment. Thanks to reinforcement learning, the overall tracking controller is capable of compensating for model uncertainties and achieving collision avoidance at the same time in environments with obstacles. In comparison to traditional deep reinforcement learning methods, our proposed learning-based control can provide stability guarantees and better sample efficiency. We demonstrate the performance of the new algorithm using an example of autonomous surface vehicles.
