基于Lyapunov神经网络的鲁棒模型强化学习USV系统

Lei Xia, C. Shao, Huiyun Li, Yunduan Cui
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摘要

本文探讨了神经网络逼近李雅普诺夫函数在无人水面车辆控制问题中的潜力。提出了一种新的基于模型的强化学习方法——Lyapunov滤波概率模型预测控制(LFPMPC),在Lyapunov神经网络的指导下探索USV控制策略。开发了基于LFPMPC的无人潜航器系统,并利用实船数据驱动的无人潜航器模拟器对其在各种环境干扰下的位置保持任务进行了评估。将Lyapunov神经网络的输出作为代价函数中系统鲁棒性的一个度量,与没有Lyapunov神经网络的基线方法相比,所提出的方法不仅在对干扰的控制稳定性方面具有显著优势,而且在系统模型的学习能力方面也具有显著优势。
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Robust Model-based Reinforcement Learning USV System Guided by Lyapunov Neural Networks
This paper explores the potential of Lyapunov function approximated by neural networks in unmanned surface vehicles (USV) control problem. A novel model-based reinforcement learning method, Lyapunov filtered probabilistic model predictive control (LFPMPC) is proposed to explore the USV control policy under the guidance of Lyapunov neural networks. The USV system based on LFPMPC is developed and evaluated by a USV simulator driven by real boat data in position-keeping task with various environmental disturbances. Taking the output of Lyapunov neural networks as one metric of the system robustness in the cost function, the proposed approach demonstrated significant superiorities in not only control stability against disturbances but also learning capabilities of the system model compared with the baseline approach without Lyapunov neural networks.
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