基于基于模型的强化学习的欠驱动飞行机器人非完整偏航控制
Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
摘要：
非完整控制是控制具有路径依赖状态的非线性系统的一种有效方法。本文研究了一种欠驱动飞行的微型飞行器--电离层飞行器，它需要在偏航方向上进行非完整控制来完成姿态控制。部署的分析控制律涉及大量的工程设计，是敏感的系统模型的不准确性。在装配和系统动力学的特定假设下，我们推导出了电离飞行器偏航控制的李括号。与解析控制律所需的大量工程工作相比，我们在模拟飞行任务中实现了数据驱动的基于模型的强化学习偏航控制器。我们证明了一个简单的基于模型的强化学习框架可以在几分钟的飞行数据中匹配导出的李括号控制（在偏航率和选择的动作中），而无需预定义的动力学函数。本文表明，基于学习的方法是有用的工具，以前只能通过基于专家的设计寻址的非线性控制律的合成。
英文摘要：
Nonholonomic control is a candidate to control nonlinear systems with path-dependant states. We investigate an underactuated flying micro-aerial-vehicle, the ionocraft, that requires nonholonomic control in the yaw-direction for complete attitude control. Deploying an analytical control law involves substantial engineering design and is sensitive to inaccuracy in the system model. With specific assumptions on assembly and system dynamics, we derive a Lie bracket for yaw control of the ionocraft. As a comparison to the significant engineering effort required for an analytic control law, we implement a data-driven model-based reinforcement learning yaw controller in a simulated flight task. We demonstrate that a simple model-based reinforcement learning framework can match the derived Lie bracket control (in yaw rate and chosen actions) in a few minutes of flight data, without a pre-defined dynamics function. This paper shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
