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Nonholonomic Yaw Control of an Underactuated Flying Robot with Model-based Reinforcement Learning
IEEE Robotics and Automation Letters ( IF 5.2 ) Pub Date : 2021-04-01 , DOI: 10.1109/lra.2020.3045930
Nathan O. Lambert , Craig B. Schindler , Daniel S. Drew , Kristofer S. J. Pister

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 letter shows that learning-based approaches are useful as a tool for synthesis of nonlinear control laws previously only addressable through expert-based design.

中文翻译:

基于模型强化学习的欠驱动飞行机器人非完整偏航控制

非完整控制是控制具有路径依赖状态的非线性系统的候选者。我们研究了一种欠驱动飞行微型飞行器,即离子飞行器,它需要在偏航方向上进行非完整控制以实现完全的姿态控制。部署分析控制律涉及大量工程设计,并且对系统模型的不准确性很敏感。通过对装配和系统动力学的特定假设,我们推导出用于离子飞行器偏航控制的李括号。与分析控制律所需的重大工程工作相比,我们在模拟飞行任务中实施了基于数据驱动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派生的李括号控制 - 在偏航率和选择的动作方面 - 在没有预定义的动力学函数的情况下。这封信表明,基于学习的方法可用作合成非线性控制律的工具,以前只能通过基于专家的设计来解决。
更新日期:2021-04-01
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