论文标题
学习模型的二次预测控制
Learning Model Predictive Control for Quadrotors
论文作者
论文摘要
空中机器人可以通过有效利用给定任务期间收集的信息来增强其在复杂和混乱的环境中的安全和敏捷导航。在本文中，我们解决了二次学习模型预测控制问题。我们设计了一个学习回收的学习 - Horizon非线性控制策略，直接在系统非线性歧管配置空间上配制了SO（3）XR^3。所提出的方法利用了过去的成功任务迭代，以随着时间的推移提高系统性能，同时尊重系统动态和执行器约束。我们进一步放松了其计算复杂性，使其与实时四极管控制要求兼容。我们展示了拟议方法在学习最短时间控制任务，尊重动态，执行器和环境限制方面的有效性。模拟和现实世界设置的几项实验验证了所提出的方法。
Aerial robots can enhance their safe and agile navigation in complex and cluttered environments by efficiently exploiting the information collected during a given task. In this paper, we address the learning model predictive control problem for quadrotors. We design a learning receding--horizon nonlinear control strategy directly formulated on the system nonlinear manifold configuration space SO(3)xR^3. The proposed approach exploits past successful task iterations to improve the system performance over time while respecting system dynamics and actuator constraints. We further relax its computational complexity making it compatible with real-time quadrotor control requirements. We show the effectiveness of the proposed approach in learning a minimum time control task, respecting dynamics, actuators, and environment constraints. Several experiments in simulation and real-world set-up validate the proposed approach.
