An Iterative Learning Approach for Motion Control and Performance Enhancement of Quadcopter UAVs
An Iterative Learning Approach for Motion Control and Performance Enhancement of Quadcopter UAVs
复制标题
四轴飞行器无人机运动控制和性能增强的迭代学习方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
J. Shamma
中科院分区：
文献类型：
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作者：
Mohammad Shaqura;J. Shamma
移动的机器人建模是一项具有挑战性的任务，特别是对于具有复杂非线性动力学的车辆。四轴无人机是一种敏捷系统，为了便于分析和控制设计，通常用名义非线性模型来描述，该模型忽略了各种复杂的动力学现象。这种简化导致限制车辆性能。为了克服这个问题，迭代学习的方法，提出了一个标称表示的系统动态与飞行试验，以提高性能。我们的目标是学习积极地导航四轴飞行器通过课程，同时避免障碍物。性能通过总体导航时间进行评估。通过将来自简化标称模型的近似梯度与实际实现的飞行轨迹混合来迭代地优化轨迹。由此产生的优化是一个二次规划，可以有效地解决。具有多个测试用例的高保真度四轴飞行器仿真通过反复试验显示出显着提高的性能。
Mobile robot modeling is a challenging task especially for vehicles with complex nonlinear dynamics. Quadcopter UAVs are agile systems that are often described with a nominal nonlinear model that neglects various complicated dynamic phenomena for the sake of easier analysis and control design. This simplification leads to limiting the vehicle performance. To overcome this issue, an iterative learning approach is presented where a nominal representation of the system dynamics is used in conjunction with flight trials to improve performance. The objective is to learn to aggressively navigate a quadcopter through a course while avoiding obstacles. The performance is assessed by overall navigation time. The trajectory is optimized iteratively by blending an approximate gradient from the simplified nominal model with actual realized flight trajectories. The resulting optimization is a quadratic program, which can be solved efficiently. High fidelity quadcopter simulations with multiple test cases show significantly improved performance through repeated trials.