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Robust Landing using Time-To-Collision Measurement with Actuator Saturation

机译:使用具有执行器饱和的碰撞时间测量进行稳健着陆

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This paper considers a landing problem for an MAV that uses only a monocular camera for guidance. Although this sensor cannot measure the absolute distance to the target, by using optical flow algorithms, time-to-collision to the target is obtained. Existing work has applied a simple proportional feedback control to simple dynamics and demonstrated its potential. However, due to the singularity in the time-to-collision measurement around the target, this feedback could require an infinite control action. This paper extends the approach into nonlinear dynamics. In particular, we explicitly consider the saturation of the actuator and include the effect of the aerial drag. It is shown that the convergence to the target is guaranteed from a set of initial conditions, and the boundaries of such initial conditions in the state space are numerically obtained. The paper then introduces parametric uncertainties in the vehicle model and in the time-to-collision measurements. Using an argument similar to the nominal case, the robust convergence to the target is proven, but the region of attraction is shown to shrink due to the existence of uncertainties. The numerical simulation validates these theoretical results.
机译:本文考虑了仅使用单眼相机进行引导的MAV的着陆问题。尽管此传感器无法测量到目标的绝对距离,但通过使用光流算法,可以获得到目标的碰撞时间。现有工作已将简单的比例反馈控制应用于简单的动力学,并证明了其潜力。但是,由于目标周围碰撞时间测量的奇异性,此反馈可能需要无限的控制动作。本文将方法扩展到非线性动力学。特别是,我们明确考虑了执行器的饱和度,并包括了风阻的影响。结果表明,从一组初始条件可以保证收敛到目标,并且可以通过数值获得这种初始条件在状态空间中的边界。然后,本文介绍了车辆模型和碰撞时间测量中的参数不确定性。使用与标称情况相似的论点,证明了到目标的鲁棒收敛性,但是由于存在不确定性,吸引区域显示出缩小。数值模拟验证了这些理论结果。

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