The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying L...The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.展开更多
推移质运动规律对河床演变具有重要影响,是河流动力学研究的重点和难点。本文开展中低水流条件下的推移质平衡输沙试验,将灰度相减方法和深度学习方法相结合,旨在提出一种优化的推移质运动颗粒识别算法,并在此基础上应用粒子跟踪测速技...推移质运动规律对河床演变具有重要影响,是河流动力学研究的重点和难点。本文开展中低水流条件下的推移质平衡输沙试验,将灰度相减方法和深度学习方法相结合,旨在提出一种优化的推移质运动颗粒识别算法,并在此基础上应用粒子跟踪测速技术(PTV)和卡尔曼滤波算法计算推移质颗粒运动轨迹,从而建立紊流相干结构与颗粒运动强度关系。为清晰捕捉运动距离较小颗粒,对YOLOv5(you only look once)目标检测模型网络结构进行卷积块改进、增加注意力机制和优化损失函数处理,以增强其在推移质颗粒识别任务中对极小目标的检测能力。结果表明:1)灰度相减方法可识别运动距离较大的颗粒,改进YOLOv5模型则能够更好识别运动距离较小的颗粒,通过合并优化两种方法,在本文设置的试验工况下可更准确识别推移质运动颗粒和运动轨迹;2)中低水流强度条件下,推移质泥沙颗粒运动强度受紊流相干结构影响沿水槽横向方向呈现间隔条带结构,并且随着水流强度增加,条带结构逐渐由密集变得稀疏,其宽度也由窄转宽,当水流强度继续增大时紊动掺混剧烈,条带结构遭到破坏逐渐消失;3)总体看来:泥沙颗粒运动多集中在水槽中间区域,边壁处运动较少,沙条带结构呈现中间宽两边窄的空间分布特征,表明条带结构是受紊流大尺度相干结构的影响而形成,而非二次流结构。展开更多
文摘The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.
文摘推移质运动规律对河床演变具有重要影响,是河流动力学研究的重点和难点。本文开展中低水流条件下的推移质平衡输沙试验,将灰度相减方法和深度学习方法相结合,旨在提出一种优化的推移质运动颗粒识别算法,并在此基础上应用粒子跟踪测速技术(PTV)和卡尔曼滤波算法计算推移质颗粒运动轨迹,从而建立紊流相干结构与颗粒运动强度关系。为清晰捕捉运动距离较小颗粒,对YOLOv5(you only look once)目标检测模型网络结构进行卷积块改进、增加注意力机制和优化损失函数处理,以增强其在推移质颗粒识别任务中对极小目标的检测能力。结果表明:1)灰度相减方法可识别运动距离较大的颗粒,改进YOLOv5模型则能够更好识别运动距离较小的颗粒,通过合并优化两种方法,在本文设置的试验工况下可更准确识别推移质运动颗粒和运动轨迹;2)中低水流强度条件下,推移质泥沙颗粒运动强度受紊流相干结构影响沿水槽横向方向呈现间隔条带结构,并且随着水流强度增加,条带结构逐渐由密集变得稀疏,其宽度也由窄转宽,当水流强度继续增大时紊动掺混剧烈,条带结构遭到破坏逐渐消失;3)总体看来:泥沙颗粒运动多集中在水槽中间区域,边壁处运动较少,沙条带结构呈现中间宽两边窄的空间分布特征,表明条带结构是受紊流大尺度相干结构的影响而形成,而非二次流结构。
