ID 原文 译文
56918 最后通过仿真验证了所建立的无人机空战目标意图预测模型能有效预测无人机空战目标意图. Finally, the simulation results show that the proposed model caneffectively predict the target intention of UAVs in air combat.
56919 针对第三关节为被动的平面四连杆(active-active-passive-active,简称AAPA)欠驱动机械臂的位姿控制问题,提出一种基于模型降阶与链式结构的控制策略.整个控制过程被划分为3个阶段. A control strategy is proposed based on the model reduction and the chained structure for a planarfour-link (Active-Active-Passive-Active, AAPA) underactuated manipulator to achieve its position-posture controlobjective. The whole control process is divided into three stages. In the first stage, the planar AAPA manipulatoris reduced to the planar virtual three-link (Active-Active-Passive, AAP) manipulator by controlling the angle ofthe fourth link and rotating it to zero.
56920 第1阶段,通过控制第四连杆的角度至零,将平面AAPA机械臂降阶为平面虚拟四连杆(active-activepassive,简称AAP)欠驱动机械臂.第2阶段,首先将平面虚拟AAP机械臂的模型转换为标准链式结构形式.然后,设计相应的控制器将平面虚拟AAP机械臂的被动关节控制至其目标位置,同时,将被动连杆的姿态角控制至中间姿态角,从而将平面AAPA机械臂进一步降阶为平面Acrobot. In the first stage, the planar AAPA manipulatoris reduced to the planar virtual three-link (Active-Active-Passive, AAP) manipulator by controlling the angle ofthe fourth link and rotating it to zero. In the second stage, the model of a planar virtual AAP manipulator istransformed into the standard chain structure form. Then, the corresponding controllers are designed to controlthe passive joint of the planar virtual AAP manipulator to its target position, and the posture angle of thepassive link is controlled to its middle posture angle at the same time. At the end of this stage, the planarAAPA manipulator is reduced to the planar Acrobot.
56921 第3阶段,控制平面Acrobot驱动连杆的角度至其目标角度,连带实现对被动连杆的角度控制,最终实现平面AAPA机械臂的位姿控制目标. In the third stage, the angle of the active link of the planarAcrobot is controlled to its target angle. Also, the angle control of the passive link of the system is realized. Consequently, the position-posture control objective of the planar AAPA manipulator is recognized.
56922 考虑到平面Acrobot存在角度约束,因此,利用遗传算法协调与优化被动关节的目标位置、被动连杆的中间姿态角、第四连杆的目标角度与被动连杆的目标姿态角,确保平面Acrobot对于系统目标位姿的目标角度解存在. Consideringthe angle constraint of the planar Acrobot, the genetic algorithm is used to coordinate and optimize the targetangle of the passive joint, the middle posture angle of the passive link, the target angle of the fourth link, and thetarget posture angle of the passive link. These ensure the target angles of the planar Acrobot corresponding tothe target position-posture of the system can be found.
56923 最后,通过仿真验证控制策略的有效性. Finally, simulation results demonstrate the effectivenessof the proposed control strategy.
56924 气体传感器阵列的优化是电子鼻领域亟需解决的关键问题之一,同时也是一种特殊的特征选择问题. Gas sensor array optimization is a key problem in the field of electronic noses, and it is also a specialfeature selection problem.
56925 本文结合特征相关性和特征重要度,提出了一种有效的新型传感器(特征)重要性衡量方法——动态特征重要度,并在此基础上提出了一种新的基于动态特征重要度的电子鼻传感器阵列优化算法SAO DFI. In this paper, we propose a novel measure of sensor (or feature) importance, nameddynamic feature importance, based on feature correlation and feature importance. Also, we propose an effectiveelectronic nose sensor array optimization algorithm SAO DFI based on the dynamic feature importance.
56926 通过对两种不同的气体环境下采集的数据进行分析,测试了重复传感器、传感器(特征)重要度、传感器(特征)相关性以及传感器特征参数对SAO DFI算法的影响,其优化结果证明了该阵列优化算法的有效性、鲁棒性和可解释性. Weanalyze the effects of repeated sensors, sensor (or feature) importance, sensor (or feature) correlation, and sensorcharacteristic parameters, based on the proposed SAO DFI algorithm using data collected in two different gasenvironments. The optimization results demonstrate the effectiveness, robustness, and interpretability of thearray optimization algorithm.
56927 面向末端具有可变负载的压电柔性悬臂梁振动主动控制,本文提出了一种基于改进多模型切换性能指标函数的多模型混合自适应振动主动控制方法. To solve the vibration suppression problem for a piezoelectric flexible cantilever beam with varyingload, this study proposes a new type of multiple model hybrid adaptive vibration control method with a newmultiple model switching cost index function.