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本文从爱国主义教育、价值观建立、科学思维培养以及装备应用案例四个方面着手,探究"电工与电路基础"课程中的具有军校特色的思政元素.通过对该课程中思政元素的分析解读,使课堂教学内涵更加深入,注重学员价值观培养的同时,为向部队培养具备科学思维和创新能力的高素质军事人才做好准备. 相似文献
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The quality of the micro-mechanical machining outcome depends significantly on the tracking performance of the miniaturized
linear motor drive precision stage. The tracking behavior of a direct drive design is prone to uncertainties such as model
parameter variations and disturbances. Robust optimal tracking controller design for this kind of precision stages with mass
and damping ratio uncertainties was researched. The mass and damping ratio uncertainties were modeled as the structured parametric
uncertainty model. An identification method for obtaining the parametric uncertainties was developed by using unbiased least
square technique. The instantaneous frequency bandwidth of the external disturbance signals was analyzed by using short time
Fourier transform technique. A two loop tracking control strategy that combines the μ-synthesis and the disturbance observer
(DOB) techniques was proposed. The μ-synthesis technique was used to design robust optimal controllers based on structured
uncertainty models. By complementing the μ controller, the DOB was applied to further improving the disturbance rejection
performance. To evaluate the positioning performance of the proposed control strategy, the comparative experiments were conducted
on a prototype micro milling machine among four control schemes: the proposed two-loop tracking control, the single loop μ
control, the PID control and the PID with DOB control. The disturbance rejection performances, the root mean square (RMS)
tracking errors and the performance robustness of different control schemes were studied. The results reveal that the proposed
control scheme has the best positioning performance. It reduces the maximal errors caused by disturbance forces such as friction
force by 60% and the RMS errors by 63.4% compared with the PID control. Compared to PID with DOB control, it reduces the RMS
errors by 29.6%. 相似文献
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着眼于提升变焦成像深度测量的精度与实时性,在给出系统设计构型的基础上,利用液体透镜调节特性与神经网络技术,提出了一种液体光学调控的新型单目视觉深度测量方法。首先,为消除液体重力效应引入光轴漂移对测量结果的影响,以目标图像面积之比作为特征参量,并给出了基于链码分类与条状分割的目标面积测算方法。然后,为描述液体透镜参数、图像特征量与目标深度之间的映射关系,构建了液体单目深度测量的神经网络模型,并通过遗传算法对模型参数进行优化。再者,对液体透镜参数进行标定获取光焦度函数,基于数据集训练得到用于深度测量的神经网络,其预测平均相对误差为0.799%。最后,设计实验对该方法进行测试验证,不同物距目标的深度测量误差平均为2.86%,其测量速度平均为108.2 ms,在1 000 mm物距条件下对不同形状目标的测量误差不超过3.60%。结果表明,融合液体光学调控与神经网络预测的单目视觉方法能够实现高精度、快速的深度测量,并且对不同形状目标均表现出较好的泛化性能。研究成果为克服变焦成像测距法的现有局限性提供了新的技术思路。 相似文献