ID 原文 译文
14025 同时，采用基于动力学逆模型前馈控制与 PID 反馈控制相结合的复合控制策略，提升放大机构的控制带宽和运动精度。 At the same time a compound control strategy which combines feedforward control based on the dynamic inverse model with PID feedback control is adopted to improve the control bandwidth and motion accuracy of the amplification mechanism．
14026 实验结果表明，复合控制策略在 1 Hz 输入频率下的均方根跟踪误差相较传统 PID 反馈控制降低了 33% ，在 10 Hz 下降低了 73% ，说明了基于动力学逆模型前馈控制同 PID 反馈控制相结合的复合控制能够大幅度提高放大机构的跟踪精度。 The experimental results show that compared with that of the traditional PID feedback control the ＲMS tracking error of the compound control strategy is reduced by 33% at 1 Hz input frequency and by 73% at 10 Hz． The compound control strategy which combines feedforward control based on dynamic inverse model with PID feedback control greatly improves the tracking accuracy of the amplification mechanism．
14027 针对加速试验中产品的失效机理是否发生改变的问题，基于加速因子不变原则,研究了工程上应用较为广泛的Weibull寿命分布失效机理一致性的检验方法。 To identify whether the product's failure mechanism in accelerating tests is changing,a testing method of failure mechanism consistency of Weibull life distribution based on the principle of acceleration factor constancy is studied.
14028 将Weibull分布转化为I型极值分布，进而研究极值分布尺度参数的统计检验方法，并采用尺度参数的Ansari-Bradley方法进行检验。 Weibull distribution is converted to I-type extreme value distribution. A method for statistical testing of the scale parameters in extreme value distribution is studied by using Ansari-Bradley method. B
14029 结合Arrhenius加速模型,基于最小二乘法估计、回归系数的性质构造t统计量,实现对加速因子与分布参数的统计检验,判别失效机理发生改变的应力。 y using Arrhenius acceleration model,t statistic is constructed based on the least squares method and characteristics of regression coefficients.Statistical testing of the acceleration factor and distribution parameters is implemented,and the stress under which the failure mechanism will change is distinguished.
14030 仿真模型验证了所提检验方法的有效性。 Simulation results show the effectiveness of the proposed method.
14031 提出一种基于Pareto解集的多目标模拟退火粒子群算法(MODPSO-SA),用于解决自主水下机器人(AUV)协同任务分配问题。 In view of cooperative task allocation of Autonomous Underwater Vehicle(AUV), a Multi-Objective Discrete Particle Swarm Optimization and Simulated Annealing(MODPSO-SA) algorithm based on Pareto is proposed.
14032 为避免粒子群算法陷入局部最优,加入改进的模拟退火技术,形成一种新的多目标局部搜索策略。 In order to make the particle swarm optimization avoid from falling into local minimum,a new multi-objective local search strategy is proposed by integrating the improved simulated annealing.
14033 仿真结果表明,MODPSO-SA算法能够得出多组合理Pareto解集,可以有效解决多AUV任务分配问题。 The simulation results show that the MODPSO-SA algorithm can obtain multiple Pareto solution sets, and solve the multi-AUV task allocation problem effectively.
14034 针对载机面对敌方来袭导弹自主规避问题,采取一种基于改进的DDPG算法的深度强化学习方法进行训练学习,在奖励函数中,除考虑规避性能外,还分别针对本机的高度保持、速度保持,以及来袭导弹的相对高度变化、接近速度变化建立奖励模型。 To solve the problem of autonomous evasion of the carrier aircraft facing incoming enemy missiles, a deep reinforcement learning method based on the improved DDPG algorithm is adopted for training and learning. In addition to considering the evasion performance in the reward function, rewarding models are established respectively for the cost of the aircraft's altitude maintenance and speed maintenance,as well as the relative altitude change and the approaching speed change of the incoming missile.