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361.
Distributed fixed-time attitude coordinated control for multiple spacecraft with actuator saturation
This paper investigates the distributed fixed-time attitude coordinated control problem for multiple spacecraft subject to actuator saturation under the directed topology. First, a distributed fixed-time observer is presented for each follower spacecraft to estimate the leader spacecraft’s states. Compared with the commonly used fixed-time observer, the settling time of the proposed fixed-time observer can be easily adjusted by some free design parameters. Next, a distributed fixed-time control ... 相似文献
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363.
针对低速巡飞弹倾斜转弯控制系统的非线性、强耦合、不确定的特征,研究了一种基于干扰观测器的自适应反演控制方法。将参数摄动、外界干扰和执行机构的响应偏差分别等效进成系统的匹配干扰和非匹配干扰,并设计在有限时间内估计误差收敛的非线性干扰观测器进行估计。利用反演控制的思想,在虚拟控制量中抵消非匹配干扰,在实际控制量中抵消匹配干扰。利用李雅普诺夫理论设计自适应调节律补偿干扰观测器的估计误差,改进控制系统的瞬态性能。仿真结果表明,干扰观测器估计误差在有限时间内收敛到一定区间,系统能够有效地克服干扰的影响,快速准确地跟踪滚转角、攻角、侧滑角参考指令。 相似文献
364.
《中国航空学报》2023,36(6):340-360
Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy. 相似文献
365.
基于飞行-推力一体化思想提出了一种针对搭载超燃冲压发动机的吸气式高超声速飞行器速度通道的状态/输入约束自适应鲁棒保性能安全控制方案。首先根据超燃冲压发动机的机理分析与计算流体动力模型数据,建立了安全子系统与性能子系统面向控制的仿射非线性模型。之后基于障碍Lyapunov理论与动态面设计方法设计了一套安全子系统状态约束控制器,从理论上保证了飞行器在跟踪指令的全过程中,发动机相关状态不会触碰安全边界,并结合自适应技术与辅助系统提高了该控制系统的鲁棒性。针对性能子系统设计了一套鲁棒自抗扰控制器,达到“保证安全的前提下不折损性能”的目的。仿真结果表明所设计的控制系统可以在保障安全的同时达到预想的性能,并显著放宽了超燃冲压发动机对飞行器飞行姿态的约束,保证了高超声速飞行器的机动灵活性。 相似文献
366.