由于可重复使用运载器再入飞行包线大、参数快变、飞行环境复杂,因此很难用单个飞行模型描述飞行器的整个再入过程。将再入飞行轨迹划分为一系列子区域,并在各区域设计单独的控制器是一个行之有效的方法。目前关于飞行轨迹如何实现区域划分缺乏一套完整成熟的理论,本文以不同状态点模型之间的间隙度量值作为划分指标,在分析飞行器模型影响因素的基础上,以再入飞行轨迹为对象设计基于间隙度量的划分法。最后针对再入飞行轨迹进行区域的定量划分并给出结果。

现在飞行器为了提高可靠性和生存能力,装备了更多操纵面。控制分配用来将虚拟控制力矩分配到这些冗余的操纵面上。由于物理因素和气动因素,每个气动操纵面都存在一定的限制,这使得控制分配问题变得更加复杂。本文提出了一种基于加权伪逆法的新型控制分配算法。加权伪逆法是一种结构简单且计算效率高的控制分配算法,但是它不能给出最优的控制分配结果。遗传算法是一种随机搜索算法。本文中,它将被用来对加权矩阵进行优化,以实现最大的可达力矩集。为了提高方法的性能,期望力矩所在空间将被分为多个部分,并使用遗传算法为每一部分寻求最优的加权矩阵。与单一加权矩阵相比,多个加权矩阵可以保证新方法在每一部分都具有更好的性能。为了验证算法的有效性,直接分配法将用以作为对照。仿真结果表明,新方法性能较传统的加权伪逆法有较大提高。

Time-varying group formation tracking problems for high-order multi-agent systems with switching topologies and unknown input are studied. Agents are divided into subgroups to achieve desired time-varying subformations with switched topologies and track leaders with unknown control input. Firstly, a time-varying formation tracking protocol is designed using local relative information of agents and their neighbors, which can deal with topology switching and compensate the unknown input of leaders. Then an algorithm to obtain the parameters of control protocols is introduced. Based on the common Lyapunov theory, it is proved that the expected group formation tracking can be achieved by high-order multi-agent systems with switching directed graphs. A numerical simulation is presented to demonstrate the effectiveness of the theoretical results. © 2020 IEEE.

This paper presents a weapon-target assignment (WTA) method for multi-to-multi interception with grouping constraints based on penalty function method. Firstly, in order to evaluate the combat performance from the interception efficiency and the required energy, the interception probability function are constructed of the heading error, the time-to-go for moving targets and the line-of-sight rate. Secondly, to ensure that each target is allocated with sufficient interception resources, and meanwhile to achieve the effective interception for multiple missiles against multiple targets, an adaptive grouping strategy is presented. Then, based on the artificial bee colony algorithm, the steps for solving WTA problem with grouping constraints are given. Finally, the proposed WTA method is verified with numerical simulations. Results indicate that the proposed WTA methods can realize the optimal allocation scheme which satisfying adaptive grouping constraints. © 2019 IEEE.

This paper presents a weapon-target assignment (WTA) method for multi-to-multi interception with grouping constraints based on penalty function method. Firstly, in order to evaluate the combat performance from the interception efficiency and the required energy, the interception probability function are constructed of the heading error, the time-to-go for moving targets and the line-of-sight rate. Secondly, to ensure that each target is allocated with sufficient interception resources, and meanwhile to achieve the effective interception for multiple missiles against multiple targets, an adaptive grouping strategy is presented. Then, based on the artificial bee colony algorithm, the steps for solving WTA problem with grouping constraints are given. Finally, the proposed WTA method is verified with numerical simulations. Results indicate that the proposed WTA methods can realize the optimal allocation scheme which satisfying adaptive grouping constraints.
© 2019 IEEE.

This paper presents a three-dimensional guidance method based on sliding mode control (SMC) theory for attacking fixed target with time constraint. Assuming the configuration is axisymmetric, the missile movement is decomposed to the lateral plane and longitudinal plane. In the lateral plane, a guidance law is proposed based on the SMC theory, which controls the impact time and the miss-distance simultaneously. In the longitudinal plane, another SMC-based guidance law is implemented to converge the line-of-sight(LOS) rate rapidly. The convergence for both the lateral and longitudinal guidance law is proved by the Lyapunov theory. Simulation results indicate that the proposed three-dimensional guidance method has high control precision for both the impact time and miss-distance. The proposed guidance method is also demonstrated to have the ability to salvo attack.
© 2018 Technical Committee on Control Theory, Chinese Association of Automation.

The tail-sitter unmanned aerial vehicle（UAV） is a kind of vertical take-off and landing（VTOL） UAV which has the merits of both fixed-wing and rotary-wing.The tail-sitter UAV in this paper has three flight modes.The most important stage is the transition stage.The model for the transition stage is established and a transition flight strategy is constructed so that the UAV can change vertical takeoff mode to cruise mode stably.A nonlinear extended state observer is presented to estimate the unknown nonlinear terms,the uncertainties and external disturbances during mode transition.The backstepping controller based on the observer is designed to implement transition flight mode.A numerical simulation is given to demonstrate the effectiveness of the obtained results.

In this paper, a new multiple extended target tracking learning algorithm based on labelled random finite sets (L-RFS) framework is proposed to estimate the number, shape and state of extended targets under clutter conditions. The algorithm mainly includes two aspects: multi-extended target dynamic modeling and multi-extended target tracking estimates. Firstly, a finite mixture model (FMM) of extended target is established under the generalized labelled multi-bernoulli (GLMB) filter. Learning the parameters of finite mixture model by Gibbs sampling and Bayesian information criterion (BIC), and then equivalent point target measurements are used in place of the actual extended target measurements. Finally, the proposed ellipse approximation model is used to realize the estimation of the extended target shape. The simulation results show that the proposed algorithm can effectively track the multiple extended targets and obtain the shape of extended target. © 2017 IEEE.

The problem concerning the methods of passive localization based on multi-sensor has attracted widespread attentions in recent years.Traditional target localization estimate techniques assume that the number of targets in the localized areas is consistent and known at any moment.This paper considers a more realistic situation that the number of targets is unknown and time-varying under cluttered environment.In the first stage,we use the method of time difference of arrival to describe the observation model.In the second stage,we apply the random finite set theory to deal with such a more complicated localization problem from the perspective of set,by using the generalized labeled multi-Bernoulli filter with the assumption of independence of all targets.Finally,we estimate the position and the number of the time-varying targets simultaneously by using a sequential Monte Carlo implementation.To verify the proposed algorithm,the final experiment provides three pairs sensors to track two targets,which moving in a 2-dimension plane with constant velocity.

This paper presents a solution to the problem of tracking multiple targets from multistatic Doppler measurements. To the best of our knowledge our solution is the first principled multitarget tracker for multistatic Doppler radar. The tracker is based on a closed form solution to the labeled multitarget Bayes filter, which can be implemented with linear complexity in the number of measurements. Numerical examples are presented to illustrate the effectiveness of the solution on simulated data. © 2017 IEEE.