Gaussian pigeon-inspired optimization approach to orbital spacecraft formation reconfiguration

With the rapid development of space technology, orbital spacecraft formation has received great attention from international and domestic academics and industry. Compared with a single monolithic, the orbital spacecraft formation system has many advantages. This paper presents an improved pigeon-inspired optimization(PIO) algorithm for solving the optimal formation reconfiguration problems of multiple orbital spacecraft. Considering that the uniform distribution random searching system in PIO has its own weakness, a modified PIO model adopting Gaussian strategy is presented and the detailed process is also given. Comparative experiments with basic PIO and particle swarm optimization(PSO) are conducted, and the results have verified the feasibility and effectiveness of the proposed Gaussian PIO(GPIO) in solving orbital spacecraft formation reconfiguration problems.     

Multi-UAV coordination control by chaotic grey wolf optimization based distributed MPC with event-triggered strategy

The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method.     

通信约束下异构多无人机任务分配方法

针对异构多无人机协同执行侦察和打击任务中,存在通信距离、时间延迟等约束条件下的局部任务分配问题,提出了一种基于合同网的分布式多无人机任务分配方法。首先建立了异构集群发现新目标时的局部任务分配问题模型,设计了局部无人机通信网络中的信息一致性算法,实现了任务分配过程中任务发布阶段各无人机的冲突消解。设计了任务分配过程中的联盟构建和无人机资源管理方法,使联盟中各无人机能够以更加平衡的方式消耗资源。仿真结果表明,该方法能够解决通信约束下,异构多无人机执行察打任务时,所触发的针对目标打击任务的任务分配问题,且能够获得最大的系统效能。     

均等通信时滞下多UAV协同编队控制

多无人机(UAV)系统编队控制中,时滞是无法回避的问题,研究时滞对多UAV编队形成和系统稳定性的影响,具有重要理论价值。重点研究均等通信时滞下多UAV协同编队控制问题,并获得系统的稳定性条件。首先,设计具有均等通信时滞的协同编队控制律,得到多UAV编队系统的闭环时滞状态方程;在恒定均等时滞下,考虑到系统模型不确定性,基于线性矩阵不等式(LMI)理论得到系统的时滞依赖稳定性条件;最后,进行仿真实验,结果表明多UAV编队系统是稳定的,期望的编队队形能够形成并保持。     

基于罚函数序列凸规划的多无人机轨迹规划

多无人机（UAVs）轨迹规划是具有非线性运动约束和非凸路径约束的最优控制问题。引入序列凸规划思想,将非凸最优控制问题近似为一系列凸优化子问题,并利用成熟的凸优化算法进行求解,以更好地权衡最优性和时效性。首先,建立了多无人机协同轨迹规划的非凸最优控制模型。然后,利用离散化和凸近似方法将其转换为凸优化问题,包括对无人机运动模型的线性化,以及对威胁规避约束和无人机碰撞约束的凸化。同时,提出了一种离散点间的威胁规避方法,保证无人机在离散轨迹点间的飞行安全。在凸优化模型的基础上,给出了基于罚函数序列凸规划求解多无人机轨迹规划的具体框架。最后,通过数值仿真验证了方法的有效性,结果表明该方法在多机轨迹规划结果的最优性和时效性都要优于伪谱法,而且优势随编队数量的增加而增大。     

Neural network-based model predictive control with fuzzy-SQP optimization for direct thrust control of turbofan engine

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming (SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction. Considering the strong nonlinearity of engine, a predictive model is established by Back Propagation (BP) neural network for the entire flight envelope, whose input and output are determined with random forest algorithm and actual situation analysis. Rolling optimization typically uses SQP as the optimization algorithm, but SQP algorithm is easy to trap into local optimization. Therefore, the fuzzy-SQP algorithm is proposed to prevent this disadvantage using fuzzy algorithm to determine the initial value of SQP. In addition to the traditional three parts of model predictive control, an online correction module is added to improve the predictive accuracy of the predictive model in the predictive time domain. Simulation results show that the BP predictive model can reach a certain degree of predictive accuracy, and the proposed control system can achieve good tracking performance with the limited parameters within the safe range.     

联合连通拓扑下多无人机编队控制

针对在联合连通拓扑条件下的多无人机系统,研究了具有二阶动力学系统模型的多无人机系统时变编队控制问题。基于一致性理论,设计了一致性控制器,将联合连通拓扑条件下的编队控制问题简化为低阶时间平均系统的渐进稳定性问题。利用Lyapunov函数证明了所设计的控制器能够实现编队控制,并利用线性矩阵不等式(LMI)方法给出了控制器的设计算法。在三维空间中,对多无人机系统进行了仿真,验证了所设计的一致性控制器能够使得多无人机系统在联合连通拓扑条件下形成时变编队。     

基于协同进化粒子群和Pareto最优解的卫星编队队形重构方法

针对卫星编队自主队形重构问题,提出了基于协同进化粒子群优化(CPSO)和Pareto最优解的求解方法.首先,使用Legendre伪谱法(LPM)将队形重构问题离散化为非线性规划(NLP)问题;其次,根据卫星编队的特点及碰撞规避的需要,使用CPSO算法对重构问题采用既独立又集中的求解方式,避免了传统优化方法对梯度的求解;...     

Fast cooperative trajectory optimization and test verification for close-range satellite formation using Finite Fourier Series method

The process of formation reconfiguration for close-range satellite formation should take into account the risk of collisions between satellites. To this end, this paper presents a method to rapidly generate low-thrust collision-avoidance trajectories in the formation reconfiguration using Finite Fourier Series (FFS). The FFS method can rapidly generate the collision-avoidance three-dimensional trajectory. The results obtained by the FFS method are used as an initial guess in the Gauss Pseudospectral Method (GPM) solver to verify the applicability of the results. Compared with the GPM method, the FFS method needs very little computing time to obtain the results with very little difference in performance index. To verify the effectiveness, the proposed method is tested and validated by a formation control testbed. Three satellite simulators in the testbed are used to simulate two-dimensional satellite formation reconfiguration. The simulation and experimental results show that the FFS method can rapidly generate trajectories and effectively reduce the risk of collision between satellites. This fast trajectory generation method has great significance for on-line, constantly satellite formation reconfiguration.     

Integrated active fault-tolerant control using IMM approach

An integrated fault detection, diagnosis, and reconfigurable control scheme based on interacting multiple model (IMM) approach is proposed. Fault detection and diagnosis (FDD) is carried out using an IMM estimator. An eigenstructure assignment (EA) technique is used for reconfigurable feedback control law design. To achieve steady-state tracking, reconfigurable feedforward controllers are also synthesized using input weighting approach. The developed scheme can deal with not only actuator and sensor faults, but also failures in, system components. To achieve fast and reliable fault detection, diagnosis, and controller reconfiguration, new fault diagnosis and controller reconfiguration mechanisms have been developed by a suitable combination of the information provided by the mode probabilities from the IMM algorithm and an index related to the closed-loop system performance. The proposed approach is evaluated using an aircraft example, and excellent results have been obtained     

基于改进Q学习的IMA系统重构蓝图生成方法

重构蓝图定义了故障状态下系统软硬件资源的重新配置方案,是实现综合模块化航空电子系统重构容错的关键。提出了一种基于改进Q学习的重构蓝图生成方法,综合考虑负载均衡、重构影响、重构时间、重构降级等多优化目标,并应用模拟退火框架改进探索策略,提高了传统Q学习算法的收敛性能。实验结果表明,与模拟退火算法、差分进化算法、传统Q学习算法相比,本文提出的改进Q学习算法效率更高,所生成重构蓝图质量更高。     

Multiple air route crossing waypoints optimization via artificial potential field method

Air route crossing waypoint optimization is one of the effective ways to improve airspace utilization, capacity and resilience in dealing with air traffic congestion and delay. However, research is lacking on the optimization of multiple Crossing Waypoints (CWPs) in the fragmented airspace separated by Prohibited, Restricted and Dangerous areas (PRDs). To tackle this issue, this paper proposes an Artificial Potential Field (APF) model considering attractive forces produced by the optimal routes and repulsive forces generated by obstacles. An optimization framework based on the APF model is proposed to optimize the different airspace topologies varying the number of CWPs, air route segments and PRDs. Based on the framework, an adaptive method is developed to dynamically control the optimization process in minimizing the total air route cost. The proposed model is applied to a busy controlled airspace. And the obtained results show that after optimization the safety-related indicators: conflict number and controller workload reduced by 7.75% and 6.51% respectively. As for the cost-effectiveness indicators: total route length, total air route cost and non-linear coefficient, declined by 1.74%, 3.13% and 1.70% respectively. While the predictability indicator, total flight delay, saw a notable reduction by 7.96%. The proposed framework and methodology can also provide an insight in the understanding of the optimization to other network systems.     

频率不变宽带波束形成权重系数的稀疏优化

在基本的傅里叶变换频率不变波束形成(FIB)基础上,从减少抽头权重系数数量,降低FIB运算量角度出发,提出基于最小l0范数的抽头权重系数稀疏优化模型,并采用正交匹配追踪(OMP)算法来求解该优化问题。在高增益、等波纹、低旁瓣FIB方向图的要求下,所提出的优化方法使得稀疏率降低到3.53%,且能够保证稀疏优化后的方向图误差小于1%。接着进一步开展阵元数量的稀疏优化,有效地减少了阵元通道数,进一步降低了算法实现的硬件复杂度。仿真结果验证了所提方法的正确性和有效性。     

Structural topology optimization under stationary random base acceleration excitations

Structural topology optimization subjected to stationary random base acceleration excitations is investigated in this paper. In the random response analysis, the Large Mass Method(LMM) which attributes artificial large mass values at each driven nodal Degree Of Freedom(DOF) to transforming the base acceleration excitations into force excitations is proposed. The Complete Quadratic Combination(CQC) which is commonly used to calculate the random responses in previous optimization has been proven to be computationally expensive especially for large-scale problems. In order to conquer this difficulty, the Pseudo Excitation Method(PEM) and the combined method of PEM and Mode Acceleration Method(MAM) are adopted into the dynamic topology optimization, and random responses are calculated using these two methods to ascertain a high efficiency over the CQC. A density-based topology optimization method minimizing dynamic responses is then formulated based on the integration of LMM and PEM or the combined method of PEM and MAM. Numerical examples are presented to verify the accuracy of the proposed schemes in dynamic response analysis and the quality of the optimized design in improving dynamic performance.     

An optimization method for metamorphic mechanisms based on multidisciplinary design optimization

The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization（MDO）. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations,and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.     

基于改进CST参数化方法和转捩模型的翼型优化设计

为提高翼型优化设计效率,增大设计空间,采用B样条基函数替代传统的形状类别函数(CST)方法中的Bezier多项式,增强了对翼型参数化表达的局部控制能力并提高了翼型局部表达精度。为了确保翼型在优化设计过程中的几何光顺特性和代理模型的准确性,采用小波分解技术提出了多分辨率翼型的局部光顺方法。采用基于本征正交分解(POD)的流场数值代理模型,并结合γ-Reθt转捩模型实现了快速准确的气动力与流动转捩预测。采用小波技术光顺的CST翼型参数化建模、POD流场数值计算代理模型以及γ-Reθt转捩模型,结合遗传算法建立了完整的翼型气动优化设计系统。针对低速层流翼型与超临界翼型进行优化设计,优化设计后的翼型升阻比分别提高了47.42%和45.85%,且对改进前后参数化建模方法的优化性能进行了对比,结果表明本文构建的翼型气动优化设计系统具备很高的优化效率。     

编队卫星队形重构防碰撞最优轨迹规划

 针对编队卫星队形重构的轨迹规划问题,提出了直接配置混合整数线性规划(DCMILP)方法。首先将卫星编队飞行问题进行简化,整个过程使用线性化描述;继而将三阶Simpson方法扩展至编队卫星的队形重构过程中,将各卫星的状态量和控制量在各节点处离散化;然后根据目标函数及碰撞规避问题等各种约束条件,将整个过程转化为混合整数线性规划问题,从而可以找到该非凸问题的全局最优解。最后,通过对三维和二维两组编队卫星队形重构进行仿真,由结果可以看出,与传统方法相比该方法快速有效,能够满足实时性的要求,使得卫星编队的自主运行成为可能。     

基于重要性测度降维的液压管路稳健优化设计

提出一种基于重要性测度的降维预处理优化方法,对不确定环境下飞机局部液压管路系统的稳健性优化问题展开研究。引入区间模型描述随机变量分布参数（即设计变量）的不确定性,引入基于方差的重要性测度衡量设计变量对优化目标的贡献程度,从而筛选出对优化目标影响较大的设计变量,简化优化模型。该方法可以有效降低优化问题的维数,进而减少优化的计算成本。通过数值算例验证了所提方法的合理性和可行性,并采用该方法对飞机局部液压管路系统进行了稳健性优化设计。     

Robustness evaluation method for unmanned aerial vehicle swarms based on complex network theory

Unmanned Aerial Vehicle (UAV) swarms have been foreseen to play an important role in military applications in the future, wherein they will be frequently subjected to different disturbances and destructions such as attacks and equipment faults. Therefore, a sophisticated robustness evaluation mechanism is of considerable importance for the reliable functioning of the UAV swarms. However, their complex characteristics and irregular dynamic evolution make them extremely challenging and uncertain to evaluate the robustness of such a system. In this paper, a complex network theory-based robustness evaluation method for a UAV swarming system is proposed. This method takes into account the dynamic evolution of UAV swarms, including dynamic reconfiguration and information correlation. The paper analyzes and models the aforementioned dynamic evolution and establishes a comprehensive robustness metric and two evaluation strategies. The robustness evaluation method and algorithms considering dynamic reconfiguration and information correlation are developed. Finally, the validity of the proposed method is verified by conducting a case study analysis. The results can further provide some guidance and reference for the robust design, mission planning and decision-making of UAV swarms.     

基于多机协作的认知无人机网络能效联合优化

针对无人机（UAV）通信网络中频谱资源紧缺的问题,构建基于认知无线电的多无人机通信网络,通过多机协作频谱感知有效探索授权频谱。提出一种基于Bisection算法的迭代算法,通过联合优化感知时间和判决门限对构建的复杂非凸问题求解,显著提高了无人机次级认知网络的能量效率（EE）。分析了无人机飞行过程中能效的变化情况,仿真结果表明,存在最优感知时间使能效获得最大值,且判决门限的选择会影响该能效最优值;提出的高能效迭代算法具有较好收敛性,有效提高了认知无人机网络的能量利用率。