导弹复合控制系统的模糊逻辑控制分配

针对空空导弹复合控制系统中的控制分配问题,提出了一种模糊逻辑控制分配算法。采用动态面控制方法设计了复合控制量的控制律。在设计过程中,利用扩张状态观测器估计汇总不确定项,并采用光滑二阶滑模控制算法设计了控制律,达到了提高鲁棒性和削弱抖振现象的目的。应用模糊逻辑控制分配算法将复合控制量分解为直接力控制指令和舵偏角控制指令。为了检验所设计的复合控制系统的控制效果,对采用基于模糊逻辑控制分配算法的复合控制系统和传统的气动力控制系统进行了对比仿真实验。仿真结果表明,所设计的复合控制系统具有更好的控制效果。     

一类控制受限不确定系统的控制分配重构控制

针对一类控制受限的不确定性系统,提出了一种基于控制分配理论的重构控制策略。该重构策略充分考虑了作动器偏转受约束的情形,基于伪逆控制分配算法,利用自适应控制理论和李雅普诺夫稳定理论,设计了控制分配阵的自适应控制律,使得系统在作动器发生饱和的情形下自适应地调节控制分配阵,从而改变作用于作动器的控制指令,完成系统的重构任务。最后以某多操纵面战机模型为算例对算法予以验证,仿真结果表明了算法的合理性。     

Optimal time-varying load sharing for divisible loads

A load sharing problem involving the optimal load allocation of divisible loads in a distributed computing system consisting of N processors interconnected through a bus-oriented network is investigated. For a divisible lend, the workload is infinitely divisible so that each fraction of the workload can be distributed and independently computed on each processor. For the first time in divisible load theory, an analysis is provided in the case when the processor speed and the channel speed are time varying due to background jobs submitted to the distributed system with nonnegligible communication delays. A numerical method to calculate the average of the time-varying processor speed and the channel speed and an algorithm to find the optimal allocation of the workload to minimize the total processing finish time are proposed via a deterministic analysis. A stochastic analysis which makes use of Markovian queueing theory is introduced for the case when arrival and departure times of the background jobs are not known     

基于布谷鸟搜索算法的一类变体飞行器容错控制

针对一类存在执行机构故障的分布式结构变体飞行器的控制分配问题,结合整数规划理论,提出一种基于布谷鸟搜索算法的容错控制方法。首先,设计虚拟控制指令,使得系统状态能够很好地跟踪参考模型;然后,将执行器概率性故障与饱和约束转换为整数规划问题中决策变量的约束,从而将执行器控制分配问题转化为一类整数规划问题;最后,采用改进的布谷鸟搜索算法进行求解,得到实际的执行器控制分配指令。仿真结果表明,在执行器存在概率性故障的情况下,该容错控制方法较无容错策略的情况能够有效提升系统的跟踪性能;与遗传算法相比,该算法得到的执行器控制分配结果更加精确。     

多目标决策的交互式maximin方法及其在资源分配中的应用

多目标决策的交互式ｍａｘｉｍｉｎ方法一直没有解决如何根据决策者的愿望（特别是变化了的愿望）调整满意解。本文研究了调整原理,调整过程中所要调整的参数具有明确的实际意义。将交互式ｍａｘｉｍｉｎ方法应用到资源分配问题,建立了资源分配的多目标决策模型,并设计了系统分解协调的求解方法。     

Anti-disturbance attitude control of combined spacecraft with enhanced control allocation scheme

In this paper, we propose a novel anti-disturbance attitude control law for combined spacecraft with an improved closed-loop control allocation scheme. More specifically, a saturated approach is adopted to guarantee the global asymptotic stability under control input saturation. To enhance the robustness of the system, a nonlinear disturbance observer is constructed to compensate the disturbances caused by inertial parameter uncertainty and unmodeled dynamics. Next, the quadratic programming algorithm is used to obtain an optimal open-loop control allocation scheme, where both energy consumption and actuator saturation have been considered in the allocation of the virtual control command. Then, a modified closed-loop control allocation scheme is proposed to reduce the allocation error under the actuator uncertainty. Finally, stability analysis of the closed-loop system with the proposed allocation scheme is provided. Simulation results confirm the effectiveness of the proposed control scheme.     

An extended chirp scaling algorithm for spaceborne sliding spotlight synthetic aperture radar imaging

A system impulse response with low sidelobes is critical in synthetic aperture radar（SAR） images because sidelobes contribute to noise and interfere with nearby scatterers. However,the conventional tricks of sidelobe suppression are unable to be exactly applied to the case of spaceborne sliding spotlight SAR due to great azimuth shifts in both time and frequency domains. In this paper, an extended chirp scaling algorithm is presented for spaceborne sliding spotlight SAR data imaging. The proposed algorithm firstly uses the spectral analysis（SPECAN） technique to avoid the azimuth spectrum folding effect and then employs the chirp scaling（CS） algorithm to achieve data focusing, i.e., the so-called two-step approach. To suppress the sidelobe level, an efficient strategy for the azimuth spectral weighting which only involves matrix multiplications and short fast Fourier transformations（FFTs） is proposed, which is a post-process executed on the focused SAR image and particularly simple to be implemented. The SAR image processed by the proposed extended CS algorithm is very precise and perfectly phase-preserving. In the end, computer simulation results verify the analysis and confirm the validity of the proposed algorithm.     

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。     

资源分配的混合式决策支持系统

本文研究静态资源分配问题。首先利用统计报表的硬数据和代表经理人员经验的软数据来建立资源分配问题的数学模型,然后用有约束非线性优化的方法作出最优资源分配的决策。     

Uncertain multiobjective redundancy allocation problem of repairable systems based on artificial bee colony algorithm

Based on the uncertainty theory, this paper is devoted to the redundancy allocation problem in repairable parallel-series systems with uncertain factors, where the failure rate, repair rate and other relative coefficients involved are considered as uncertain variables. The availability of the system and the corresponding designing cost are considered as two optimization objectives. A crisp multiobjective optimization formulation is presented on the basis of uncertainty theory to solve this resultant problem. For solving this problem efficiently, a new multiobjective artificial bee colony algorithm is proposed to search the Pareto efficient set, which introduces rank value and crowding distance in the greedy selection strategy, applies fast non-dominated sort procedure in the exploitation search and inserts tournament selection in the onlooker bee phase. It shows that the proposed algorithm outperforms NSGA-II greatly and can solve multiobjective redundancy allocation problem efficiently. Finally, a numerical example is provided to illustrate this approach.     

不确定环境下多无人机任务分配

针对复杂战场环境下无人机与攻击目标之间距离的不确定性,将该距离抽象为一个区间数。在此基础上,构建了不确定环境下多无人机任务分配的数学模型。根据多无人机任务分配问题的特殊性,重新设计了差分进化算法的编码方式、变异操作、交叉操作等。其中,选择操作中,在区间数排序方法的基础上,依照可能度来计算候选解被选中的概率。鉴于差分进化算法中不同变异策略的内在特点和适用场合不尽相同,提出了 3种多变异策略的差分进化算法,以便最大限度地发挥各种变异策略的技术优势。针对 CEC2013测试函数和多无人机任务分配问题分别开展仿真实验,实验结果表明,多变异策略的差分进化算法其性能改进显著,非常适合于求解多无人机任务分 配问题。     

两类混合优化控制分配算法的灵敏度分析

在研究指令灵敏度的基础上,定义了一种描述控制分配算法响应飞机包线范围内工作状态变化的灵敏度.阐明了控制分配模块的功能,对比研究了两类实时性较好的混合优化控制分配算法:线性混合优化法和二次混合优化法;分别采用单纯型法和有效集法进行求解,结合指令灵敏度和状态灵敏度的定义分析了两类混合优化方法的灵敏度.计算结果表明,在虚拟指...     

航天测控资源统一分配的多维遗传算法

对于航天器测控资源分配这一类时间窗受限的约束满足问题,国内外已有许多应用遗传算法的研究并取得了较好的成果。目前的研究主要针对的是低轨道航天器这类可见弧段较短、全弧段跟踪的问题,对于地球同步以及高轨道航天器这一类长弧段可见、非全弧段跟踪的资源分配问题,相关的应用研究较少。针对高低轨航天器测控资源统一分配问题的特点,经过对标准遗传算法的扩展,设计了一种能够描述高、低轨道航天器资源分配问题的多维编码方法,定义了相应的交叉算子、变异算子等算法要素,从而建立了一种能够解决航天测控资源统一分配问题的多维遗传算法。仿真计算表明,该算法能够有效解决目前我国航天测控网面临的"一网多星"管理模式下,高低轨航天器统一管理、统一分配测控资源的问题。     

Optimal sensor selection strategy for discrete-time stateestimators

A novel sensor selection strategy is introduced, which can be implemented on-line in time-varying discrete-time system. We consider a case in which several measurement subsystem are available, each of which may be used to drive a state estimation algorithm. However, due to practical implementation constraints (such as the ability of the on-board computer to process the acquired data), only one of these subsystems can actually by utilized at a measurement update. An algorithm is needed, by which the optimal measurement subsystem to be used is selected at each sensor selection epoch. The approach described is based on using the square root V-Lambda information filter as the underlying state estimation algorithm. This algorithm continuously provides its user with the spectral factors of the estimation error covariance matrix, which are used in this work as the basis for an on-line decision procedure by which the optimal measurement strategy is derived. At each sensor selection epoch, a measurement subsystem is selected, which contributes the largest amount of information along the principal state space direction associated with the largest current estimation error. A numerical example is presented, which demonstrates the performance of the new algorithm. The state estimation problem is solved for a third-order time-varying system equipped with three measurement subsystem, only one of which can be used at a measurement update. It is shown that the optimal measurement strategy algorithm enhances the estimator by substantially reducing the maximal estimation error     

基于改进烟花算法的资源分配

资源分配问题作为一个NP-Hard问题,在云计算、无线电、卫星调度、多无人机协同作业等领域皆有研究需求,是一个共性的数学问题。烟花算法作为一种智能优化算法,具有求解大规模资源分配问题的能力,但也存在求解精度低等问题。为了提高传统烟花算法的计算效率和全局寻优能力,提出一种改进烟花算法,用遗传算法中的变异算子替代高斯变异操作,并增加模拟退火流程。最后在多无人机协同作业任务分配数学模型上进行仿真验证,实验结果表明在收敛速度以及计算精度方面,该算法均优于其余3种烟花算法。     

Multi-UAV reconnaissance task allocation for heterogeneous targets using an opposition-based genetic algorithm with double-chromosome encoding

This paper presents a novel multiple Unmanned Aerial Vehicles (UAVs) reconnaissance task allocation model for heterogeneous targets and an effective genetic algorithm to optimize UAVs’ task sequence. Heterogeneous targets are classified into point targets, line targets and area targets according to features of target geometry and sensor’s field of view. Each UAV is regarded as a Dubins vehicle to consider the kinematic constraints. And the objective of task allocation is to minimize the task execution time and UAVs’ total consumptions. Then, multi-UAV reconnaissance task allocation is formulated as an extended Multiple Dubins Travelling Salesmen Problem (MDTSP), where visit paths to the heterogeneous targets must meet specific constraints due to the targets’ feature. As a complex combinatorial optimization problem, the dimensions of MDTSP are further increased due to the heterogeneity of targets. To efficiently solve this computationally expensive problem, the Opposition-based Genetic Algorithm using Double-chromosomes Encoding and Multiple Mutation Operators (OGA-DEMMO) is developed to improve the population variety for enhancing the global exploration capability. The simulation results demonstrate that OGA-DEMMO outperforms the ordinary genetic algorithm, ant colony optimization and random search in terms of optimality of the allocation results, especially for large scale reconnaissance task allocation problems.     

High resolution reconstruction of ISAR images

A 2-D high-resolution spectral analysis algorithm with application to an inverse synthetic aperture radar (ISAR) imaging, is presented. This algorithm is based on two-dimensional linear prediction using autoregressive (AR) coefficients. Stability is guaranteed by AR process pole adjustment. An ISAR target is modeled for a complex scatterer geometry. Computer simulation results are provided for the high-resolution reconstruction of ISAR images     

A Logarithmic Frequency Allocation Algorithm for Wideband Discrete Frequency Pulse Trains

It is shown that signal waveforms utilizing discrete frequency modulation (DFM) which are generated using a narrowband or frequency shift algorithm have ambiguity sidelobe distortion which is caused by the approximation of time compression by frequency shift. A logarithmic frequency allocation algorithm is presented which couches the signal design problem in terms of band and step ratios, rather than in terms of bandwidth and frequency steps, and is consistent with the wideband formulation of the ambiguity function. The algorithm makes use of the same basic code generating sequence used for narrowband frequency allocation, but the resulting signal will have invariant ambiguity sidelobe positions for any receiver realization in the delay-time compression plane.     

基于遗传算法的测站资源优化分配方法研究

针对测站级测控资源分配中存在的测控任务冲突问题,根据资源分配的一般原则及最优准则,建立资源分配优化模型,利用遗传算法给出该模型的求解方法及步骤。通过实例分析,证明该方法用于解决资源分配问题行之有效。     

复合式旋翼飞行器多目标控制分配策略

针对复合式旋翼飞行器操纵冗余多模式切换控制问题,提出一种基于赋权多目标混合优化的控制分配策略。该策略根据复合式旋翼飞行器过渡模式舵面操纵特性,建立飞行器带约束过渡过程控制分配模型;设计混合多目标优化性能指标评价函数,有效处理操纵量控制受限、交叉强耦合及非线性特性,并减少舵面耗能;采用改进的粒子群优化算法动态更新操纵量及控制通道的权系数矩阵,提高控制面操纵效率,加快优化搜索速度,快速求解过渡过程多目标控制分配变量。该策略实现复合式旋翼飞行器模式切换过渡过程实时有效地操纵量控制分配,保证飞行器快速准确跟踪控制指令的能力。同时,通过多目标控制分配策略,飞行控制系统不需要增加额外的模式切换控制器,降低系统设计难度,提高安全性。