Adaptive optimization methodology based on Kriging modeling and a trust region method

Surrogate-Based Optimization (SBO) is becoming increasingly popular since it can remarkably reduce the computational cost for design optimizations based on high-fidelity and expensive numerical analyses. However, for complicated optimization problems with a large design space, many design variables, and strong nonlinearity, SBO converges slowly and shows imperfection in local exploitation. This paper proposes a trust region method within the framework of an SBO process based on the Kriging model. In each refinement cycle, new samples are selected by a certain design of experiment method within a variable design space, which is sequentially updated by the trust region method. A multi-dimensional trust-region radius is proposed to improve the adaptability of the developed methodology. Further, the scale factor and the limit factor of the trust region are studied to evaluate their effects on the optimization process. Thereafter, different SBO methods using error-based exploration, prediction-based exploitation, refinement based on the expected improvement function, a hybrid refinement strategy, and the developed trust-region-based refinement are utilized in four analytical tests. Further, the developed optimization methodology is employed in the drag minimization of an RAE2822 airfoil. Results indicate that it has better robustness and local exploitation capability in comparison with those of other SBO methods.     

燃料电池无人机混合电源动态平衡能量管理策略

飞机电推进的动力系统趋于混合能源形式的发展方向，不同类型的源具有不同的特性，混合能源协调工作的方式可以提高动力系统的性能。本文所研究的飞机电推进系统的能源形式为燃料电池和锂电池所做成的混合能源。针对无人机动力系统工况的特殊性，本文在基于规则的能量管理策略研究基础上，提出了一种基于燃料电池氢气消耗的动态平衡能量管理策略，使燃料电池和辅助电源的能量消耗处于相对平衡的状态，避免了其中一种电源能量先耗尽的情况，可以满足多种工况的变化，提高了混合电源的能量利用率和稳定性，保证了无人机动力系统的可靠性。通过仿真分析结果证明了可行性，最后设计了能量管理系统的硬件并进行了实验验证，通过对实验结果计算分析验证了该能量管理策略的可行性。     

吸气式组合动力高超声速飞行器上升段制导方法研究

针对涡轮/冲压/火箭三组合动力水平起降高超声速飞行器爬升段飞行轨迹设计和制导律设计问题，首先考虑宽速域组合动力发动机多模态特性和高低速气动特性差异，分别开展了涡轮段、引射段、纯冲压段及冲压火箭段的飞行策略研究，提出了不同阶段的飞行攻角剖面构型和火箭流量剖面构型，将无穷维轨迹优化问题转化为有限维参数规划问题，进而完成了组合动力上升段飞行轨迹的优化设计；在此基础上，结合轨迹线性化控制方法，开展了组合动力上升段轨迹跟踪制导律设计研究，给出了保证闭环稳定性和控制品质的制导律参数设计准则，最后通过开展仿真分析说明了提出轨迹设计及制导方法的有效性。     

解流水车间作业排序问题的一种禁止搜索算法

流水车间的作业排序（Ｆｌｏｗｂｓｈｏｐｓｃｈｅｄｕｌｉｎｇ）问题是生产管理与组合优化领域研究的重要课题。禁止搜索（ＴａｂｕＳｅａｒｃｈ）算法对于这类问题具有较强的寻优能力。本文针对流水车间的作业排序问题设计了一种禁止搜索算法的实现形式，通过大量的算例分析表明，该算法具有良好的寻优性与运算效率。     

时变加权LQT设计方法理论推导及应用

利用矩阵运算公式和极值原理,对时变加权性能指标下的线性二次型跟踪器(LQT)迭代方程进行了理论推导。以F-16飞机为例,利用飞行控制经验构建俯仰速率控制系统线性框图。在MATLAB/Simulink环境下,针对两种时变加权性能指标和一种定常加权性能指标,利用多变量优化算法完成了一个状态点的控制律参数设计,并进行了时域和频域对比分析,初步验证了时变加权LQT设计方法的工程可行性和有效性。     

A multi-objective multi-memetic algorithm for network-wide conflict-free 4D flight trajectories planning

Under the demand of strategic air traffic flow management and the concept of trajectory based operations (TBO),the network-wide 4D flight trajectories planning (N4DFTP) problem has been investigated with the purpose of safely and efficiently allocating 4D trajectories (4DTs) (3D position and time) for all the flights in the whole airway network.Considering that the introduction of large-scale 4DTs inevitably increases the problem complexity,an efficient model for strategic level conflict management is developed in this paper.Specifically,a bi-objective N4DFTP problem that aims to minimize both potential conflicts and the trajectory cost is formulated.In consideration of the large-scale,high-complexity,and multi-objective characteristics of the N4DFTP problem,a multi-objective multi-memetic algorithm (MOMMA) that incorporates an evolutionary global search framework together with three problem-specific local search operators is implemented.It is capable of rapidly and effectively allocating 4DTs via rerouting,target time controlling,and flight level changing.Additionally,to balance the ability of exploitation and exploration of the algorithm,a special hybridization scheme is adopted for the integration of local and global search.Empirical studies using real air traffic data in China with different network complexities show that the pro posed MOMMA is effective to solve the N4DFTP problem.The solutions achieved are competitive for elaborate decision support under a TBO environment.     

基于改进鸽群算法的高超声速飞行器轨迹优化

采用基于罚函数思想的约束处理技术改进鸽群智能算法,应用于复杂的带约束的飞行器轨迹优化问题。以高超声速飞行器爬升段轨迹优化为例,建立其包含微分方程约束、路径约束和终端约束的优化数学模型,通过罚函数构造算法的适应值函数并对优化变量添加边界强约束,将改进的鸽群智能(PIO)算法和粒子群(PSO)算法对高超声速飞行器爬升段优化数学模型进行对比仿真分析。仿真结果表明,改进的鸽群智能算法在解决此类复杂带约束优化问题中展现出了更好的优化效率,具有良好的工程应用价值。     

多旋翼无人机飞行控制自动调参技术

目前,多旋翼无人机控制器设计问题中存在着大量的依靠经验的调参工作。为了使调参简单而又可靠,本文基于控制器参数与控制系统性能响应存在的对应关系,提出了自动调参思想。在满足控制器各项性能指标的前提下,利用粒子群算法(Particle swarm optimization,PSO)提炼出优化目标和约束条件。对被控对象进行建模并搭建非线性模型。然后,利用工程实践方法估算出参数范围,并利用粒子群快速优化特点自动寻找在约束条件下符合性能指标的控制器参数。最后,通过Matlab/Simulink对模型进行仿真验证。仿真结果分析表明,PSD可快速准确地对飞行控制进行自动调参。     

大展弦比复合材料机翼气动剪裁设计新方法研究

根据机翼气动载荷和机翼弹性变形之间存在的关系,以机翼的总升力不变和结构强度作为约束条件,提出一种新的气动/结构耦合的刚度设计方法。该方法首先通过数值实验设计研究机翼扭转变形和弯曲变形对机翼气动载荷的影响,并用主成分回归方法构建了机翼变形和气动载荷之间的响应面模型。然后以该模型为基础,构建气动/结构一体化设计模型,此模型仅考虑强度约束和总升力不变的要求,放弃了传统优化设计模型中的挠度和扭转约束。通过2种优化模型的对比,说明应用该方法设计出的机翼结构,重量减轻1.23%,机翼总体扭转变形减小33%,刚度设计更为合理。     

A faster optimization method based on support vector regression for aerodynamic problems

In this paper, a new strategy for optimal design of complex aerodynamic configuration with a reasonable low computational effort is proposed. In order to solve the formulated aerodynamic optimization problem with heavy computation complexity, two steps are taken: (1) a sequential approximation method based on support vector regression (SVR) and hybrid cross validation strategy, is proposed to predict aerodynamic coefficients, and thus approximates the objective function and constraint conditions of the originally formulated optimization problem with given limited sample points; (2) a sequential optimization algorithm is proposed to ensure the obtained optimal solution by solving the approximation optimization problem in step (1) is very close to the optimal solution of the originally formulated optimization problem. In the end, we adopt a complex aerodynamic design problem, that is optimal aerodynamic design of a flight vehicle with grid fins, to demonstrate our proposed optimization methods, and numerical results show that better results can be obtained with a significantly lower computational effort than using classical optimization techniques.