Advanced optimization of gas turbine aero-engine transient performance using linkage-learning genetic algorithm: Part I,building blocks detection and optimization in runway

This paper proposes a Linkage Learning Genetic Algorithm (LLGA) based on the messy Genetic Algorithm (mGA) to optimize the Min-Max fuel controller performance in Gas Turbine Engine (GTE). For this purpose, a GTE fuel controller Simulink model based on the Min-Max selection strategy is firstly built. Then, the objective function that considers both performance indices (response time and fuel consumption) and penalty items (fluctuation, tracking error, overspeed and acceleration/deceleration) is established to quantify the controller performance. Next, the task to optimize the fuel controller is converted to find the optimization gains combination that could minimize the objective function while satisfying constraints and limitations. In order to reduce the optimization time and to avoid trapping in the local optimums, two kinds of building block detection methods including lower fitness value method and bigger fitness value change method are proposed to determine the most important bits which have more contribution on fitness value of the chromosomes. Then the procedures to apply LLGA in controller gains tuning are specified stepwise and the optimization results in runway condition are depicted subsequently. Finally, the comparison is made between the LLGA and the simple GA in GTE controller optimization to confirm the effectiveness of the proposed approach. The results show that the LLGA method can get better solution than simple GA within the same iterations or optimization time. The extension applications of the LLGA method in other flight conditions and the complete flight mission simulation will be carried out in part II.     

一种面向运载火箭自动对接的三维测量场构建方法

旋转激光自动经纬仪系统(R-LATs)是一种基于前方角度交汇的分布式大尺寸测量系统。各激光扫描基站的布局位置直接影响着测量区域内的测量精度和应用性能。从测量区域的测量不确定度量化着手,利用蒙特卡罗法获取各点的测量不确定度,以测量区域的最大不确定度作为主要优化目标,利用差分进化算法自动寻优,更简单有效地实现了面向运载火箭自动对接的R-LATs系统布站优化。最后进行了运载火箭对接测量场的实际应用验证。应用结果表明:该方法能有效优化激光扫描基站布局,提高系统测量精度,满足实际应用需求。     

基于TANA函数的非概率可靠度分析方法

建立一种基于TANA(Two-Point Adaptive Nonlinear Approximation)函数的高效稳定非概率型可靠度分析方法。首先,对随机变量采用凸集合非概率多椭球模型进行描述,在可靠度指标法框架下,采用增强混沌控制(Enhanced Chaos Control,ECC)方法,在逼近最佳设计点时,利用两点自适应非线性逼近的TANA2函数拟合强非线性的功能函数,减少了可靠度分析中功能函数的调用次数,有效提高了算法的效率及稳定性。还对不同非线性程度的功能函数进行了可靠度求解以及优化的算例验证。     

Hierarchical global search for fuel-optimal impulsive transfers between lunar libration orbits

The attention to the periodic orbit in the Earth-Moon restricted three-body system continues to grow due to its special environment and locations. This research investigates the feasibility of constructing fuel-optimal single and multiple impulse transfers between unstable periodic orbits at L₁ and L₂ points. Invariant manifolds, which could provide the appropriate initial trajectories for optimization, are analyzed deeply to enable previously unknown orbit options and potentially to reduce mission cost. A global search strategy based on comparing the orbital state of the unstable and stable manifolds, incorporated with low-thrust techniques, is performed to seek a suitable matching point for maneuver application. Then the sequential quadratic programming (SQP) is adopted to further optimize the velocity increment and obtain the single/multiple impulse optimal transfers. The associated constraint gradients are derived to achieve higher accuracy and rapidity of the algorithm. To highlight the effectivity of the transfer scheme, three-dimensional low-energy transfers between different types and spatial regions of performing single and multiple impulses are explored. The total Delta-V required varies between a few meters per second and tens of meters per second, and the related flight time is about several weeks, mainly depending on the energy of periodic orbits and the invariant manifold structure. The results obtained in this paper can provide a useful reference for the selection of escape and capture site along the manifolds, maneuver magnitude and transfer time.     

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。     

飞机高检维修计划制定的优化策略

针对现有高检计划编排的问题，利用工程管理思路和工具，详细阐述了制定高检计划时的优化策略和步骤。     

结构动力学与颤振约束下无人机机翼优化设计

提出了同时考虑结构动力学约束和颤振约束的优化设计方法，建立了利用规划优化算法求解问题的流程，并将其融入大型CAE软件系统HAJIF2013中，研究了优化问题中目标函数和约束条件的选取方式。以某型无人机机翼颤振模型为算例，利用拉盖尔迭代方法自动计算颤振速度，并对优化算法的有效性进行了验证。研究表明，所提出的优化设计方法有效，实现了整个优化求解流程的自动化，对工程设计具有指导意义。     

基于响应表面法的钛合金零件磨抛加工工艺参数优化

钛合金的化学活性高、热导率低、弹性模量小,是一种典型的难加工材料。随着工业技术的发展,现代工业对钛合金的表面质量提出了更高的要求,因此,对于钛合金磨抛加工这种能够获取高表面质量的加工方法的研究显得非常重要。采用Box-Behnken试验设计方法,进行了工艺参数对表面粗糙度的影响试验,然后基于响应表面法,建立了钛合金磨抛加工表面粗糙度二阶预测模型,并对工艺参数进行了优化。     

Optimal solar sail trajectory approximation with finite Fourier series

The heliocentric transfer of a solar sail-based spacecraft is usually studied from an optimal perspective, by looking for the control law that minimizes the total flight time. The optimal control problem can be solved either with an indirect approach, whose solution is difficult to obtain due to its sensitivity to an initial guess of the costates, or with a direct method, which requires a good estimate of a feasible (guess) trajectory. This work presents a procedure to generate an approximate optimal trajectory through a finite Fourier series. The minimum time problem is solved using a nonlinear programming solver, in which the optimization parameters are the coefficients of the Fourier series and the positions of the spacecraft along the initial and target orbits. Suitable constraints are enforced on the direction and magnitude of the sail propulsive acceleration vector in order to obtain feasible solutions. A comparison with the numerical results from an indirect approach shows that the proposed method provides a good approximation of the optimal trajectory with a small computational effort.     

Multiple-constraint cooperative guidance based on two-stage sequential convex programming

An improved approach is presented in this paper to implement highly constrained cooperative guidance to attack a stationary target. The problem with time-varying Proportional Navigation (PN) gain is first formulated as a nonlinear optimal control problem, which is difficult to solve due to the existence of nonlinear kinematics and nonconvex constraints. After convexification treatments and discretization, the solution to the original problem can be approximately obtained by solving a sequence of Second-Order Cone Programming (SOCP) problems, which can be readily solved by state-of-the-art Interior-Point Methods (IPMs). To mitigate the sensibility of the algorithm on the user-provided initial profile, a Two-Stage Sequential Convex Programming (TSSCP) method is presented in detail. Furthermore, numerical simulations under different mission scenarios are conducted to show the superiority of the proposed method in solving the cooperative guidance problem. The research indicated that the TSSCP method is more tractable and reliable than the traditional methods and has great potential for real-time processing and on-board implementation.