基于分区间强化学习的集群导弹快速任务分配EI北大核心CSCD

针对集群导弹在线任务分配面临的环境不确定、耗时过长等问题,本文研究了一种基于分区间强化学习的集群导弹快速任务分配算法.首先,建立集群导弹的综合攻防性能模型,并将存在环境不确定性的集群导弹任务分配问题表述为马尔可夫决策过程.其次,针对该过程采用分区间强化学习,通过将搜索空间划分成若干个子区间,降低搜索维度,加快算法的收敛过程,并通过理论证明给出了最优区间划分依据.最后,通过3组仿真实验,分别从收敛速度、不确定条件下的寻优能力以及导弹和目标数量可变情况下的决策能力3个方面,验证了所提算法的快速性和优化性能.     

蚁群优化算法在求解随机组合优化问题中的应用综述*

不确定条件下的优化问题更贴近真实世界环境,因而日益受到广泛关注。综述了蚁群优化在求解一组不确定条件下的组合优化问题,即随机组合优化问题方面的应用。首先介绍了不确定条件下组合优化问题的概念分类模型,给出了随机组合优化问题的一般定义;然后指出了其与求解传统确定性组合优化问题的不同之处,即目标函数的计算存在不确定性,并详细论述了目前解决方法的进展;最后分析了该领域值得重点关注的几个研究方向,并对其未来发展进行了展望。     

大规模分布式资源搜索技术研究进展*

针对分布式资源搜索技术及其分类的特点,分别从基于网格的搜索技术的穷举式、集中式、路由式,以及基于P2P系统的搜索技术的集中式、全分布式非结构化、混合式、全分布式结构化等几个方面,对当前研究的分布式资源搜索技术进行了归纳总结,并且对该研究领域需要解决的问题进行了总结,对进一步研究的方向进行了展望。     

一种分数阶修正比例导引律

针对导弹的机动目标追踪问题,本文提出了一种基于分数阶微积分的修正比例导引律(modified proportional navigation law based on fractional calculus,FO-PPN).通过选取Lyapunov-like函数,从理论上证明了FO-PPN制导下的理想导弹能够击中带有时变法向加速度的机动目标.仿真结果表明,修正后的比例导引律在保持原有追踪性能的同时,还可以有效改善传统比例导引下导弹在命中点处法向过载突变的不足.     

An SLP filter algorithm for probabilistic analytical target cascading

Decision-making under uncertainty is particularly challenging in the case of multidisciplinary, multilevel system optimization problems. Subsystem interactions cause strong couplings, which may be amplified by uncertainty. Thus, effective coordination strategies can be particularly beneficial. Analytical target cascading (ATC) is a deterministic optimization method for multilevel hierarchical system design that has been extended to probabilistic formulations. Solving the probabilistic optimization problem requires propagation of uncertainty, namely, evaluating or estimating the output distributions, a task that is computationally expensive for highly nonlinear functions. This article presents the use of sequential linear programming (SLP) for probabilistic ATC. By linearizing and solving a problem successively, the strategy takes advantage of the simplicity and ease of uncertainty propagation for a linear system under the assumption that inputs are normally distributed or can be transformed into equivalent normal distributions. A suspension strategy, developed for a deterministic SLP coordination strategy for ATC, is applied to reduce computational cost by suspending the analyses of subsystems that do not need considerable redesign. The accuracy and effectiveness of the proposed coordination strategy is demonstrated with several numerical examples.     

Coalition-hierarchical game under uncertainty conditions

An optimization problem in a coalition-hierarchical game under uncertainty conditions is formulated. In the game, information assumptions are that the player of the high hierarchical level (controlling Center) and each low-level coalition estimates uncertainty in its own way. The Center constructs its strategy from the maximum condition for its own payoff function and its minimum in uncertainty. The relationships between coalitions are built upon the guaranteeing absolute active equilibrium understood in the sense of providing the players with guaranteed payoff under the actual uncertainty. The guaranteed uncertainty is obtained with the help of Slater principle. The total equilibrium in the game is called CH-equilibrium. For a quadratic game version, sufficient optimality conditions are obtained. A numerical procedure for solving the game is described and an example is given.     

空间绳系机器人的多次推力逼近策略研究

针对空间绳系机器人近距离逼近问题,提出了基于时间最优的一般N次推力机动策略及算法,由演化得到的等时间N次机动模式和等速度增量N次推力机动模式,能够将多次推力机动策略中复杂的多时间变量求值问题转化为有非线性约束的最小机动时间问题,并利用基于罚函数法的遗传算法进行求解,对空间绳系机器人的近距离逼近问题进行了仿真。仿真表明:一般N次推力机动策略及算法能够很好解决此类多时间变量求值问题;且不需要进行初始速度脉冲修正,相对于基于"时间倒流法"的N次推力策略和连续推力具有较小的机动时间,并在某些情形下相较于后两者以及双冲量机动具有较少的能耗或者较小的最大视界角,为绳系机器人推动逼近设计提供了依据。     

A game theoretic approach to optimal control in the presence of uncertainty

The optimal control problem, in the presence of uncertainty in the plant, is formulated as a game between the uncertainty and the control variables. This approach gives an optimal control strategy which is effective even under the "worst" conditions of uncertainty. The optimal control of a second-order plant with uncertainty in frequency brings out several interesting features. In particular, the existence of a barrier demarcating the controllable and uncontrollable regions in the phase plane is revealed, which is absent in the corresponding one-sided optimization.     

Integrated aircraft control for rendezvous of aircraft-launched missiles with radiating targets

This paper solves the problem of rendezvous of an aircraft-launched missile with a maneuvering radiating target so as to maximize the launch range (agreed launch conditions). The information maneuver (vertical, horizontal, and spatial) of the aircraft that maximally improves the accuracy of radiating target tracking and is executed before the aircraft meets the agreed launch conditions is determined. An algorithm for determining the current demands for aircraft control with respect to bank angle and load factor is developed to solve the problem of rendezvous with each tracked target. The integrated aircraft control is based on choosing a priority target at the current time and supplying the current control demand in terms of the desired bank angle and load factor.     

Effects of disciplinary uncertainty on multi-objective optimization in aircraft conceptual design

The problem of aircraft sizing during conceptual design is characterized by limited knowledge and high uncertainty. Uncertainty is especially prevalent in the early-phase estimates of design characteristics from the aerodynamics, propulsion, and weights discipline areas. In order to develop effective conceptual designs that are robust and fare well in later program phases, trade space exploration and optimization should favor design choices that are both ??balanced?? in terms of the multiple performance objectives and resistant to system-level losses due to missed targets for disciplinary metrics. This paper presents a study of the effects of uncertainty in multi-objective optimization in aircraft conceptual design by demonstrating the changes in the Pareto frontiers due to variability in disciplinary metrics and differences in the formulation of the probabilistic optimization problem. By analyzing these frontiers, the decision maker can judge the tradeoff between expected performance and resistance to uncertainty and can identify regions of the design space where this tradeoff is either favorable or high risk, resulting in improved decision making. To enable this analysis, multi-objective optimization and visualization techniques are tailored to the problem by incorporating Monte Carlo methods and other mechanisms of quantitatively capturing the effects of uncertainty.     

Robust topology optimization of structures with uncertainties in stiffness – Application to truss structures

A computational strategy is proposed for robust structural topology optimization in the presence of uncertainties with known second order statistics. The strategy combines deterministic topology optimization techniques with a perturbation method for the quantification of uncertainties associated with structural stiffness, such as uncertain material properties and/or structure geometry. The use of perturbation transforms the problem of topology optimization under uncertainty to an augmented deterministic topology optimization problem. This in turn leads to significant computational savings when compared with Monte Carlo-based optimization algorithms which involve multiple formations and inversions of the global stiffness matrix. Examples from truss structures are presented to show the importance of including the effect of controlling the variability in the final design. It is also shown that results obtained from the proposed method are in excellent agreement with those obtained from a Monte Carlo-based optimization algorithm.     

A hyper-heuristic approach to aircraft structural design optimization

The conceptual design of an aircraft is a challenging problem in which optimization can be of great importance to the quality of design generated. Mass optimization of the structural design of an aircraft aims to produce an airframe of minimal mass whilst maintaining satisfactory strength under various loading conditions due to flight and ground manoeuvres. Hyper-heuristic optimization is an evolving field of research wherein the optimization process is continuously adapted in order to provide greater improvements in the quality of the solution generated. The relative infancy of hyper-heuristic optimization has resulted in limited application within the field of aerospace design. This paper describes a framework for the mass optimization of the structural layout of an aircraft at the conceptual level of design employing a novel hyper-heuristic approach. This hyper-heuristic approach encourages solution space exploration, thus reducing the likelihood of premature convergence, and improves the feasibility of and convergence upon the best solution found. A case study is presented to illustrate the effects of hyper-heuristics on the problem for a large commercial aircraft. Resulting solutions were generated of considerably lighter mass than the baseline aircraft. A further improvement in solution quality was found with the use of the hyper-heuristics compared to that obtained without, albeit with a penalty on computation time.     

Optimal design of aircraft structures with damage tolerance requirements

Damage tolerance analysis (DTA) was considered in the global design optimization of an aircraft wing structure. Residual strength and fatigue life requirements, based on the damage tolerance philosophy, were investigated as new design constraints. The global/local finite element approach allowed local fatigue requirements to be considered in the global design optimization. AFGROW fatigue crack growth analysis provided a new strength criterion for satisfying damage tolerance requirements within a global optimization environment. Initial research with the ASTROS program used this damage tolerance constraint to optimize cracked skin panels on the lower wing of a fighter/attack aircraft. For an aerodynamic and structural model of this type of aircraft, ASTROS simulated symmetric and asymmetric maneuvers during the optimization. Symmetric maneuvers, without underwing stores, produced the highest stresses and drove the optimization of the inboard lower wing skin. Asymmetric maneuvers, with underwing stores, affected the optimum thickness of the outboard hard points. Subsequent design optimizations included DTA and von Mises stress constraints simultaneously. In the configuration with no stores, the optimization was driven by the DTA constraint and, therefore, DTA requirements can have an active role to play in preliminary aircraft design.     

Differential evolution based 3-D guidance law for a realistic interceptor model

This paper presents a novel, soft computing based solution to a complex optimal control or dynamic optimization problem that requires the solution to be available in real-time. The complexities in this problem of optimal guidance of interceptors launched with high initial heading errors include the more involved physics of a three dimensional missile–target engagement, and those posed by the assumption of a realistic dynamic model such as time-varying missile speed, thrust, drag and mass, besides gravity, and upper bound on the lateral acceleration. The classic, pure proportional navigation law is augmented with a polynomial function of the heading error, and the values of the coefficients of the polynomial are determined using differential evolution (DE). The performance of the proposed DE enhanced guidance law is compared against the existing conventional laws in the literature, on the criteria of time and energy optimality, peak lateral acceleration demanded, terminal speed and robustness to unanticipated target maneuvers, to illustrate the superiority of the proposed law.     

Variations in the application of a budget of uncertainty optimization approach

It is possible to consider uncertainty simultaneously with the design process. In fact, a Budget of Uncertainty(BoU) can be determined alongside the design solution, allowing the determination of uncertainty intervals for selected design variables and problem parameters. This paper presents a new strategy for optimization under uncertainty which provides for this simultaneous design and uncertainty determination. To test the theory, a simple Taylor series expansion strategy is used to propagate uncertainty in a design problem’s objectives and constraints and a new BoU design algorithm is formulated. Due to the need for competing objectives, nominal performance and robust design, the new formulation is a multiobjective problem with primary and secondary weights to allow for lexicographic weights of uncertain parameters and variation between optimal and robust solutions. This paper compares and contrasts three different Goal Programming techniques as solutions to the multiobjective problem. Within the paper, the term Budget of Uncertainty (BoU) is used to describe the fundamental idea of uncertainty allocation across design variables and problem parameters as well as for a shorthand to describe the presented formulation. An engineering design problem, that of a helical spring, is presented to further illustrate the new method, and an uncertainty budget is considered which trades uncertainty in coil diameter against uncertainty in wire diameter.     

Multicriteria optimization of technological processes under uncertainty conditions

The multicriteria optimization problem under uncertainty conditions is considered, which arises at the design stage of a technological process. The uncertainty stems from the inaccuracy of mathematical models and a certain uncertainty of the conditions in which the process will be implemented. The generalization is treated of well known multicriteria optimization methods in the case of the uncertainty accounting. Here, account will be taken of the possibility of the use of control variables at the operation stage to compensate for the uncertainty effect.     

Robust stabilizing inventory control in supply networks under uncertainty of external demand and supply time-delays

A synthesis problem of the robust inventory control strategy for supply networks under uncertain bounded demand and uncertainty of supply time-delays and with presence of asymmetric constraints on states and controls is considered. Control is constructed based on the invariant ellipsoid method as a linear non-stationary feedback with respect to deviation of the current stock level from the chosen safety level. Solvability conditions of the synthesis problem are stated in the form of linear matrix inequalities and are reduced to solving semi-definite programming and one-dimensional convex optimization problems. The resulting control strategy ensures suppression of influence of intervalbounded external demand and robust stabilization of the closed-loop system. Control of the supply network of three nodes is considered as an example.     

某型飞机的毁伤计算与仿真系统研究

在空战对抗中,关于我方飞机在敌方导弹威胁下的损毁状态以及战伤情况的统计分析对指导我军战时战伤抢修和战场备件筹措具有重大意义,而建立飞机在一定威胁下的毁伤模型并通过多次仿真计算进行分析是进行飞机战伤抢修研究的一个重要手段.在分析破片杀伤型空空导弹的杀伤机理并建立我军某型主战飞机的易损性模型和详细结构模型的基础上,重点介绍了毁伤种类及相应计算模型的建立,通过仿真实现,得到了一定弹目交会条件下飞机的损毁概率以及战伤部位清单,为后期战伤模式分析、战伤评估和抢修方案的制定提供了重要依据.     

基于微分观测器的飞行模拟转台伺服系统非线性控制

飞行模拟器转台伺服系统是导弹飞行的重要模拟设备,用于获取实验数据。针对飞行模拟转台伺服系统在跟踪控制过程中存在参数不确定性、非线性摩擦等不确定性问题,提出了一种基于微分观测器的飞行模拟转台伺服系统非线性控制方法;考虑系统在跟踪控制过程中存在不确定性问题,设计了微分观测器来估计复合不确定扰动;设计非线性控制器来控制飞行模拟转台伺服系统,使得系统可以收敛到期望位置转角信号;通过李雅普洛夫稳定性证明控制器作用在系统条件下的鲁棒性;通过MATLAB/Simulink仿真试验平台验证了文中提出的控制策略能够使系统有效跟踪期望位置转角,具有一定工程应用价值;     

A Stochastic Programming Strategy in Microgrid Cyber Physical Energy System for Energy Optimal Operation

This paper focuses on the energy optimal operation problem of microgrids (MGs) under stochastic environment. The deterministic method of MGs operation is often uneconomical because it fails to consider the high randomness of unconventional energy resources. Therefore, it is necessary to develop a novel operation approach combining the uncertainty in the physical world with modeling strategy in the cyber system. This paper proposes an energy scheduling optimization strategy based on stochastic programming model by considering the uncertainty in MGs. The goal is to minimize the expected operation cost of MGs. The uncertainties are modeled based on autoregressive moving average (ARMA) model to expose the effects of physical world on cyber world. Through the comparison of the simulation results with deterministic method, it is shown that the effectiveness and robustness of proposed stochastic energy scheduling optimization strategy for MGs are valid.