气动-引力辅助轨道机动的拟谱法反馈制导律

气动-引力辅助轨道机动相对于独立的引力辅助轨道机动而言能实现更大更灵活的速度偏转角同时减少燃料消耗。利用拟谱法设计出开环最优轨迹。对于飞行过程中的制导问题,根据拟谱法实现速度快结果精确的优点,将飞行时间按照等距及变间距分段,对每个独立时段采用实时在线轨迹生成的方法得到这一时间范围的最优轨迹,通过不断的轨迹更新实现飞行中对参数扰动等因素的抑制作用,结果显示采用变间距节点对干扰抑制效果有所增强。同时采用预留时间裕度的方式,可以较好地解决制导过程中在线最优化轨迹生成所需少量计算时间导致的制导偏差问题。     

一类参数不确定性系统的鲁棒控制

讨论了带有分母多项式摄动的参数不确定性系统的鲁棒镇定问题,给出了这类参数不确定性系统存在鲁棒控制器的一个充分条件,并且当这个条件被满足时,得到了部分鲁棒控制器的一个参数化公式.     

考虑导弹速度时变的角度约束最优中制导律

以导弹逆轨拦截高速运动目标为背景,本文运用间接高斯伪谱法设计带攻击角度约束的最优中制导律.通过零化弹目相对法向速度,将攻击角度约束转化为视线角约束.考虑导弹速度时变的情况,建立带角度约束的制导方程.根据极小值原理推导最优中制导律的解析表达式,运用高斯伪谱法对最优中制导律进行离散化,把微分方程转化为代数方程,避免了求解Riccati方程.该方法不需要预先知道导弹未来的速度信息,计算量小,具有较好的实时性.仿真结果表明该中制导律可以满足逆轨拦截对弹目交会角的约束,且中制导末端的过载较小.     

不确定性系统族的鲁棒稳定裕度

本文给出了不确定性系统族的鲁棒稳定裕度定义以及计算方法。这些结果具有非常一般化的意义。     

混合不确定性系统的鲁棒镇定及性能设计

采用双回路控制方法讨论具有混合不确定性系统的鲁棒镇定及性能设计问题，其内环控制器可改善对象特性，从而使整个双回路闭环系统具有鲁棒稳定性．     

混合不确定性系统的鲁棒镇定及性能设计

采用双回路控制方法讨论具有混合不确定性系统的鲁棒镇定及性能设计问题,其内环控制器可改善对象特性,从而使整个双回路闭环系统具有鲁棒稳定性.     

含参数不确定性系统的鲁棒性能设计

讨论含有参数不确定性系统的鲁棒性能设计问题。首先将该问题转化为一个多项式簇的鲁棒稳定性问题，从而得到鲁棒性能控制器应满足的频域充要条件，然后通过一个双回路控制器的设计将鲁棒镇定和鲁棒性能设计分两步进行，使设计条理化、简单化。     

反临拦截弹中制导弹道在线优化设计

为了实现反临拦截弹中制导段的弹道在线优化,基于邻域最优控制(NOC)理论和改进间接高斯伪谱法(GPM)设计一种弹道在线生成算法.根据临近空间拦截作战特点设计弹道优化模型,基于NOC理论推导关于修正状态量和协态量的两点边值问题.对横截条件进行二阶变分,通过逆向递推将终端最优协态修正值表示成跟踪偏差和终端约束修正量的表达式.将拦截弹的当前状态量作为初始约束值,终端最优协态修正值作为干扰量,利用标称弹道数据,基于改进间接GPM求得控制修正量.仿真结果表明,所提出的方法可以快速生成一条修正弹道,且具有较高的终端修正精度.     

基于定量反馈理论的气动调节阀控制系统设计

为了使气动调节阀控制系统满足工业过程对于模型不确定性的鲁棒性要求,设计了1种基于定量反馈理论(QFT)的气动调节阀控制系统。基于QFT的控制器是1种二自由度控制器结构,由前置滤波器和反馈控制器组成。QFT对于控制对象的不确定性具有很强的鲁棒性,能够较好地避免气动调节阀控制系统在工业过程中受到的干扰和模型不确定性影响。通过对气动调节阀控制系统进行仿真分析,验证了基于QFT的控制方法能够较好地避免该系统的不确定性,使得该系统具有较强的鲁棒性,满足相应的性能指标要求。与传统基于常规比例积分微分(PID)控制方法的气动调节阀控制系统相比,基于QFT控制方法的气动调节阀控制系统更加适用于工业过程。     

Neighbouring optimal guidance for aeroassisted non-coplanar orbital transfer

The fuel-optimal control problem in aeroassisted non-coplanar orbital transfer is addressed. The equations of motion for the atmospheric manoeuvre are non-linear and the optimal )nominal) trajectory and control are obtained. In order to follow the nominal trajectory under actual conditions, a neighbouring optimum guidance scheme is designed using linear quadratic regulator theory for onboard real-time implementation. One of the state variables is used as the independent variable in preference to the time. The weighting matrices in the performance index are chosen by a combination of a heuristic method and an optimal modal approach. The necessary feedback control law is obtained in order to minimize the deviations from the nominal conditons. The results are presented to a typical aeroassisted non-coplanar orbital transfer problem.     

考虑灵敏度区域的多目标鲁棒性优化算法

随机变量导致工程问题具有不确定性.设计者希望设计方案不仅能满足目标性能最优,而且希望目标性能受不确定性的影响在可接受范围之内.对此,本文提出了考虑灵敏度区域的多目标鲁棒性优化方法(multi-objective robust optimization based on sensitivity region,SR–MORO).SR–MORO可以用来解决设计变量存在不确定性时目标鲁棒性优化设计问题.该方法假定不确定性变量属于区间变量,并不需要知道随机变量的概率分布.SR–MORO采用非梯度优化方法,所以,它可以解决目标函数和约束条件不连续的情况.当参数变化幅度大,超过目标函数线性变化范围,该方法也同样适用.最后,通过实例验证了本方法的适用性.     

A new uncertainty propagation method considering multimodal probability density functions

Structural and Multidisciplinary Optimization - In practical engineering applications, random variables may follow multimodal distributions with multiple modes in the probability density functions,...     

A modified Benders decomposition method for efficient robust optimization under interval uncertainty

The goal of robust optimization problems is to find an optimal solution that is minimally sensitive to uncertain factors. Uncertain factors can include inputs to the problem such as parameters, decision variables, or both. Given any combination of possible uncertain factors, a solution is said to be robust if it is feasible and the variation in its objective function value is acceptable within a given user-specified range. Previous approaches for general nonlinear robust optimization problems under interval uncertainty involve nested optimization and are not computationally tractable. The overall objective in this paper is to develop an efficient robust optimization method that is scalable and does not contain nested optimization. The proposed method is applied to a variety of numerical and engineering examples to test its applicability. Current results show that the approach is able to numerically obtain a locally optimal robust solution to problems with quasi-convex constraints (≤ type) and an approximate locally optimal robust solution to general nonlinear optimization problems.     

A robust one-class transfer learning method with uncertain data

Knowledge and Information Systems - One-class classification aims at constructing a distinctive classifier based on one class of examples. Most of the existing one-class classification methods are...     

A robust and high precision optimal explicit guidance scheme for solid motor propelled launch vehicles with thrust and drag uncertainty

A new nonlinear optimal and explicit guidance law is presented in this paper for launch vehicles propelled by solid motors. It can ensure very high terminal precision despite not having the exact knowledge of the thrust–time curve apriori. This was motivated from using it for a carrier launch vehicle in a hypersonic mission, which demands an extremely narrow terminal accuracy window for the launch vehicle for successful initiation of operation of the hypersonic vehicle. The proposed explicit guidance scheme, which computes the optimal guidance command online, ensures the required stringent final conditions with high precision at the injection point. A key feature of the proposed guidance law is an innovative extension of the recently developed model predictive static programming guidance with flexible final time. A penalty function approach is also followed to meet the input and output inequality constraints throughout the vehicle trajectory. In this paper, the guidance law has been successfully validated from nonlinear six degree-of-freedom simulation studies by designing an inner-loop autopilot as well, which enhances confidence of its usefulness significantly. In addition to excellent nominal results, the proposed guidance has been found to have good robustness for perturbed cases as well.     

A multiobjective integrated multiproject scheduling and multiskilled workforce assignment model considering learning effect under uncertainty

Today, organizations try to decline academically expenses using humans and resources in addition to rising managers and operators' satisfaction. Meantime, a very important step in the process of decision is the assignment of human resources, particularly in connection with research and development (R&D) projects in which the system is highly dependent on the capabilities of human resources. In this study, we tried all the assumptions that come true in the real world, considered a model for applied R&D projects to reduce costs and increase the efficiency of projects. Therefore, an integrated multiproject scheduling and multiskill human resource assignment model under uncertainty has developed for R&D projects. Furthermore, it is assumed that the activity processing time is related to human resources assignment that means the learning effect is considered. To demonstrate the proposed model efficiency, the various dimensions instance problem was solved accurately and efficiently in GAMS software, and the results have been reported. In addition, the proposed model is validated through the input parameter sensitivity analysis. The results indicate a suitable performance of the proposed fuzzy mathematical programming model is due to the complexity of the problem.     

考虑偏好不确定的合作性单元制造优化研究

企业为加速产品的迭代更新,可采取合作生产及研发制造一体化的战略。该研究在产品设计方面考虑顾客偏好的动态变化和竞争产品的影响,在生产方面引入单元化制造资源合作共享思想。通过建立考虑顾客偏好不确定的合作性单元制造模型,以最大化单位成本所带来的顾客效用为目标,提升消费者福祉。提出了混合候鸟优化算法(HMBO)进行求解,并将它与经典候鸟优化算法(MBO)和模拟退火算法(SA)进行了比较。算例及大量数值实验验证了该方法的正确性和合理性,结果表明在相同运行时间内HMBO优于MBO和SA。     

Decentralized robust control for multiconnected objects with structural uncertainty

We solve the problem of constructing a decentralized robust regulating system for a multiconnected control object that provides for compensating interconnections in local subsystems and also parametric and external bounded perturbations, compensating with precision δ if one does not measure the derivatives of the local subsystems output vector and in full if the derivatives are measured.     

Sonar mapping of a mobile robot considering position uncertainty

This paper suggests a new sonar mapping method considering the position uncertainty of a mobile robot. Sonar mapping is used for recognizing the unknown environment for a mobile robot during navigation. Usually accumulated position error of a mobile robot causes considerable deterioration of the quality of a constructed map. In this paper, therefore, a new Bayesian probability map construction method is proposed, which considers estimation of the position error of a mobile robot. In this method, we applied approximation transformation theory to estimate the position uncertainty of a real mobile robot, and introduced cell ordering uncertainty caused by the position uncertainty of a robot in cell-based map updating. Through simulation we showed the effect of a robot's position uncertainty on the quality of a reconstructed map. Also, the developed methods were implemented on a real mobile robot, AMROYS-II, which was built in our laboratory and shown to be useful enough in a real environment.