基于伪谱法的翼伞系统归航轨迹容错设计

针对翼伞系统在归航过程中,控制电机工作异常致使控制性能发生变化,无法按原有规划轨迹到达目标点的问题,提出一种基于Gauss伪谱法的归航轨迹容错设计方法.首先根据翼伞系统控制特性的不同,分别建立了正常和单电机异常工作状态下的质点模型,并根据伞形参数确定了两种工作状态下的约束条件和目标函数;其次,利用Gauss伪谱法分别对两种工作状态下轨迹规划的最优控制问题求解,获得翼伞系统不同状态下的最优飞行轨迹.仿真结果表明,在约束情况下,翼伞系统无论在正常和单电机异常工作时都可以顺利到达目标点,获得高精度的飞行轨迹.     

翼伞自主归航控制系统设计

设计一种用于翼伞空投系统上的控制系统。采用ARM控制器作为核心控制元件,为操纵绳电机设计驱动电路。针对经典PID控制过程中存在的缺陷,结合翼伞系统飞行特点,提出基于LADRC 控制理论设计控制算法,并利用MATLAB仿真软件对该算法的正确性进行验证,以及介绍控制系统的软件工作流程。     

翼伞系统动力学建模与分段归航设计

翼伞回收系统具有精确、定点、无损等特点,已成为当前回收领域的一个研究热点,首先建立了含附加质量的翼伞系统的六自由度动力学模型,然后将其带入翼伞系统的动力学方程中得到了运动轨迹以及三个姿态角随不同操纵方式变化的时间曲线,对翼伞系统的基本运动特性进行了分析.其次采用分段归航策略对翼伞系统的轨迹规划进行了设计,并利用遗传算法...     

翼伞系统在较大风场中的归航控制

翼伞系统是一类特殊的柔翼飞行器,由于其飞行速度较低,容易受到风场的影响.针对翼伞系统在较大风场中难以准确跟踪归航轨迹、实现精确着陆,因此将风场中平均风的影响在轨迹规划中予以考虑,采用一种改进的粒子群算法(particle swarm optimization,PSO)优化分段归航轨迹;将紊流的影响作为外界的干扰,由线性自抗扰控制器(linear active disturbance rejection controller,LADRC)进行修正.仿真结果表明,该归航控制方法对提高翼伞系统在较大风场环境下的抗风性能和归航精度有重要意义.     

未知风场条件下翼伞系统自适应归航算法研究

针对翼伞系统在未知风场条件和障碍区域内难以保证着陆精度的问题,提出了翼伞系统航迹在线规划方法.在建立翼伞系统六自由度(6DOF)动力学模型的基础上,改进了经典分段归航控制方案,设计了自适应航迹规划算法,得到了低能耗、短航时的航迹规划结果.设计了PD控制器以仿真验证算法的有效性,仿真结果表明:提出的算法满足精确空投任务要求.     

基于Serret-Frenet坐标系的翼伞系统轨迹跟踪控制

基于翼伞系统归航轨迹的特点,采用Serret-Frenet坐标系表示距离“平衡”轨迹的偏差,得到线性时不变的误差运动方程．由此误差方程可以得到控制量与轨迹偏差之间的传递函数,直接进行轨迹控制器的设计．对于控制器输入所需的轨迹偏差和偏差率可以采用解析的方法近似求解,极大地简化了计算．整个设计流程简单明了,采用PD控制器进行轨迹跟踪的算例表明此套方法的有效性．     

翼伞空投机器人系统的六自由度仿真

以灾难环境下翼伞空投机器人系统为背景,建立了翼伞系统六自由度的简化运动模型,针对预选的某伞型对该翼伞系统进行了动力学仿真.设置了常值侧风的仿真环境,分析了风对系统的影响,得出了翼伞的转弯、雀降等重要性能的参数,提供了验证控制算法的仿真基础,为设计实际控制系统提供理论依据.     

基于风洞试验的组合翼伞气动特性研究

以组合翼伞气动特性为研究对象,通过开展8m×6m直流开口式风洞试验对组合翼伞气动特性开展试验研究.对组合翼伞进行风洞试验气动特性研究,通过光测系统获取稳定状态下组合翼伞实际迎角,六分量天平测试系统获取气动力(矩),得到不同迎角下组合翼伞的气动力(矩)数据.通过本次试验探索柔性翼伞风洞试验方法,积累组合翼伞真实试验气动数...     

基于实时多任务操作系统的动力翼伞系统设计

为了确保动力翼伞控制器的多功能实现及其系统稳定运行，设计了一种基于实时操作系统μC/OS-III的动力翼伞控制系统。系统基于Cortex-M4内核的微控制器STM32F407IGT6和Cortex-M3内核的微控制器STM32F103VCT6硬件平台，采用μC/OS-III系统实现了飞行模式选择、GPS采集、控制量计算、地面站交互、舵机位置采集、横向控制、纵向控制和系统信息读写等任务。详细介绍了系统总体构成以及软硬件实现方法。实验表明，采用μC/OS-III对动力翼伞系统进行实时多任务管理，可以最大化利用CPU资源，提高系统的运行效率，增强系统的稳定性和实时性。     

Optimal Matching Control of a Low Energy Charged Particle Beam in Particle Accelerators

Particle accelerators are devices used for research in scientific problems such as high energy and nuclear physics. In a particle accelerator, the shape of particle beam envelope is changed dynamically along the forward direction. Thus, this reference direction can be considered as an auxiliary "time" beam axis. In this paper, the optimal beam matching control problem for a low energy transport system in a charged particle accelerator is considered. The beam matching procedure is formulated as a finite "time" dynamic optimization problem, in which the Kapchinsky-Vladimirsky (K-V) coupled envelope equations model beam dynamics. The aim is to drive any arbitrary initial beam state to a prescribed target state, as well as to track reference trajectory as closely as possible, through the control of the lens focusing strengths in the beam matching channel. We first apply the control parameterization method to optimize lens focusing strengths, and then combine this with the time-scaling transformation technique to further optimize the drift and lens length in the beam matching channel. The exact gradients of the cost function with respect to the decision parameters are computed explicitly through the state sensitivity-based analysis method. Finally, numerical simulations are illustrated to verify the effectiveness of the proposed approach.      

基于随机灵敏度分析的化工过程裕量设计

对化工过程进行最优设计时,由于过程中参数的不确定性,需要在既满足过程约束又保证经济效益最优的前提下对设计变量增加裕量。本文考虑过程不确定参数的随机分布,结合灵敏度分析,提出一种基于随机灵敏度的化工过程最优裕量设计方法。首先,取不确定参数的标称值,进行化工过程最优设计,得到最优设计点的标称值;其次,假设过程不确定参数服从正态分布,基于灵敏度分析确定约束变量的均值和方差,在保证低概率违反约束的条件下优化求解设计裕量,定量分析不确定参数对设计变量的影响;最后,通过对串联连续搅拌釜式反应器（Continuous stirred tank reactor,CSTR）系统进行仿真实验,说明该裕量设计方法的具体步骤,并得到合理的设计裕量值,验证所提方法的正确性。     

Order-(n+m) direct differentiation determination of design sensitivity for constrained multibody dynamic systems

With the complexity and large dimensionality of many modern multibody dynamic applications, the efficiency of the sensitivity evaluation methods used can greatly impact the overall computation cost and as such can greatly limit the usefulness of the sensitivity information. Most current direct differentiation approaches suffer from prohibitive computational cost, which may be as great as O(n⁴+n²m²+nm³) (for systems with n generalized coordinates and m algebraic constraints). This paper presents a concise and computationally efficient sensitivity analysis scheme to facilitate such sensitivity calculations. A unique feature of this scheme is its use of recursive procedures to directly embed the algebraic constraint relations in forming and simultaneously solving a minimal set of equations. This results in far fewer operations than more traditional, or so-called O(n), counterparts. The algorithm determines the derivatives of generalized accelerations in O(n+m) operations overall. The resulting equations are exact to integration accuracy and enforce constraints exactly at both the velocity and acceleration levels.     

Multi-objective cellular particle swarm optimization for wellbore trajectory design

Wellbore trajectory design is a determinant issue in drilling engineering. The criteria to evaluate a wellbore trajectory are summarized as the total trajectory length, the torque and the well profile energy in this paper. By minimizing the wellbore trajectory length, torque and profile energy simultaneously, it is most likely that a wellbore trajectory designed to arrive at the specific target can be drilled more safely, quickly and cheaply than other potential trajectories. However, these three objectives are often in conflict with each other and related in a highly nonlinear relationship. A multi-objective cellular particle swarm optimization (MOCPSO) with an adaptive neighborhood function is developed in this paper. Then, MOCPSO is applied with the three objective functions to gain a set of Pareto optimal solutions that are beneficial for a less risky and less costly wellbore trajectory design option. Besides, MOCPSO’s performance is compared with multi-objective PSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm-II (NSGA-II). Effect of the proposed neighborhood function is also investigated by making contrasts with the commonly used four neighborhood templates. Moreover, the radius parameter in the adaptive neighborhood function is analyzed to reveal its influence on the optimization performance. It can be inferred that MOCPSO is statistically superior to both multi-objective PSO, NSGA-II and MOEA/D at the 0.05 level of significance on the wellbore trajectory design problem. And the proposed adaptive neighborhood function performs either comparable or better as compared to the other commonly used neighborhood functions. According to the parameter analysis, it can be concluded that the MOCPSO approach with radius value of 1or 1.5 has a better statistical performance.     

Feasibility refinement in sequential quadratic programming using parametric sensitivity analysis

In this paper we present results that extend the sequential quadratic programming (SQP) algorithm with an additional feasibility refinement based on parametric sensitivity derivatives. The refinement is applicable without restriction on the problem dimensions in sparse SQP solvers. Parametric sensitivity analysis is a tool for post optimality analysis of the solution of a nonlinear optimization problem. For the refinement approach we apply this technique on the quadratic subproblems in order to improve the overall algorithm. The sensitivity derivatives required for this approach can be computed without noticeable computational effort as the system of linear equations to be solved coincides with the system already solved for the search direction computation. For similar algorithms in the context of post optimality analysis a linear rate of convergence has been proven and therefore an extrapolation method is applied to speed up the process. The presented algorithm has been integrated into the nonlinear program (NLP) solver WORHP and we perform a numerical study to evaluate different termination criteria for the proposed algorithm. Furthermore, numerical results on the CUTEst test set are shown.     

On the enhancement of computation and exploration of discretization approaches for meshless shape design sensitivity analysis

A new implementation of Reproducing Kernel Particle Method (RKPM) is proposed to enhance the process of shape design sensitivity analysis (DSA). The acceleration process is accomplished by expressing RKPM shape functions and their derivatives explicitly in terms of kernel function moments. In addition, two different discretization approaches are explored elaborately, which emanate from discretizing design sensitivity equation using the direct differentiation method. Comparison of these two approaches is made, and the equivalence of these two superficially different approaches is demonstrated through two elastostatics problems. The effectiveness of the enhanced RKPM is also verified by comparison of consumption of computer time between the classical method and the improved method.     

温枪弹道中光电靶灵敏度控制系统优化设计

传统控制系统存在灵敏度控制效果差、性能不稳定等问题,无法满足高效控制标准,提出温枪弹道中光电靶灵敏度控制系统优化设计。根据实际条件和干扰元素设计原理,从温枪弹道中光电靶灵敏度控制角度出发,将光信号转换为微弱电流信号,对微弱电流信号进行转换,设计光电靶灵敏控制器和光电转换电路,完成系统硬件部分设计;选择基于优化环境下的软件程序开发板来实现软件编程,完成光电靶灵敏度控制系统的优化。实验验证结果可知,该系统灵敏度控制效果强、性能稳定。     

Optimal uncertainty reduction for multi-disciplinary multi-output systems using sensitivity analysis

We present a sensitivity analysis based uncertainty reduction approach, called Multi-dIsciplinary Multi-Output Sensitivity Analysis (MIMOSA), for the analysis model of a multi-disciplinary engineering system decomposed into multiple subsystems with each subsystem analysis having multiple inputs with reducible uncertainty and multiple outputs. MIMOSA can determine: (1) the sensitivity of system and subsystem outputs to input uncertainties at both system and subsystem levels, (2) the sensitivity of the system outputs to the variation from subsystem outputs, and (3) the optimal “investment” required to reduce uncertainty in inputs in order to obtain a maximum reduction in output variations at both the system and subsystem levels. A numerical and an engineering example with two and three subsystems, respectively, have been used to demonstrate the applicability of the MIMOSA approach.     

Multi-objective robust optimization using a sensitivity region concept

In multi-objective design optimization, it is quite desirable to obtain solutions that are multi-objectively optimum and insensitive to uncontrollable (noisy) parameter variations. We call such solutions robust Pareto solutions. In this paper we present a method to measure the multi-objective sensitivity of a design alternative, and an approach to use such a measure to obtain multi-objectively robust Pareto optimum solutions. Our sensitivity measure does not require a presumed probability distribution of uncontrollable parameters and does not utilize gradient information; therefore, it is applicable to multi-objective optimization problems that have non-differentiable and/or discontinuous objective functions, and also to problems with large parameter variations. As a demonstration, we apply our robust optimization method to an engineering example, the design of a vibrating platform. We show that the solutions obtained for this example are indeed robust.