基于约束法线的多环耦合机构自由度分析计算

针对空间多环耦合机构比空间并联机构更加复杂,运动链之间通过运动副作用于耦合构件产生了更多的约束作用,提出了一种适用于多环耦合机构的自由度计算新方法。首先,确定机构中的耦合构件,结合运动副约束法线分布,把机构中各构件之间通过运动副产生的约束,简化成约束点上法线的分布;然后,将耦合机构的自由度分析转化为分析各个耦合构件的约束法线几何关系,根据建立的约束法线几何关系对自由度的判定理论,分析判定耦合构件的自由度性质,并确定各耦合构件的过约束数目;最后,通过机构的连续性确定虚约束数目,代入自由度计算公式,得到多环耦合机构的自由度。结合实例分析可知,新方法不仅可以求得各耦合构件的自由度,还可准确计算耦合机构的自由度。新方法理论依据充分,物理意义明确,可有效应用于耦合机构的自由度分析。

考虑低谷时刻负调峰能力及风电预测区间的多目标机组组合优化研究

大规模风电并入电力系统以后,因其反调峰特性及随机性,对系统发电计划和调度带来严峻挑战。系统常规机组在负荷低谷时刻的可调出力在很大程度上决定了系统的风电接纳能力。因此,为了使系统经济、可靠地运行,提出一种综合考虑系统运行成本以及常规机组低谷时刻负调峰能力的多目标机组组合优化模型,并使用高效易行的归一化法线约束方法求解该多目标问题。为应对风电的随机性,将所得的Pareto解集与风电预测区间结合,对不同的机组组合方案进行评估,最终可得到兼顾系统经济性和可靠性的机组组合方案。最后,基于10机系统及其扩展系统(20机)进行仿真分析,结果验证了所提方法的可行性,能够为决策者提供切合实际的机组组合优化方案。

基于改进萤火虫算法的冷热电联供系统多目标优化调度

冷热电联供系统应用和优化具有重要的意义。所提出的新型冷热电联供系统优化模型考虑了能源利用、环境保护和成本等限制因素。在该模型基础上提出了一种改进萤火虫算法,采用了多群组搜索、无用的内部种群删除和改变步长因子等方法,这能提高搜索速度、寻找最优解。同时提出基于规范法线约束的改进萤火虫算法的双目标帕累托前沿优化算法,该方法通过规范法线约束法转化为两类单目标问题,然后通过改进的萤火虫算法进行单目标搜索。仿真算例采用标准IEEE39节点系统验证了所提算法在解决多目标冷热电联供系统优化问题时的优越性。

ICP算法在隧道点云拼接中应用研究

三维激光隧道点云拼接精度直接影响到隧道变形分析的可靠性。为了获得更好的配准结果,针对经典ICP算法配准效率较低的情况,结合已有的ICP改进算法,提出一种基于靶标中心法线空间夹角的约束条件。该方法利用两站点云同一靶标的中心点的法线夹角作为约束条件剔除误差较大和错误的对应点对,进而提高算法效率。实验结果表明改进后的ICP算法在计算效率上得到明显提高,拼接精度达到毫米级。

基于能量最小的长边条翼身融合5边域过渡曲面的构造

为了构造某类飞行器的机翼面、机身面和长边条面间的5边域翼身融合过渡曲面,确定过渡线、边界线、内部曲线及内部曲线上的法线矢量,以边界线、过渡线和内部曲线为位置约束,以基曲面在过渡线处的法线矢量和内部曲线处的法矢为法矢约束,采用基于能量最小的曲面造型方法分别构建5张4边域曲面.研究表明此方法使这5张曲面在内部跨界曲线处,以及它们和基曲面间在过渡线处的法矢相同.实现了5张4边域曲面拼接构造G1连续的5边域翼身融合过渡曲面.

Three-dimensional multi-constraint route planning of unmanned aerial vehicle low-altitude penetration based on coevolutionary multi-agent genetic algorithm

To address the issue of premature convergence and slow convergence rate in three-dimensional (3D) route planning of unmanned aerial vehicle (UAV) low-altitude penetration,a novel route planning method was proposed.First and foremost,a coevolutionary multi-agent genetic algorithm (CE-MAGA) was formed by introducing coevolutionary mechanism to multi-agent genetic algorithm (MAGA),an efficient global optimization algorithm.A dynamic route representation form was also adopted to improve the flight route accuracy.Moreover,an efficient constraint handling method was used to simplify the treatment of multi-constraint and reduce the time-cost of planning computation.Simulation and corresponding analysis show that the planning results of CE-MAGA have better performance on terrain following,terrain avoidance,threat avoidance (TF/TA2) and lower route costs than other existing algorithms.In addition,feasible flight routes can be acquired within 2 s,and the convergence rate of the whole evolutionary process is very fast.

Global Convergence of a New Restarting Conjugate Gradient Method for Nonlinear Optimizations

Conjugate gradient optimization algorithms depend on the search directions with different choices for the parameters in the search directions. In this note, by combining the nice numerical performance of PR and HS methods with the global convergence property of the class of conjugate gradient methods presented by HU and STOREY(1991), a class of new restarting conjugate gradient methods is presented. Global convergences of the new method with two kinds of common line searches, are proved. Firstly, it is shown that, using reverse modulus of continuity function and forcing function, the new method for solving unconstrained optimization can work for a continously dif ferentiable function with Curry-Altman's step size rule and a bounded level set. Secondly, by using comparing technique, some general convergence properties of the new method with other kind of step size rule are established. Numerical experiments show that the new method is efficient by comparing with FR conjugate gradient method.

A hybrid algorithm based on ILP and genetic algorithm for time-aware test case prioritization

To solve the problem of time-awarc test case prioritization,a hybrid algorithm composed of integer linear programming and the genetic algorithm(ILP-GA)is proposed.First,the test case suite which cm maximize the number of covered program entities a d satisfy time constraints is selected by integer linea progamming.Secondly,the individual is encoded according to the cover matrices of entities,and the coverage rate of program entities is used as the fitness function and the genetic algorithm is used to prioritize the selected test cases.Five typical open source projects are selected as benchmark programs.Branch and method are selected as program entities,and time constraint percentages a e 25%and 75%.The experimental results show that the ILP-GA convergence has faster speed and better stability than ILP-additional and IP-total in most cases,which contributes to the detection of software defects as early as possible and reduces the software testing costs.

Instantaneous phase inversion based on an unwrapping algorithm

The full-waveform inversion method is a high-precision inversion method based on the minimization of the misfit between the synthetic seismograms and the observed data.However,this method suffers from cycle skipping in the time domain or phase wrapping in the frequency because of the inaccurate initial velocity or the lack of low-frequency information.furthermore,the object scale of inversion is affected by the observation system and wavelet bandwidth,the inversion for large-scale structures is a strongly nonlinear problem that is considerably difficult to solve.In this study,we modify the unwrapping algorithm to obtain accurate unwrapped instantaneous phase,then using this phase conducts the inversion for reducing the strong nonlinearity.The normal instantaneous phases are measured as modulo 2π,leading the loss of true phase information.The path integral algorithm can be used to unwrap the instantaneous phase of the seismograms having time series and onedimensional(1 D)signal characteristics.However,the unwrapped phase is easily affected by the numerical simulation and phase calculations,resulting in the low resolution of inversion parameters.To increase the noise resistance and ensure the inversion accuracy,we present an improved unwrapping method by adding an envelope into the path integral unwrapping algorithm for restricting the phase mutation points,getting accurate instantaneous phase.The objective function constructed by unwrapping instantaneous phase is less affected by the local minimum,thereby making it suitable for full-waveform inversion.Further,the corresponding instantaneous phase inversion formulas are provided.Using the improved algorithm,we can invert the low-wavenumber components of the underneath structure and ensure the accuracy of the inverted velocity.Finally,the numerical tests of the 2 D Marmousi model and 3 D SEG/EAGE salt model prove the accuracy of the proposed algorithm and the ability to restore largescale low-wavenumber structures,respectively.

An optimal on-site calibration method in measurement system of dynamic envelope curve of high-speed vehicle

High-speed vehicle dynamic envelope curve is defined as the maximum limit outline affected by a variety of adverse factors while the train is running. Considering the difficulties in the current measurement system such as complicated calibration process,cumbersome aided-instruments,strict limitation of working distance, this paper carries out an optical method in which two high-speed cameras with variable-zoom lenses are adopted as binocular stereo sensors of measurement system and a high-ac-curacy 3D target with fast reconstruction is designed. The intrinsic parameters of the sensors and the relative positions between coordinate systems are solved by the method of colinearity constrained optimization algorithm. The calibration process is easy to operate and the device is also of portability. Most importantly, the severe working distance limitation between sensors and measured body is solved, enhancing the adaptability of measurement system to environment. Experimental results show that when the sensors are in the range of 8 -16 m away from the measured body, system accuracy can reach up to ±0. 5 mm, which meets the requirements to measure the dynamic envelope curve of high-speed vehicle.

A NEW SOLUTION MODEL OF NONLINEAR DYNAMIC LEAST SQUARE ADJUSTMENT

The nonlinear least square adjustment is a head object studied in technology fields. The paper studies on the non derivative solution to the nonlinear dynamic least square adjustment and puts forward a new algorithm model and its solution model. The method has little calculation load and is simple. This opens up a theoretical method to solve the linear dynamic least square adjustment.

An active set algorithm for nonlinear optimization with polyhedral constraints

A polyhedral active set algorithm PASA is developed for solving a nonlinear optimization problem whose feasible set is a polyhedron. Phase one of the algorithm is the gradient projection method, while phase two is any algorithm for solving a linearly constrained optimization problem. Rules are provided for branching between the two phases. Global convergence to a stationary point is established, while asymptotically PASA performs only phase two when either a nondegeneracy assumption holds, or the active constraints are linearly independent and a strong second-order sufficient optimality condition holds.

A Dwindling Filter Line Search Algorithm for Nonlinear Equality Constrained Optimization

This paper proposes a dwindling filter line search algorithm for nonlinear equality constrained optimization. A dwindling filter, which is a modification of the traditional filter, is employed in the algorithm. The envelope of the dwindling filter becomes thinner and thinner as the step size approaches zero. This new algorithm has more flexibility for the acceptance of the trial step and requires less computational costs compared with traditional filter algorithm. The global and local convergence of the proposed algorithm are given under some reasonable conditions. The numerical experiments are reported to show the effectiveness of the dwindling filter algorithm.

A proximal point algorithm revisit on the alternating direction method of multipliers

The alternating direction method of multipliers(ADMM)is a benchmark for solving convex programming problems with separable objective functions and linear constraints.In the literature it has been illustrated as an application of the proximal point algorithm(PPA)to the dual problem of the model under consideration.This paper shows that ADMM can also be regarded as an application of PPA to the primal model with a customized choice of the proximal parameter.This primal illustration of ADMM is thus complemental to its dual illustration in the literature.This PPA revisit on ADMM from the primal perspective also enables us to recover the generalized ADMM proposed by Eckstein and Bertsekas easily.A worst-case O(1/t)convergence rate in ergodic sense is established for a slight extension of Eckstein and Bertsekas’s generalized ADMM.

A LINE SEARCH FILTER INEXACT SQP METHOD FOR NONLINEAR EQUALITY CONSTRAINED OPTIMIZATION

This paper proposes an inexact SQP method in association with line search filter technique for solving nonlinear equality constrained optimization. For large-scale applications, it is expensive to get an exact search direction, and hence the authors use an inexact method that finds an approximate solution satisfying some appropriate conditions. The global convergence of the proposed algorithm is established by using line search filter technique. The second-order correction step is used to overcome the Maratos effect, while the line search filter inexact SQP method has q-superlinear local convergence rate. Finally, the results of numerical experiments indicate that the proposed method is efficient for the given test problems.

GEOMETRIC ERROR CONTROL IN THE PARABOLA-BLENDING LINEAR INTERPOLATOR

This paper considers geometric error control in the parabola-blending linear interpolation method(Zhang,et al.,2011).Classical model of chord error by approximation with contact circle on the parabolas leads to incorrect result.By computing the geometric error directly without accumulating the approximation error and chord error,the authors realize correct geometric error control by establishing inequality constraints on the accelerations of the motion.

A LINE SEARCH FILTER SECANT METHOD FOR NONLINEAR EQUALITY CONSTRAINED OPTIMIZATION

This paper formulates and analyzes a line search method for general nonlinear equalityconstrained optimization based on filter methods for step acceptance and secant methods for searchdirection.The feature of the new algorithm is that the secant algorithm is used to produce a searchdirection,a backtracking line search procedure is used to generate step size,some filtered rules areused to determine step acceptance,second order correction technique is used to reduce infeasibility andovercome the Maratos effect.Global convergence properties of this method are analyzed:under mildassumptions it is showed that every limit point of the sequence of iterates generated by the algorithmis feasible,and that there exists at least one limit point that is a stationary point for the problem.Moreover,it is also established that the Maratos effect can be overcome in our new approach by addingsecond order correction steps so that fast local superlinear convergence to a second order sufficient localsolution is achieved.Finally,the results of numerical experiments are reported to show the effectivenessof the line search filter secant method.

A Dwindling Filter Algorithm with a Modified Subproblem for Nonlinear Inequality Constrained Optimization

The authors propose a dwindling filter algorithm with Zhou's modified subproblem for nonlinear inequality constrained optimization.The feasibility restoration phase,which is always used in the traditional filter method,is not needed.Under mild conditions,global convergence and local superlinear convergence rates are obtained.Numerical results demonstrate that the new algorithm is effective.

THE CONVERGENCE OF A NEW MODIFIED BFGS METHOD WITHOUT LINE SEARCHES FOR UNCONSTRAINED OPTIMIZATION OR COMPLEXITY SYSTEMS

In this paper,a new modified BFGS method without line searches is proposed.Unlike traditionalBFGS method,this modified BFGS method is proposed based on the so-called fixed steplengthstrategy introduced by Sun and Zhang.Under some suitable assumptions,the global convergence andthe superlinear convergence of the new algorithm are established,respectively.And some preliminarynumerical experiments,which shows that the new Algorithm is feasible,is also reported.

GLOBAL CONVERGENCE OF A CLASS OF SMOOTH PENALTY METHODS FOR SEMI-INFINITE PROGRAMMING

For the semi-infinite programming （SIP） problem, the authors first convert it into an equivalent nonlinear programming problem with only one inequality constraint by using an integral function, and then propose a smooth penalty method based on a class of smooth functions. The main feature of this method is that the global solution of the penalty function is not necessarily solved at each iteration, and under mild assumptions, the method is always feasible and efficient when the evaluation of the integral function is not very expensive. The global convergence property is obtained in the absence of any constraint qualifications, that is, any accumulation point of the sequence generated by the algorithm is the solution of the SIP. Moreover, the authors show a perturbation theorem of the method and obtain several interesting results. Furthermore, the authors show that all iterative points remain feasible after a finite number of iterations under the Mangasarian-Fromovitz constraint qualification. Finally, numerical results are given.