Position,Jacobian and workspace analysis of a 3-PSP spatial parallel manipulator

This paper investigates the problems of kinematics, Jacobian, singularity and workspace analysis of a spatial type of 3-PSP parallel manipulator. First, structure and motion variables of the robot are addressed. Two operational modes, non-pure translational and coupled mixed-type are considered. Two inverse kinematics solutions, an analytical and a numerical, for the two operational modes are presented. The direct kinematics of the robot is also solved utilizing a new geometrical approach. It is shown, unlike most parallel robots, the direct kinematics problem of this robot has a unique solution. Next, analytical expressions for the velocity and acceleration relations are derived in invariant form. Auxiliary vectors are introduced to eliminate passive velocity and acceleration vectors. The three types of conventional singularities are analyzed. The notion of non-pure rotational and non-pure translational Jacobian matrices is introduced. The non-pure rotational and non-pure translational Jacobian matrices are combined to form the Jacobian of constraint matrix which is then used to obtain the constraint singularity. Finally, two methods, a discretization method and one based on direct kinematics are presented and robot non-pure translation and coupled mixed-type reachable workspaces are obtained. The influence of tool length on workspace is also studied.     

Bagging Constraint Score for feature selection with pairwise constraints

Constraint Score is a recently proposed method for feature selection by using pairwise constraints which specify whether a pair of instances belongs to the same class or not. It has been shown that the Constraint Score, with only a small amount of pairwise constraints, achieves comparable performance to those fully supervised feature selection methods such as Fisher Score. However, one major disadvantage of the Constraint Score is that its performance is dependent on a good selection on the composition and cardinality of constraint set, which is very challenging in practice. In this work, we address the problem by importing Bagging into Constraint Score and a new method called Bagging Constraint Score (BCS) is proposed. Instead of seeking one appropriate constraint set for single Constraint Score, in BCS we perform multiple Constraint Score, each of which uses a bootstrapped subset of original given constraint set. Diversity analysis on individuals of ensemble shows that resampling pairwise constraints is helpful for simultaneously improving accuracy and diversity of individuals. We conduct extensive experiments on a series of high-dimensional datasets from UCI repository and gene databases, and the experimental results validate the effectiveness of the proposed method.     

求解约束优化问题的粒子进化变异遗传算法

设计一种求解约束优化问题的粒子进化变异遗传算法(IGA_PSE).首先,分析候选解约束条件离差统计信息与约束违反函数之间的关系及其性质,基于约束条件离差统计信息提出一种改进约束处理方法;其次,基于粒子进化策略提出3种新变异算子;然后,讨论该算法早熟收敛的3种情况,并提出相应的种群多样化维持策略;最后,通过数值实验表明所提出的算法能够有效求解约束优化问题.     

面向欠约束几何系统的一种同伦求解方法

针对几何约束系统的数值求解过程中，经常发生的数值不稳定性问题，构造了一种面向欠约束系统的同伦方法，并将其与现有的求解与分解方法有机地结合起来，提出了一种牛顿－同伦混合方法，在牛顿迭代失败的位置自动调用欠约束同伦法，既提高了几何约束求解器的效率，同时又保证了求解的效率。     

具有奇异位置的多体系统动力学方程的改进算法

多体系统进行数值仿真时,很多选择了微分代数混合方程作为多体系统动力学数学模型.本文在现有的约束稳定化理论基础上,提出了针对具有奇异位置的多体系统动力学方程的改进算法.算法通过修正速度违约和控制稳定项,讨论了具有奇异位置的微分代数混合方程的数值仿真问题并给出了稳定项中相关系数的建议值,从而有效克服了求解混合方程时因为构型奇异给计算造成的困难.算例分别采用改进算法与ADAMS软件进行仿真,计算结果的比较表明了改进算法的有效性.本文给出的基于能量守恒的能量差曲线也证明了改进算法的有效性.     

Global and local scatter based semi-supervised dimensionality reduction with active constraints selection in ensemble subspaces

Semi-supervised dimensionality reduction has attracted an increasing amount of attention in this big-data era. Many algorithms have been developed with a small number of pairwise constraints to achieve performances comparable to those of fully supervised methods. However, one challenging problem with semi-supervised approaches is the appropriate choice of the constraint set, including the cardinality and the composition of the constraint set which, to a large extent, affects the performance of the resulting algorithm. In this work, we address the problem by incorporating ensemble subspaces and active learning into dimensionality reduction and propose a new global and local scatter based semi-supervised dimensionality reduction method with active constraints selection. Unlike traditional methods that select the supervised information in one subspace, we pick up pairwise constraints in ensemble subspaces, where a novel active learning algorithm is designed with both exploration and filtering to generate informative pairwise constraints. The automatic constraint selection approach proposed in this paper can be generalized to be used with all constraint-based semi-supervised learning algorithms. Comparative experiments are conducted on four face database and the results validate the effectiveness of the proposed method.     

基于输出反馈的欠驱动TORA系统的有界输入控制

针对欠驱动TORA(Translational oscillations with a rotational actuator)系统,设计了一种具有执行器饱和约束的输出反馈等[7]特别地,由Rand学者提出的TORA系统,最初控制器.与其他现有方法相比,本文方法不仅考虑了执行器饱和约束和作为双自旋航天器的简化模型用于研究自振现象.后来由于速度信号不可测情形,而且考虑了旋转小球可能存在的循环行为.具体而TORA系统具有强耦合、高度非线性、欠驱动等特性,而被言,首先根据TORA系统模型分析了TORA系统的控制目标;随后,作为一种非线性基准系统主要用于非线性控制器设计、验证构造了一种新颖的能量函数,在此基础上设计了一种考虑执行器饱和约非线性控制算法的控制性能或教学研究.目前国内外已有多束的输出反馈控制器,并通过严格的数学分析证明了闭环系统关于平衡点的稳定性;最后,借助数值仿真测试检验了所提控制器的控制性能,并所高校和研究机构针对TORA系统的控制问题展开研究.对与已有方法进行了对比.仿真测试结果表明本文所提方法具有更好的控法制性能.     

Tri-pyramid Robot: Design and kinematic analysis of a 3-DOF translational parallel manipulator

3-DOF translational parallel manipulators have been developed in many different forms, but they still have respective disadvantages in different applications. To overcome their disadvantages, the structure and constraint design of a 3-DOF translational parallel manipulator is presented and named the Tri-pyramid Robot. In the constraint design of the presented manipulator, a conical displacement subset is defined based on displacement group theory. A triangular pyramidal constraint is presented and applied in the constraint designs between the manipulator?s subchains. The structural properties including the decoupled motions, overconstraint elimination, singularity free workspace, fixed actuators and isotropic configuration are analyzed and compared to existing structures. The Tri-pyramid Robot is constrained and realized by a minimal number of 1-DOF joints. The kinematic position solutions, workspace with variation of structural parameters, Jacobian matrix, isotropic and dexterity analysis are performed and evaluated in the numerical simulations.     

An effective cellular particle swarm optimization for parameters optimization of a multi-pass milling process

In the milling process, the selection of machining parameters is very important as these parameters determine the processing time, quality, cost and so on, especially in the high-accuracy machine tools. However, the parameters optimization of a multi-pass milling process is a nonlinear constrained optimization problem which is difficult to be solved by the traditional optimization techniques. Therefore, in order to solve this problem effectively, this paper proposes a novel parameters optimization method based on the cellular particle swarm optimization (CPSO). To address the constraints efficiently, the proposed method combines two constraints handling techniques, including the penalty function method and the constraints handling strategy of PSO. In the proposed CPSO, the smart cell constructs its neighborhood with self-adaptive function and constraints handling techniques, which guide the unfeasible particles to move to the feasible regions and search for better solutions. A case is adopted and solved to illustrate the effectiveness of the proposed CPSO algorithm. The results of the experiment study are analyzed and compared with those of the previous algorithms. The experimental results show that the proposed approach outperforms other algorithms and has achieved significant improvement.     

基于祖冲之类方法的多体动力学方程保能量保约束积分

针对一类多体动力学问题导出的微分-代数方程,提出一种保能量、保约束的算法.该算法基于祖冲之类方法和欧拉中点保辛差分,利用祖冲之类方法保证在时间格点上精确满足约束方程,避免约束违约问题;并进一步证明该算法在时间格点上可以精确保能量.数值算例进一步验证该算法的可靠性.     

基于祖冲之类方法的多体动力学方程保能量保约束积分

针对一类多体动力学问题导出的微分 代数方程,提出一种保能量、保约束的算法.该算法基于祖冲之类方法和欧拉中点保辛差分,利用祖冲之类方法保证在时间格点上精确满足约束方程,避免约束违约问题;并进一步证明该算法在时间格点上可以精确保能量.数值算例进一步验证该算法的可靠性.     

Quaternion-based robust trajectory tracking control for uncertain quadrotors

This paper presents a robust nonlinear controller design approach for uncertain quadrotors to implement trajectory tracking missions. The quaternion representation is applied to describe the rotational dynamics in order to avoid the singularity problem existing in the Euler angle representation. A nonlinear robust controller is proposed, which consists of an attitude controller to stabilize the rotational motions and a position controller to control translational motions. The quadrotor dynamics involves uncertainties such as parameter uncertainties, nonlinearities, and external disturbances and their effects on closed-loop control system can be guaranteed to be restrained. Simulation results on the quadrotor demonstrate the effectiveness of the designed control approach.     

Hybrid parallel computing of minimum action method

In this work, we report a hybrid (MPI/OpenMP) parallelization strategy for the minimum action method recently proposed in [17]. For nonlinear dynamical systems, the minimum action method is a useful numerical tool to study the transition behavior induced by small noise and the structure of the phase space. The crucial part of the minimum action method is to minimize the Freidlin–Wentzell action functional. Due to the fact that the corresponding Euler–Lagrange equation is, in general, highly nonlinear and of high order, we solve the optimization problem directly instead of discretizing the Euler–Lagrange equation to provide a general but equivalent numerical framework. To enhance the efficiency of the minimum action method for general dynamical systems we consider parallel computing. In particular, we present a hybrid parallelization strategy based on MPI and OpenMP. Numerical results are presented to demonstrate the efficiency of the proposed parallelization strategy.     

Surrogate relaxation of a fuzzy multidimensional 0–1 knapsack model by surrogate constraint normalization rules and a methodology for multi-attribute project portfolio selection

In this paper, a multidimensional 0–1 knapsack model with fuzzy parameters is defuzzified using triangular norm (t-norm) and t-conorm fuzzy relations. In the first part of the paper, the surrogate relaxation models of the defuzzified models are developed, and the use of surrogate constraint normalization rules is proposed as the surrogate multipliers. A methodology is proposed to evaluate some surrogate constraint normalization rules proposed in the literature as well as one rule proposed in this paper. Three distance metrics are used to find the distance of fuzzy objective function from the surrogate models to the distance of fuzzy objective function from the original models. A numerical experiment shows that the rule proposed in this paper dominates the other rules considered in this paper for three distance metrics given the whole assumptions. In the second part of the paper, a methodology is proposed for multi-attribute project portfolio selection, and optimal solutions from the original defuzzified models as well as near-optimal solutions from their surrogate relaxation models are considered as alternatives. The aggregation of evaluation results is managed using a simple yet effective method so-called fuzzy Simple Additive Weighting (SAW) method. Then, the methodology is applied to a hypothetical construction project portfolio selection problem with multiple attributes.     

基于R2指标和目标空间分解的高维多目标粒子群优化算法

基于R2指标和分解策略的多目标粒子群优化算法(R2-MOPSO)在求解2、3个目标优化问题时具有较好的收敛性和多样性,但在求解高维多目标优化问题时难度较大.对此,提出一种基于R2指标和目标空间分解的高维多目标粒子群优化算法(R2-MOPSO-II).首先借鉴R2指标和目标空间分解策略综合权衡选择过程的收敛性和多样性,设计双层档案维护策略;然后设计一种新的向导选择策略来连接目标空间和决策变量空间,进而提出一种基于双层档案的速度和位置更新策略以权衡粒子群优化算法的勘探和开采能力;最后通过引入高斯学习策略和精英学习策略防止粒子陷入局部最优前沿.数值仿真结果表明,所提出算法在求解DTLZ和WFG测试问题时具有较好的收敛性和多样性.     

Improved genetic algorithm with two-level approximation for truss topology optimization

Truss topology optimization using Genetic Algorithms (GAs) usually requires large computational cost, especially for large-scale problems. To decrease the structural analyses, a GA with a Two-level Approximation (GATA) was proposed in a previous work, and showed good computational efficiency with less structural analyses. However, this optimization method easily converges to sub-optimum points, resulting in a poor ability to search for a global optimum. Therefore, to address this problem, we propose an Improved GA with a Two-level Approximation (IGATA) which includes several modifications to the approximation function and simple GA developed previously. A Branched Multi-point Approximation (BMA) function, which is efficient and without singularity, is introduced to construct a first-level approximation problem. A modified Lemonge penalty function is adopted for the fitness calculation, while an Elite Selection Strategy (ESS) is proposed to improve the quality of the initial points. The results of numerical examples confirm the lower computational cost of the algorithm incorporating these modifications. Numerous numerical experiments show good reliability of the IGATA given appropriate values for the considered parameters.     

新型动态控制力矩陀螺操纵律设计

首先提出一类新型控制力矩陀螺(CMG)操纵律,改变使用静态数据求逆构造力矩分配矩阵的设计思路,利用近似的动态微分特性构造力矩分配矩阵,进而对新型操纵律奇异性进行分析.基于奇异分析结果,设计一种切换型CMG奇异回避策略,使CMG能在首选力矩分配矩阵接近奇异时,使用非奇异的备选分配矩阵将之替换.仿真结果表明,所设计的CMG操纵律具有较高的计算精度,同时具有一定的奇异适应性.     

Comparative study of four penalty-free constraint-handling techniques in structural optimization using harmony search

This study investigates the search capability, stability, and computational efficiency of four improved penalty-free constraint-handling techniques (CHTs), including the death penalty, the Deb rule, the filter method, and the mapping strategy, in structural optimization using harmony search (HS). The first three general-purpose CHTs have been improved by hybridizing with a structural analysis filter strategy to enhance their computational efficiency based on the characteristics of structural optimization and the solution updating rule of the HS. This study also has modified the mapping operator of the mapping strategy to handle size and shape optimization with Euler buckling constraints. Four numerical examples examine the performances of these CHTs. The comparative results show that the mapping strategy exhibits apparent superiority both in search capability and stability among the four. However, it also demands the most computational cost, and the improved Deb rule becomes the most competitive method while considering computational efficiency. The difference between the death penalty and the Deb rule method is that the feasible solution initialization process required by the death penalty method will deteriorate its computational efficiency in problems with small feasible space. The filter method always reserves some infeasible solutions to guide the search in the iteration process. However, its performances are inferior to the other three approaches in four benchmark problems.

     

Globally convergent autocalibration using interval analysis

We address the problem of autocalibration of a moving camera with unknown constant intrinsic parameters. Existing autocalibration techniques use numerical optimization algorithms whose convergence to the correct result cannot be guaranteed, in general. To address this problem, we have developed a method where an interval branch-and-bound method is employed for numerical minimization. Thanks to the properties of interval analysis this method converges to the global solution with mathematical certainty and arbitrary accuracy and the only input information it requires from the user are a set of point correspondences and a search interval. The cost function is based on the Huang-Faugeras constraint of the essential matrix. A recently proposed interval extension based on Bernstein polynomial forms has been investigated to speed up the search for the solution. Finally, experimental results are presented.     

Handling many-objective optimisation problems with R2 indicator and decomposition-based particle swarm optimiser

An R2 indicator-based multi-objective particle swarm optimiser (R2-MOPSO) can obtain well-convergence and well-distributed solutions while solving two and three objectives optimisation problems. However, R2-MOPSO faces difficulty to tackle many-objective optimisation problems because balancing convergence and diversity is a key issue in high-dimensional objective space. In order to address this issue, this paper proposes a novel algorithm, named R2-MaPSO, which combines the R2 indicator and decomposition-based archiving pruning strategy into particle swarm optimiser for many-objective optimisation problems. The innovations of the proposed algorithm mainly contains three crucial factors: (1) A bi-level archiving maintenance approach based on the R2 indicator and objective space decomposition strategy is designed to balance convergence and diversity. (2) The global-best leader selection is based on the R2 indicator and the personal-best leader selection is based on the Pareto dominance. Meanwhile, the objective space decomposition leader selection adopts the feedback information from the bi-level archive. (3) A new velocity updated method is modified to enhance the exploration and exploitation ability. In addition, an elitist learning strategy and a smart Gaussian learning strategy are embedded into R2-MaPSO to help the algorithm jump out of the local optimal front. The performance of the proposed algorithm is validated and compared with some algorithms on a number of unconstraint benchmark problems, i.e. DTLZ1-DTLZ4, WFG test suites from 3 to 15 objectives. Experimental results have demonstrated a better performance of the proposed algorithm compared with several multi-objective particle swarm optimisers and multi-objective evolutionary algorithms for many-objective optimisation problems.