带法向约束的隐式T样条曲线重构

目的 隐式曲线能够描述复杂的几何形状和拓扑结构,而传统的隐式B样条曲线的控制网格需要大量多余的控制点满足拓扑约束。有些情况下,获取的数据点不仅包含坐标信息,还包含相应的法向约束条件。针对这个问题,提出了一种带法向约束的隐式T样条曲线重建算法。方法 结合曲率自适应地调整采样点的疏密,利用二叉树及其细分过程从散乱数据点集构造2维T网格;基于隐式T样条函数提出了一种有效的曲线拟合模型。通过加入偏移数据点和光滑项消除额外零水平集,同时加入法向项减小曲线的法向误差,并依据最优化原理将问题转化为线性方程组求解得到控制系数,从而实现隐式曲线的重构。在误差较大的区域进行T网格局部细分,提高重建隐式曲线的精度。结果 实验在3个数据集上与两种方法进行比较,实验结果表明,本文算法的法向误差显著减小,法向平均误差由10^-3数量级缩小为10^-4数量级,法向最大误差由10^-2数量级缩小为10^-3数量级。在重构曲线质量上,消除了额外零水平集。与隐式B样条控制网格相比,3个数据集的T网格的控制点数量只有B样条网格的55.88%、39.80%和47.06%。结论 本文算法能在保证数据点精度的前提下,有效降低法向误差,消除了额外的零水平集。与隐式B样条曲线相比,本文方法减少了控制系数的数量,提高了运算速度。     

An improved critical constraint method for structural optimization of product families

This paper discusses important improvements in the efficient Critical Constraint Method (CCM) for the optimization of structural product families subjected to multiple crash load cases. The method was first presented by Öman and Nilsson (Struct Multidisc Optim 41(5):797–815, 2010). However, the algorithm often converged towards an infeasible solution, which considerably limited the applicability of the method. Therefore, improvements are presented here to make the method more robust regarding feasible solutions, resulting in only a minor decrease in efficiency compared to the original method. The improvements include; a penalty approach to control the feasibility of the method by continuously pushing the solution out of the infeasible region, a dynamic contraction algorithm to increase the accuracy and robustness of the method by considering the optimization progress and variable history in the reduction of the step size, and the implementation of a parallel approach to further increase the efficiency of the method by enabling the full potential of large-scale computer clusters. Finally, the potential of the improved CCM algorithm is demonstrated on a large-scale industrial family optimization problem and it is concluded that the high efficiency of the method enables the usage of large product family optimization in the design process.     

An improved method for determining the stability of interval matrices

In this paper, the problem of the stability of interval matrices has been tackled using the properties of real stability radius. Based on these, a necessary and sufficient condition has been developed for the Hurwitz (Schur) stability of an interval matrix. An algorithm has been suggested on the basis of the above results to determine the stability of such a system. This work provides an alternative tool to that proposed by Wang et al. and it has been claimed, on the basis of comparative results, that it is a more efficient method in terms of both the computation time and the number of matrices to be checked.     

Linear quadratic control with stability degree constraint

The problem of static state feedback Linear Quadratic (LQ) optimal control subject to a prescribed degree of stability for the closed-loop system is considered in this paper. A necessary optimality condition is given via the Lagrange multiplier method. A globally convergent algorithm is provided to solve the optimization problem. It is shown that the algorithm recovers the standard LQ feedback gain provided the desired stability degree is small enough to be within the range by the standard LQ design. As for other cases the optimum occurs on the boundary of the α-region. A numerical example shows that the proposed algorithm provides a better design compared to the existing methods.     

两种改进的模拟退火算法求解大值域约束满足问题

随机约束满足问题的相变现象及求解算法是NP-完全问题的研究热点。RB模型（Revised B）是一个非平凡的随机约束满足问题,它具有精确的可满足性相变现象和极易产生难解实例这两个重要特征。针对RB模型这一类具有大值域的随机约束满足问题,提出了两种基于模拟退火的改进算法即RSA（Revised Simulated Annealing Algorithm）和GSA（Genetic-simulated Annealing Algorithm）。将这两种算法用于求解RB模型的随机实例,数值实验结果表明：在进入相变区域时,RSA和GSA算法依然可以有效地找到随机实例的解,并且在求解效率上明显优于随机游走算法。在接近相变阈值点时,由这两种算法得到的最优解仅使得极少数的约束无法满足。     

An improved approximation algorithm for two-machine flow shop scheduling with an availability constraint

We study the problem of scheduling n jobs in a two-machine flow shop where the second machine is not available for processing during a given time interval. A resumable scenario is assumed, i.e., if a job cannot be finished before the down period it is continued after the machine becomes available again. The objective is to minimize the makespan. The best fast approximation algorithm for this problem guarantees a relative worst-case error bound of 4/3. We present an improved algorithm with a relative worst-case error bound of 5/4.     

Computationally efficient algorithms for constraint handling with guaranteed stability and near optimality

Predictive control strategies allow for the systematic handling of constraint, performance and stability. However, the associated algorithms can be computational burdensome and/or difficult to unravel. The aim of this paper is to discuss and compare algorithms based on invariant sets which meet the additional requirement for computational simplicity. There may of course be a concomitant loss of optimality, but as illustrated, this can be minimal and often is a small price to pay when one considers the significant improvements in efficiency.     

Multidirectional compliance and constraint for improved robotic deburring. Part 1: improved positioning

This two-part paper presents a method for both improving the positioning capability and increasing the effective stiffness (bracing) of a robotic manipulator through multidirectional compliance and constraint. Improved relative positioning and increased stiffness are obtained through the use of: (1) an end-effector mounted jig capable of establishing a workpart-based reference frame through multipoint contact with the workpart, and (2) a manipulator compliance designed to provide a specified form of directional coupling in its mapping of forces to deflections. The directional coupling in the compliance is shown to be important in establishing multipoint contact (during insertion) and in maintaining contact (while edge tracking) despite finite positional errors. Improved manipulator positioning is demonstrated in the context of workpart edge deburring.     

Multidirectional compliance and constraint for improved robotic deburring. Part 2: improved bracing

This two-part paper presents a method for both improving the positioning capability and increasing the effective stiffness (bracing) of a robotic manipulator through multidirectional compliance and constraint. Improved positioning and improved bracing are attained through the effective use of multiple unilateral kinematic constraints in different directions. The companion paper identified how to specify the compliant characteristics of a manipulator so contact forces lead to deflections that eliminate positional misalignments and result in improved relative positioning through force guidance. In this part, we show that the characteristics beneficial to force guidance are the same characteristics that provide improved bracing when partially constrained by contact. Improved bracing is demonstrated in the context of workpart edge deburring.     

边界保持的隐式曲面三角化方法

隐式曲面三角化是隐式曲面绘制的常用算法.对于开区域上散乱点数据重建的隐式曲面,常用的隐式曲面三角化方法得到网格模型不能很好地保持散乱点数据的边界.针对该问题,提出了一种边界保持的隐式曲面三角化方法.根据散乱点数据的空间分布,控制等值面的抽取范围,实现了边界保持.实验结果表明,该算法能够产生和散乱点数据边界一致的三角网格.     

An improved robust stability and robust stabilization method for linear discrete-time uncertain systems

This paper addresses the problems of the robust stability and robust stabilization of a discrete-time system with polytopic uncertainties.A new and simple method is presented to directly decouple the Lyapunov matrix and the system dynamic matrix.Combining this method with the parameter-dependent Lyapunov function approach yields new criteria that include some existing ones as special cases.A numerical example illustrates the improvement over the existing ones.     

An improved robust stability and robust stabilization method for linear discrete-time uncertain systems

This paper addresses the problems of the robust stability and robust stabilization of a discrete-time system with polytopic uncertainties. A new and simple method is presented to directly decouple the Lyapunov matrix and the system dynamic matrix. Combining this method with the parameter-dependent Lyapunov function approach yields new criteria that include some existing ones as special cases. A numerical example illustrates the improvement over the existing ones.     

An improved constraint satisfaction adaptive neural network for job-shop scheduling

This paper presents an improved constraint satisfaction adaptive neural network for job-shop scheduling problems. The neural network is constructed based on the constraint conditions of a job-shop scheduling problem. Its structure and neuron connections can change adaptively according to the real-time constraint satisfaction situations that arise during the solving process. Several heuristics are also integrated within the neural network to enhance its convergence, accelerate its convergence, and improve the quality of the solutions produced. An experimental study based on a set of benchmark job-shop scheduling problems shows that the improved constraint satisfaction adaptive neural network outperforms the original constraint satisfaction adaptive neural network in terms of computational time and the quality of schedules it produces. The neural network approach is also experimentally validated to outperform three classical heuristic algorithms that are widely used as the basis of many state-of-the-art scheduling systems. Hence, it may also be used to construct advanced job-shop scheduling systems.     

An improved robust stability and robust stabilization method for linear discrete-time uncertain systems

This paper addresses the problems of the robust stability and robust stabilization of a discrete-time system with polytopic uncertainties. A new and simple method is presented to directly decouple the Lyapunov matrix and the system dynamic matrix. Combining this method with the parameter-dependent Lyapunov function approach yields new criteria that include some existing ones as special cases. A numerical example illustrates the improvement over the existing ones.     

Weighted control with ‐stability constraint for switched positive linear systems

This paper is concerned with the problem of control with ‐stability constraint for a class of switched positive linear systems. The ‐stability means that all the poles of each subsystem of the resultant closed‐loop system belong to a prescribed disk in the complex plane. A sufficient condition is derived for the existence of a set of state‐feedback controllers, which guarantees that the closed‐loop system is not only positive and exponentially stable with each subsystem ‐stable but also has a weighted performance for a class of switching signals with average dwell time greater than a certain positive constant. Both continuous‐time and discrete‐time cases are considered, and all of the obtained conditions are formulated in terms of linear matrix inequalities, whose solution also yields the desired controller gains and the corresponding minimal average dwell time. Numerical examples are given to illustrate the effectiveness of the presented approach.Copyright © 2012 John Wiley & Sons, Ltd.     

An improved model for vehicle routing problem with time constraint based on genetic algorithm

A vehicle routing problem (VRP) with time constraint is one of the important problems in distribution and transportation. Thus the generic VRP and its practical extensions are discussed in great detail in the literatures. In the VRP, the service of a customer must start and finish within a given time interval. The objective of this problem is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. In this research we concentrated on developing a GA–TSP model by improving the genetic algorithm (GA) operators and the initial population. For the computational purpose, we developed a GUI (graphic user interface)-type computer program according to the proposed method. The computational results show that the proposed method is very effective on a set of standard test problems and it can be potentially useful in solving the VRPs.     

Fault detection for interval type-2 fuzzy stochastic systems with D stability constraint

This paper investigates the fault detection problem for interval type-2 (IT2) fuzzy stochastic systems with D stability constraint. In the design process, the constructed IT2 fuzzy stochastic system and fault detection filter use different membership functions and the number of fuzzy rules. The parameter uncertainties in the IT2 membership functions are captured through upper and lower membership functions. For relaxing the stability analysis and deriving the existence conditions of the fault detection filter that guarantee the mean-square asymptotically stable and H_∞ performance of the inferred IT2 fault detection system, the approach of dividing the state space and the values of upper and lower membership functions are exploited. Finally, simulation results are given to show the effectiveness of the presented results.     

On the stability of receding-horizon LQ control with zero-stateterminal constraint

Receding-horizon control with zero state terminal constraint for linear time-varying discrete systems is considered. It is shown that uniform controllability suffices to ensure closed-loop exponential stability thus relaxing the reversibility and/or detectability requirements made in the existing literature. The result has interesting implications in the design of predictive controllers with guaranteed closed-loop stability