基于CUDA的AES并行算法优化

为提升高级加密标准(AES)的加密性能,利用显卡的通用计算能力,在统一计算设备架构(CUDA)平台上实现AES的128位、192位和256位3个版本的GPU并行算法,并提出优化的AES并行算法。在考虑块内线程数量、共享存储器容量和总块数的基础上,根据分块最优值的经验数据指导AES算法在GPU上的最优分块。实验结果表明,与未优化的AES并行算法相比,该算法的3个版本在Nvidia Geforce G210显卡上的加密速度分别提高5.28%,14.55%和12.53%,而在Nvidia Geforce GTX460显卡上的加密速度分别提高12.48%,15.40%和15.84%,且能更好地对SSL数据进行加密。     

不同重力环境的空间机械臂自抗扰轨迹跟踪控制

针对空间机械臂在地面装调与空间应用时,由于重力环境变化导致机械臂模型发生变化的问题,提出了一种自抗扰控制算法,用于完成空间机械臂轨迹跟踪控制的任务.该算法通过将系统模型及未知外扰作为系统的总和扰动,并利用扩张状态观测器对该扰动进行观测且给予补偿,从而提高了系统抗扰的性能.当机械臂模型随重力环境变化而发生变化时,使用同一个自抗扰控制器对其末端轨迹进行控制,均能取得较好的控制效果.通过对系统的稳定性进行分析,证明了所设计控制器的有效性.将仿真结果与PD控制及自适应鲁棒控制做比较,结果表明该控制算法不仅能适应机械臂模型的变化而且还能有效抵抗系统的扰动,从而使系统具有较强的鲁棒性.     

基于改进离散粒子群禁忌算法的并行测试任务调度

针对并行测试任务调度问题特点,提出了一种解决并行测试任务调度的离散粒子群禁忌算法,设计了符合并行测试任务调度特点的粒子初始化编码方式、粒子位置转移公式以及禁忌搜索邻域。通过与禁忌搜索相结合,避免了离散粒子群算法早熟问题。通过实验以及算法对比,表明提出的算法是快速有效的,能够很好地解决并行测试任务调度问题,求得测试总时间最短的任务调度序列。     

平行梁式电容传感器力学性能的仿真与分析

针对S型平行梁式电容称重传感器参数结构设计问题,运用有限元软件ANSYS Workbench对平行梁式弹性体结构进行参数化模拟仿真,基于二次多项式曲面拟合建立其结构力学性能与主要结构参数的函数关系,并通过算例分析验证了拟合公式的有效性。研究表明,拟合公式对复杂仿真计算后的结果有很好的近似,能更好的反应实际情况,公式结构更加简洁,具有较快的收敛速度、较高的精度和较好的推广能力,有利于准确实现平行梁式电容称重传感器结构参数设计。     

基于有限元方法的3-PPSR大长径比柔性并联机器人刚度模型分析

为实现较大工作空间的运动,设计了一种大长径比的3-PPSR结构的微操作柔性并联机器人.该机器人由压电马达驱动,采用大长径比柔性铰链连接,柔性铰链的变形范围在毫米级,适用于要求大变形的场合,同时具有结构简单、无奇异、无间隙、运动精度高等特点.由于系统的刚度直接影响系统的运动精度、承载负荷等性能,针对大长径比柔性铰链特点,运用有限元方法,建立了柔性铰链的数学模型,并采用整体刚度的方法,结合机构协调方程和力平衡方程,得到系统的柔性刚度模型.最后,采用ANSYS比较了所建理论刚度模型结果与有限元模型分析的结果.分析结果表明,理论刚度模型合理,符合机构的运动特征.     

基于似人特性的拟人臂机器人自主抓取动作规划

针对拟人臂机器人在家庭环境中的自主抓取任务,提出了一种强调运动姿态似人特性的拟人臂动作的规划方法.该方法分为两个部分,构型规划和运动规划,构型规划基于人体工程学中广泛使用的快速上肢评估准则评价机器人运动姿态的似人特性,并在此基础上以机器人末端传速速率最优为目标规划机器人运动过程中的关键构型.运动规划结合了Fitts定律和似人运动模型规划了机器人末端的空间轨迹.本文最后以Motoman SDA10D拟人双臂机器人和Mitsubishi PA10机器人为例,具体介绍了该方法的应用和规划的结果,规划结果证明了本方法的可行性和有效性.     

施釉机器人静力学分析的研究

根据6自由度施釉机器人的终端受力，利用雅可比矩阵，求解出各转动关节在稳定平衡状态下的受力和所需的驱动力矩，为设计施釉机器人确定各转轴的电机及其他动力元件提供了可靠的依据。     

Decomposition of timed automata for solving scheduling problems

A decomposition algorithm for scheduling problems based on timed automata (TA) model is proposed. The problem is represented as an optimal state transition problem for TA. The model comprises of the parallel composition of submodels such as jobs and resources. The procedure of the proposed methodology can be divided into two steps. The first step is to decompose the TA model into several submodels by using decomposable condition. The second step is to combine individual solution of subproblems for the decomposed submodels by the penalty function method. A feasible solution for the entire model is derived through the iterated computation of solving the subproblem for each submodel. The proposed methodology is applied to solve flowshop and jobshop scheduling problems. Computational experiments demonstrate the effectiveness of the proposed algorithm compared with a conventional TA scheduling algorithm without decomposition.     

Stability analysis and controller synthesis for discrete uncertain singular fuzzy systems with distinct difference matrices in the rules

This paper mainly studies an extended discrete singular fuzzy model incorporating the multiple difference matrices in the rules and discusses its stability and design issues. By embracing additional algebraic constraint, traditional discrete Takagi-Sugeno (T-S) fuzzy model can be extended to a generalised discrete singular Takagi-Sugeno (GDST-S) model with individual difference matrices E_i in the locally singular models, where it can describe a larger class of physical or non-linear systems. Based on the linear matrix inequality (LMI) approach, we focus on deriving some explicit stability and design criteria expressed by the LMIs for the regarded system. Thus, the stability verification and controller synthesis can be performed by the current LMI tools. Finally, some illustrative examples are given to illustrate the effectiveness and validity of the proposed approach.     

Adaptive recurrent neural network control of uncertain constrained nonholonomic mobile manipulators

In this article, motion/force control problem of a class of constrained mobile manipulators with unknown dynamics is considered. The system is subject to both holonomic and nonholonomic constraints. An adaptive recurrent neural network controller is proposed to deal with the unmodelled system dynamics. The proposed control strategy guarantees that the system motion asymptotically converges to the desired manifold while the constraint force remains bounded. In addition, an adaptive method is proposed to identify the contact surface. Simulation studies are carried out to verify the validation of the proposed approach.