Hybrid integrator backstepping control of robotic manipulators driven by brushless DC motors

In this paper, a hybrid integrator backstepping controller is proposed for robotic manipulators actuated with brushless DC motors in the presence of arbitrary uncertain inertia parameters of the manipulator and the electrical parameters of the actuators. The main features are: 1) the design, requiring at most the joint velocities and stator currents feedback; 2) the semiglobal asymptotic stability of the controller being established in the Lyapunov sense; and 3) suitability of the scheme for multijoint robots. Simulation results are included to demonstrate the tracking performance.     

复杂工业过程运行的混合智能优化控制方法

工业过程运行的优化控制的目标是将反应产品在加工过程中的质量、效率、消耗的工艺指标控制在目标值范围内. 由于复杂工业过程的工艺指标难于在线测量且与控制回路输出之间的动态特性具有强非线性、强耦合、难以用精确模型描述、随生产边界条件变化而变化的综合复杂性,因此, 难以采用已有优化控制方法, 运行控制只能采用人工设定的控制方式. 由于人工控制不能及时准确地随运行工况调整设定值, 难以将工艺指标控制在目标值范围内, 甚至造成故障工况.本文提出了根据运行工况实时调整控制回路设定值, 通过控制系统跟踪调整后的设定值, 将工艺指标控制在目标值范围内的过程优化运行的混合智能控制方法. 该方法由控制回路预设定模型、前馈补偿与反馈补偿器、工艺指标预报模型、故障工况诊断和容错控制器组成. 在某选矿厂22台竖炉组成的焙烧过程的应用案例, 证明了所提出方法的有效性.     

Structural topology optimization using ant colony optimization algorithm

The ant colony optimization (ACO) algorithm, a relatively recent bio-inspired approach to solve combinatorial optimization problems mimicking the behavior of real ant colonies, is applied to problems of continuum structural topology design. An overview of the ACO algorithm is first described. A discretized topology design representation and the method for mapping ant's trail into this representation are then detailed. Subsequently, a modified ACO algorithm with elitist ants, niche strategy and memory of multiple colonies is illustrated. Several well-studied examples from structural topology optimization problems of minimum weight and minimum compliance are used to demonstrate its efficiency and versatility. The results indicate the effectiveness of the proposed algorithm and its ability to find families of multi-modal optimal design.     

时间序列的细微距离发现

时间序列是信息系统一储存在的一类重要数据对象,而序列间的距离计算是很多时间序列数据开采或数据提取问题的核心。针对目前的序列距离定义模型对非总体的细微关联特征不敏感的问题,提出了一种新的时间序列距离定义模－时间序列的细微距离ＭＤ（Ｘ,Ｙ）,并提出了一种将时间序列由时域映射到频域,在频域中分离出不同的序列变化形式,以确定时间序列细微差别程度的算法－ＦＤＤ算法。ＦＤＤ算法具有较高的效率,且可以 作基准值     

Comments on “Adaptive variable structure set-point control ofunderactuated robots” [and reply]

This note points out several technical errors in the above mentioned paper by Su et al. (ibid. vol.44 (1999)). It is shown that the choice of Lyapunov function is inadequate for the system stability analysis, and the asymptotic tracking of the underactuated robot joints is not achievable under certain conditions. In reply, Su addresses, from the comments, some important issues that were not duly emphasised in the paper     

A 0.7-2-GHz self-calibrated multiphase delay-locked loop

A 0.7-2-GHz precise multiphase delay-locked loop (DLL) using a digital calibration circuit is presented. Incorporating with the proposed digital calibration circuit, the mismatch-induced timing error among multiphase clocks in the proposed DLL can be self-calibrated. When the calibration procedure is finished, the digital calibration circuit can be turned off automatically to save power dissipations and reduce noise generations. A start controlled circuit is proposed to enlarge the operating frequency range of the DLL. Both the start-controlled circuit and the calibration circuit require an external reset signal to ensure the correctness of the calibration after temperature,operating frequency, and power supply voltage are settled. This DLL with the digital calibration circuit has been fabricated in a 0.18-/spl mu/m CMOS process. The measured results show the DLL exhibits a lock range of 0.7-2 GHz while the peak-to-peak jitter and rms jitter is 18.9ps and 2.5 ps at 2 GHz, respectively. When the calibration procedure is completed and the DLL operates at 1 GHz, the maximum mismatch-induced timing error among multiphase clocks is reduced from 20.4 ps (7.34 degree) to 3.5 ps (1.26 degree).     

Self-organizing neuro-fuzzy system for control of unknown plants

A cluster-based self-organizing neuro-fuzzy system (SO-NFS) is proposed for control of unknown plants. The neuro-fuzzy system can learn its knowledge base from input-output training data. A plant model is not required for training, that is, the plant is unknown to the SO-NFS. Using new data types, the vectors and matrices, a construction theory is developed for the organization process and the inference activities of the cluster-based SO-NFS. With the construction theory, a compact equation for describing the relation between the input base variables and inference results is established. This equation not only gives the inference relation between inputs and outputs but also specifies the linguistic meanings in the process. New pseudo-error learning control is proposed for closed-loop control applications. Using a cluster-based algorithm, the neuro-fuzzy system in its genesis can be generated by the stimulation of input/output training data to have its initial control policy (IF-THEN rules) for application. With the well-known random optimization method, the generated neuro-fuzzy system can learn its data base for specific applications. The proposed approach can be applied on control of unknown plants, and can levitate the curse of dimensionality in traditional fuzzy systems. Two examples are demonstrated.     

获取(&)空间上专家线性分类判断规则算法的优化方法

本文介绍了基于决策模型"the Moving Basis Heuristics"的在整个决策对象空间上获取专家线性分类判断规则的算法的优化方法.     

Some functional inequalities and inclusion relationships associated with certain families of integral operators

By making use of certain familiar integral operators, the authors introduce and investigate several new subclasses of starlike, convex, close-to-convex, and quasi-convex functions. Among other results presented here, the authors establish a number of inclusion relationships associated with some of these integral operators. Some of the results established in this paper would provide extensions of those given in earlier works.     

一种基于特征向量提取的FMDP模型求解方法

在诸如机器人足球赛等典型的可分解马尔可夫决策过程(factored Markov decision process，简称FMDP)模型中，不同状态属性在不同的状态下，对于状态评估的影响程度是不同的，其中存在若干关键状态属性，能够唯一或近似判断当前状态的好坏．为了解决FMDP模型中普遍存在的“维数灾”问题，在效用函数非线性的情况下，通过对状态特征向量的提取近似状态效用函数，同时根据对FMDP模型的认知程度，从线性规划和再励学习两种求解角度分别进行约束不等式组的化简和状态效用函数的高维移植，从而达到降低计算复杂度，加快联合策略生成速度的目的、以机器人足球赛任意球战术配合为背景进行实验来验证基于状态特征向量的再励学习算法的有效性和学习结果的可移植性．与传统再励学习算法相比，基于状态特征向量的再励学习算法能够极大地加快策略的学习速度．但更重要的是，还可以将学习到的状态效用函数方便地移植到更高维的FMDP模型中，从而直接计算出联合策略而不需要重新进行学习．