洁净机器人手臂的转动惯量分析及运动控制

针对洁净机器人手臂的转动惯量对系统动态性能的影响,在利用动能公式分析得到其转动惯量与位置关系的基础上,提出了一种位置PI闭环加前馈参数整定的控制方法。该控制方法是由位置值实时得到转动惯量,再由转动惯量来实时整定PI参数。仿真结果表明了该控制方法能有效抑制转动惯量的变化对系统动态性能的影响,且简单可行。

平面自由运动半物理仿真系统的初始条件实现

提出了一种平面自由运动半物理仿真系统的设计,对系统仿真试验的初始条件形成过程进行了具体阐述。系统中应用了一种平面两自由度直角坐标运动装置,用于完成系统驱动及速度和位置控制,同时这种运动装置能够进行直角坐标跟随运动,并与其末端测量机构共同实现对平面自由运动物体的高精度大范围位姿测量。建立了系统的运动学模型,并设计相应的控制算法实现试验所需的运动过程,采用半闭环和闭环相结合的方法有效控制系统的末端累积误差;针对提出的复合测量方法,建立了测量原理的数学模型,并进行了精度分析和仿真计算。实验证明这种设计和相应的控制测量方法合理可行。

MWCNT操作的动力学运动学建模研究

为实现MWCNT操作运动过程的视觉显示,本文建立了MWCNT的动力学模型,据此可推导出推动MWCNT所需施加力的大小,并根据探针的实际受力判断其能否运动;同时还建立了MWCNT的运动学模型,根据探针的实际位置可获得探针操作下MWCNT的新位置与姿态,并借助虚拟现实技术对视觉界面进行实时更新,实现了MWCNT运动过程的实时视觉显示。基于上述视觉显示,操作者可在线控制探针的作用位置与运动轨迹、以及施加在探针上作用力的大小与方向,实现对MWCNT操作过程及结果的在线控制。MWCNT的操作实验初步验证了该模型的有效性。

一种五自由度机电测控系统的设计与实现

文章提出了一种基于五自由度机电系统的测控系统设计方法,并从机械系统构成、测控系统结构及软硬件设计方面论述了系统实现技术。针对系统特定的功能要求,文章详细介绍了位姿组合测量和各位姿自由度控制的测控方法,并应用模块化设计和数据流分析方法进行软硬件设计。通过实验进行系统特性分析,得到运行参数指标。实验证明这种设计方法和实现技术合理可行。

基于超短基线的缆控水下机器人动力定位

动力定位 (DynamicPositioning(DP) )技术是水下机器人的关键技术之一。因此针对当前动力定位主要在缆控水下机器人 (ROV)中应用的情况 ,给出了ROV动力定位技术的实施方法。通过声学定位技术确定ROV的坐标 ,计算出与期望位姿的差 ,将其作为神经网络控制器的输入量来控制ROV ,从而进行动力定位。同时还重点研究了ROV动力定位中的主要研究内容即水声定位技术和定位控制技术的构建。

基于神经网络的一类非线性系统模型参考控制

针对一类非线性系统,提出了一种神经网络模型参考控制方案。在训练实现对象模型的网络和实现控制器的网络时,由状态方程产生训练样本。通过对倒立摆系统的仿真实验验证了控制方案和样本生成策略的有效性,在仿真实验中用不同初始状态验证了训练后的神经网络的泛化能力。

六功能水下机械手的变结构控制方法和应用研究

随着各国对海洋资源开发利用的加剧，水下机械手作为作业型水下机器人首选配置的一种通用作业工具，应用范围不断扩展，而现有的水下机械手采用主从控制方式，在水下复杂的作业环境下精度及效率不高，限制了水下机械手的使用。引入监控模式的机械手系统是水下机械手发展的一个主流方向。本课题就是以实现机械手的监控模式为目标，针对在实现监控式机械手过程中遇到的两个鱼待解决的难题：机械手的实时运动学逆解和复杂条件下满足高精度轨迹跟踪要求的控制方法，以六功能水下机械手为对象展开并完成课题工作。本文的主要研究成果分为以下三个部分：针对此机械手运动学逆解不能简单由解析方式给出的特点，本文提出运用基于约束最优化的变尺度算法。运用惩罚函数和二次插值选取迭代步长，保证了搜索的有效性；没有直接求解二阶导数，不存在奇异解。该算法是超线性收敛。对实例的求解证明了该算法的快速性和在解决此水下机械手在线轨迹规划时的有效。针对现有的水下机械手关节位置控制中采用PID控制算法而存在的一些缺点，本文以电液位置伺服的肩转关节为例，进行了滑模控制方法的研究。设计了单关节的滑模控制器，并把简化后的滑模控制器运用到实际的控制系统中，仿真和实验结果表明在复杂条件下滑模控制与PID控制相比有高精度和快速的跟踪性能。针对滑模控制的“颤振”现象，引入模糊控制的思想，设计了单关节的模糊滑模控制器，仿真和最终的实验证明了运用模糊滑模控制能有效消弱“颤振”，系统有良好的跟踪性能。针对原有的控制系统不适合于监控方式的机械手控制的特点，重新设计了水下机械手的水下控制器，组建了基于RS－485通讯，以PC机为上位机系统和89C52为下位机系统的机械手两级控制系统，并在这个人机界面友好、工作稳定的系统上完成了本文所有的实验。

基于运动速度的机器人学习控制

为了使机器人跟踪给定的期望轨线，提出了一种新的基于机器人运动重复性的学习控制法．在这种方法中机器人通过重复试验得到期望运动，这种控制法的优点：一是对于在期望运动附近非线性机器人动力学的近似表达式的线性时变机械系统产生期望运动的输入力矩可不由估计机器人动力学的物理参数形成；二是可以适当的选择位置、速度和加速度反馈增益矩阵，从而加快误差收敛速度；三是加入了加速度反馈，减少了速度反馈，减少了重复试验的次数．这是因为在每次试验的初始时刻不存在位置和速度误差，但存在加速度误差．另外，这种控制法的有效性通过ＰＵＭＡ５６２机器人的前三个关节的计算机仿真结果得到验证。

一个简单的机器人视觉引导装配实验系统

本文对视觉控制下的一个简单实验室装配系统作了介绍,讨论了系统组成、机器人控制、二维图象特征的提取、对物体自动识别、定位定向、系统标定、实现垒积木装配工作.本实验系统用的是我所研制的国内第一台示教再现机器人.

Neural Models of Motion Integration, Segmentation, and Probablistic Decision-Making

When brain mechanism carry out motion integration and segmentation processes that compute unambiguous global motion percepts from ambiguous local motion signals? Consider, for example, a deer running at variable speeds behind forest cover. The forest cover is an occluder that creates apertures through which fragments of the deer's motion signals are intermittently experienced. The brain coherently groups these fragments into a trackable percept of the deer in its trajectory. Form and motion processes are needed to accomplish this using feedforward and feedback interactions both within and across cortical processing streams. All the cortical areas V1, V2, MT, and MST are involved in these interactions. Figure-ground processes in the form stream through V2, such as the seperation of occluding boundaries of the forest cover from the boundaries of the deer, select the motion signals which determine global object motion percepts in the motion stream through MT. Sparse, but unambiguous, feauture tracking signals are amplified before they propogate across position and are intergrated with far more numerous ambiguous motion signals. Figure-ground and integration processes together determine the global percept. A neural model predicts the processing stages that embody these form and motion interactions. Model concepts and data are summarized about motion grouping across apertures in response to a wide variety of displays, and probabilistic decision making in parietal cortex in response to random dot displays.

The VITEWRITE Model of Handwriting Production

This article describes the VITEWRITE model for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in the outflow command to a given synergy occurs. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. Each synergy exhibits a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

The What-And-Where Filter: A Spatial Mapping Neural Network for Object Recognition and Image Understanding

The What-and-Where filter forms part of a neural network architecture for spatial mapping, object recognition, and image understanding. The Where fllter responds to an image figure that has been separated from its background. It generates a spatial map whose cell activations simultaneously represent the position, orientation, ancl size of all tbe figures in a scene (where they are). This spatial map may he used to direct spatially localized attention to these image features. A multiscale array of oriented detectors, followed by competitve and interpolative interactions between position, orientation, and size scales, is used to define the Where filter. This analysis discloses several issues that need to be dealt with by a spatial mapping system that is based upon oriented filters, such as the role of cliff filters with and without normalization, the double peak problem of maximum orientation across size scale, and the different self-similar interpolation properties across orientation than across size scale. Several computationally efficient Where filters are proposed. The Where filter rnay be used for parallel transformation of multiple image figures into invariant representations that are insensitive to the figures' original position, orientation, and size. These invariant figural representations form part of a system devoted to attentive object learning and recognition (what it is). Unlike some alternative models where serial search for a target occurs, a What and Where representation can he used to rapidly search in parallel for a desired target in a scene. Such a representation can also be used to learn multidimensional representations of objects and their spatial relationships for purposes of image understanding. The What-and-Where filter is inspired by neurobiological data showing that a Where processing stream in the cerebral cortex is used for attentive spatial localization and orientation, whereas a What processing stream is used for attentive object learning and recognition.

The Hippocampus and Cerebellum in Adaptively Timed Learning, Recognition, and Movement

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensory-motor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contain circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar timing controls the release of conditioned responses. This property is part of the model's description of how cognitive-emotional interactions focus attention on motivationally valued cues, and how this process breaks down due to hippocampal ablation. The model suggests that the hippocampal mechanisms that help to rapidly draw attention to salient cues could prematurely release motor commands were not the release of these commands adaptively timed by the cerebellum. The model hippocampal system modulates cortical recognition learning without actually encoding the representational information that the cortex encodes. These properties avoid the difficulties faced by several models that propose a direct hippocampal role in recognition learning. Learning within the model hippocampal system controls adaptive timing and spatial orientation. Model properties hereby clarify how hippocampal ablations cause amnesic symptoms and difficulties with tasks which combine task delays, novelty detection, and attention towards goal objects amid distractions. When these model recognition, reinforcement, sensory-motor, and timing processes work together, they suggest how the brain can accomplish conditioning of multiple sensory events to delayed rewards, as during serial compound conditioning.

A Neural Network Model for Cursive Script Production

This article describes a neural network model, called the VITEWRITE model, for generating handwriting movements. The model consists of a sequential controller, or motor program, that interacts with a trajectory generator to move a. hand with redundant degrees of freedom. The neural trajectory generator is the Vector Integration to Endpoint (VITE) model for synchronous variable-speed control of multijoint movements. VITE properties enable a simple control strategy to generate complex handwritten script if the hand model contains redundant degrees of freedom. The proposed controller launches transient directional commands to independent hand synergies at times when the hand begins to move, or when a velocity peak in a given synergy is achieved. The VITE model translates these temporally disjoint synergy commands into smooth curvilinear trajectories among temporally overlapping synergetic movements. The separate "score" of onset times used in most prior models is hereby replaced by a self-scaling activity-released "motor program" that uses few memory resources, enables each synergy to exhibit a unimodal velocity profile during any stroke, generates letters that are invariant under speed and size rescaling, and enables effortless. connection of letter shapes into words. Speed and size rescaling are achieved by scalar GO and GRO signals that express computationally simple volitional commands. Psychophysical data concerning band movements, such as the isochrony principle, asymmetric velocity profiles, and the two-thirds power law relating movement curvature and velocity arise as emergent properties of model interactions.

Resonant power conversion topologies for inductive charging of electrical vehicle batteries

This thesis is concerned with inductive charging of electric vehicle batteries. Rectified power form the 50/60 Hz utility feeds a dc-ac converter which delivers high-frequency ac power to the electric vehicle inductive coupling inlet. The inlet configuration has been defined by the Society of Automotive Engineers in Recommended Practice J-1773. This thesis studies converter topologies related to the series resonant converter. When coupled to the vehicle inlet, the frequency-controlled series-resonant converter results in a capacitively-filtered series-parallel LCLC (SP-LCLC) resonant converter topology with zero voltage switching and many other desirable features. A novel time-domain transformation analysis, termed Modal Analysis, is developed, using a state variable transformation, to analyze and characterize this multi-resonant fourth-orderconverter. Next, Fundamental Mode Approximation (FMA) Analysis, based on a voltage-source model of the load, and its novel extension, Rectifier-Compensated FMA (RCFMA) Analysis, are developed and applied to the SP-LCLC converter. The RCFMA Analysis is a simpler and more intuitive analysis than the Modal Analysis, and provides a relatively accurate closed-form solution for the converter behavior. Phase control of the SP-LCLC converter is investigated as a control option. FMA and RCFMA Analyses are used for detailed characterization. The analyses identify areas of operation, which are also validated experimentally, where it is advantageous to phase control the converter. A novel hybrid control scheme is proposed which integrates frequency and phase control and achieves reduced operating frequency range and improved partial-load efficiency. The phase-controlled SP-LCLC converter can also be configured with a parallel load and is an excellent option for the application. The resulting topology implements soft-switching over the entire load range and has high full-load and partial-load efficiencies. RCFMA Analysis is used to analyze and characterize the new converter topology, and good correlation is shown with experimental results. Finally, a novel single-stage power-factor-corrected ac-dc converter is introduced, which uses the current-source characteristic of the SP-LCLC topology to provide power factor correction over a wide output power range from zero to full load. This converter exhibits all the advantageous characteristics of its dc-dc counterpart, with a reduced parts count and cost. Simulation and experimental results verify the operation of the new converter.