Seat Comfort. M4-M6.

Hardness is defined as the resistance and load bearing capability of an item. Seat hardness is an important factor in seat comfort as it impacts on a number of variables including seat postural stability, postural control, pressure comfort as a result of tissue deformation, and occupant vibration. The development of the test rig further on described in this report will enable Futuris Automotive to develop their current comfort testing procedures and thus increase the comfort of their automotive seats. The test rig consists of a buttock indenter, which produces a controlled application of a load to a seat cushion with measured displacement via a linear indenter. In parallel with the physical property presented, an analytic (software) finite element tool was developed to simulate seat pressure in an ANSYS Workbench V13 environment. This report also details the procedure required for Futuris to accurately and precisely measure cushion hardness which will enhance their comfort testing procedures, product development and target settings. The report is divided into three main sections: 1 Test equipment specification (M4) - A detailed description of the process used to build the seat cushion indenter and a description of the indenter mechanical structure and electrical functionality (chapter 2). 2 Analytic tool specification (M5) – A detailed description of the CAE seat and indenter software tool, developed as a finite element model (FEM) under ANSYS Workbench V13 to simulate indentation of a physical seat cushion similar to the hardware tool (chapter 3). 3 Product Development and Comfort Design Procedure (M6) - The cushion hardness testing procedure to be used with the physical indenter. This milestone is partially incomplete, as it covers a description of the test procedure to be applied, however not the operating system (control software) required to operate the physical property (chapter 4). Although outside the scope of this project, this report also details the testing procedures required to measure overall seatback hardness.

Temperature and composition profile during double-track laser cladding of H13 tool steel

Multi-track laser cladding is now applied commercially in a range of industries such as automotive, mining and aerospace due to its diversified potential for material processing. The knowledge of temperature, velocity and composition distribution history is essential for a better understanding of the process and subsequent microstructure evolution and properties. Numerical simulation not only helps to understand the complex physical phenomena and underlying principles involved in this process, but it can also be used in the process prediction and system control. The double-track coaxial laser cladding with H13 tool steel powder injection is simulated using a comprehensive three-dimensional model, based on the mass, momentum, energy conservation and solute transport equation. Some important physical phenomena, such as heat transfer, phase changes, mass addition and fluid flow, are taken into account in the calculation. The physical properties for a mixture of solid and liquid phase are defined by treating it as a continuum media. The velocity of the laser beam during the transition between two tracks is considered. The evolution of temperature and composition of different monitoring locations is simulated.

Learning Multirobot Hose Transportation and Deployment by Distributed Round-Robin Q-Learning

Multi-Agent Reinforcement Learning (MARL) algorithms face two main difficulties: the curse of dimensionality, and environment non-stationarity due to the independent learning processes carried out by the agents concurrently. In this paper we formalize and prove the convergence of a Distributed Round Robin Q-learning (D-RR-QL) algorithm for cooperative systems. The computational complexity of this algorithm increases linearly with the number of agents. Moreover, it eliminates environment non sta tionarity by carrying a round-robin scheduling of the action selection and execution. That this learning scheme allows the implementation of Modular State-Action Vetoes (MSAV) in cooperative multi-agent systems, which speeds up learning convergence in over-constrained systems by vetoing state-action pairs which lead to undesired termination states (UTS) in the relevant state-action subspace. Each agent's local state-action value function learning is an independent process, including the MSAV policies. Coordination of locally optimal policies to obtain the global optimal joint policy is achieved by a greedy selection procedure using message passing. We show that D-RR-QL improves over state-of-the-art approaches, such as Distributed Q-Learning, Team Q-Learning and Coordinated Reinforcement Learning in a paradigmatic Linked Multi-Component Robotic System (L-MCRS) control problem: the hose transportation task. L-MCRS are over-constrained systems with many UTS induced by the interaction of the passive linking element and the active mobile robots.

Medaka vasa is required for migration but not survival of primordial germ cells

Vasa is essential for germline development. However, the precise processes in which vasa involves vary considerably in diverse animal phyla. Here we show that vasa is required for primordial germ cell (PGC) migration in the medakafish. vasa knockdown by two morpholinos led to the PGC migration defect that was rescued by coinjection of Vasa RNA. Interestingly, Vasa knockdown did not alter the PGC number, identity, proliferation and motility even at ectopic locations. We established a cell culture system for tracing PGCs at the single cell level in vitro. In this culture system, control and morpholino-injected gastrulae produced the same PGC number and the same time course of PGC survival. importantly, vasa-depleted PGCs in culture had similar motility and locomotion to normal PGCs. Expression patterns of wt1a, sdf1b and cxcT4b in migratory tissues remained unchanged by Vasa knockdown. By chimera formation we show that PGCs from vasa-depleted blastulae failed to migrate properly in the normal environment, whereas control PGCs migrated normally in vasa-disrupted embryos. Furthermore, ectopic PGCs in vasa-depleted embryos also retained all the PGC properties examined. Taken together, medaka vasa is cell-autonomously required for PGC migration, but dispensable to PGC proliferation, motility, identity and survival. (C) 2009 Elsevier Ireland Ltd. All rights reserved.

S／T曲线速度规划在定点DSP上的实现

本文将S／T曲线速度规划的思想引入全数字伺服驱动系统中,通过提高速度的平滑性,特别是高速启、制动状态,来提高伺服系统的整体控制性能。基于定点数字信号处理器DSP芯片对提出的算法进行了实现。由于定点运算的限制,算法在实现中需要进行特殊的处理,本文对此进行了研究,并提出了一种余码补偿方案。实验研究表明,使用本文提出的方法可以提高系统运行的平稳性和控制的精确度。

模糊免疫非线性PID控制的优化设计

在深入分析生物免疫系统中T细胞对B细胞辅助调节作用的基础上,提出了免疫反馈原理。针对非最小相位极点系统控制的难点,借鉴免疫反馈原理,结合积分控制的规律,提出了一种模糊免疫非线性PID控制方法。由于该方法中的参数确定比较复杂,利用免疫进化算法进行参数优化设计,实现了控制参数的合理设计。仿真结果表明,该方法在非最小相位极点系统控制中可行且有效,优于PID控制方法,具有更好的响应特性和抗干扰性能。

基于ARM的智能型定氮仪的研制

通过对凯氏定氮法和整个系统控制原理的分析,介绍了一种基于ARM的测定物质中氮元素含量的系统,该系统采用ARM微控制器LPC2214作为整个系统的控制核心,通过步进电机驱动进行标准溶液的滴定和光电检测确定滴定终点,形成闭环系统,实现了准确测定物质中氮元素的含量。文中给出了系统软硬件设计方案,对其实现方法及关键技术进行了研究。实验结果表明:该系统工作稳定,测量数据准确,具有一定的应用价值。

基于QNX实时系统的行为辅助机器人控制系统

论文设计研制了行为辅助机器人验证平台,以此平台为基础可以针对行为辅助机器人进行力控制研究。针对机器人力控制的实际要求,设计了行为辅助机器人柔性关节结构,辨识了系统参数。基于QNX实时操作系统设计了控制系统软件。机器人控制系统软件主要包括传感器数据采集和控制算法两部分,可以满足柔性关节控制需求。

面向实时控制任务的高速数据采集存储方法

提出一种基于CAN总线,面向实时控制任务的高速数据采集存储方法.该方法将数据实时采集、系统实时控制、数据存储与显示分配在相互独立的处理单元中实现,处理单元之间通过独立的CAN网络进行通信.该数据采集方法解决了数据实时采集与系统实时控制之间的矛盾.利用队列技术,该方法解决了数据存储与数据实时显示之间的矛盾.该数据采集存储方法已成功应用到某大型仿真系统中.

轮式移动宜人机器人技术研究

轮式移动宜人机器人项目研究的主要目的是开发自主式仿人机器人样机 ,探索先进的机器人理论和技术。轮式移动宜人机器人由正交轮式移动平台、腰部、躯干及头部和双臂组成 ,共 2 1个自由度。整体结构包括 :电源系统、机械系统、控制系统和传感系统。电源系统采用车载电池供电。机械系统包括变刚度结构 ,提高了机器人与人交互作业的安全性。控制系统分为中央协调层和执行层结构。传感系统主要实现关节位置检测、姿态检测、力检测和视觉。文章讨论了此机器人的研究进展。

分布式过程控制系统在大电流稳流控制中的应用

本文介绍了一个用于供电系统的ＤＰＣＳ，以及智能控制─实时专家系统控制在ＤＰＣＳ中的应用、特点和实现。

Voltage Sensing Using an Asynchronous Charge-to-Digital Converter for Energy-Autonomous Environments

In future systems with relatively unreliable and unpredictable energy sources such as harvesters, the system power supply may become non-deterministic. For energy effective operations, Vdd is an important parameter in any meaningful system control mechanism. Reliable and accurate on-chip voltage sensors are therefore indispensible for the power and computation management of such systems. Existing voltage sensing methods are not suitable because they usually require a stable and known reference (voltage, current, time, frequency, etc.), which is difficult to obtain in this environment. This paper describes an autonomous reference-free voltage sensor designed using an asynchronous counter powered by the charge on a capacitor and a small controller. Unlike existing methods, the voltage information is directly generated as a digital code. The sensor, fabricated in the 180 nm technology node, was tested successfully through performing measurements over the voltage range from 1.8 V down to 0.8 V.

Managing Future offshore wind power variability in a European Supergrid

The future European power system will have a hierarchical structure created by layers of system control from a Supergrid via regional high-voltage transmission through to medium and low-voltage distribution. Each level will have generation sources such as large-scale offshore wind, wave, solar thermal, nuclear directly connected to this Supergrid and high levels of embedded generation, connected to the medium-voltage distribution system. It is expected that the fuel portfolio will be dominated by offshore wind in Northern Europe and PV in Southern Europe. The strategies required to manage the coordination of supply-side variability with demand-side variability will include large scale interconnection, demand side management, load aggregation and storage in the context of the Supergrid combined with the Smart Grid. The design challenge associated with this will not only include control topology, data acquisition, analysis and communications technologies, but also the selection of fuel portfolio at a macro level. This paper quantifies the amount of demand side management, storage and so-called 'back-up generation' needed to support an 80% renewable energy portfolio in Europe by 2050. © 2013 IEEE.