随机横浪中船舶倾覆过程模拟与安全域计算

研讨了随机海浪中船舶非线性随机横摇运动倾覆过程和安全域问题.考虑非线性阻尼、非线性复原力矩和随机波浪,建立了随机横浪中船舶运动的随机非线性微分方程.考虑两参数海浪谱和不同的有义波高,计算随机波浪力矩,采用简谐加速度数值计算方法求解随机微分方程,进行了随机倾覆过程的数值模拟.考虑船舶运动的瞬时状态,发展了胞映射方法计算随机非线性横摇运动安全域的方法,并编制了计算机程序.模拟了一条渔船不同有义波高作用下横摇倾覆过程,考虑不同的初始条件及随机波浪参数,在初始值平面上采用胞映射法构造了渔船航行的安全域.波高较小时,安全域是一个连续的区域;随着有义波高增大,安全域面积减小并且不再连续,倾覆域和安全域相互包围.计算结果表明,船舶运动的瞬时状态对安全域具有重要影响,而且随机海浪中安全域具有随机特性.     

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

A number of driver models were fitted to a large data set of human truck driving, from a simulated near-crash, low-friction scenario, yielding two main insights: steering to avoid a collision was best described as an open-loop manoeuvre of predetermined duration, but with situation-adapted amplitude, and subsequent vehicle stabilisation could to a large extent be accounted for by a simple yaw rate nulling control law. These two phenomena, which could be hypothesised to generalise to passenger car driving, were found to determine the ability of four driver models adopted from the literature to fit the human data. Based on the obtained results, it is argued that the concept of internal vehicle models may be less valuable when modelling driver behaviour in non-routine situations such as near-crashes, where behaviour may be better described as direct responses to salient perceptual cues. Some methodological issues in comparing and validating driver models are also discussed.     

Driver models for personalised driving assistance

We propose a learning-based driver modelling approach which can identify manoeuvres performed by drivers on the highway and predict the future driver inputs. We show how this approach can be applied to provide personalised driving assistance. In a first example, the driver model is used to predict unintentional lane departures and a model predictive controller is used to keep the car in the lane. In a second example, the driver model estimates the preferred acceleration of the driver during lane keeping, and a model predictive controller is implemented to provide a personalised adaptive cruise control. For both applications, we use a combination of real data and simulation to evaluate the proposed approaches.     

驾驶人驾驶决策机制遵循最小作用量原理

为提升智能汽车的自主决策能力，使其能够学习人的决策智慧以适应复杂多变的道路交通环境，需要揭示驾驶人决策机制。首先通过对自然驾驶数据的分析，发现在车辆行驶过程中能够反映驾驶人决策行为的主要运动特征参数存在极值现象，而产生极值现象的内在动因是驾驶人遵循“趋利避害”的基本决策机制，即驾驶过程中驾驶人力图实现机动性和安全性综合性能最优。受自然界包括物理和生物行为上的众多极值现象遵循最小作用量原理的启发，提出驾驶人决策机制遵循最小作用量原理的假设。随后建立抽象描述驾驶过程的物理模型，并提出最小作用量决策模型（Least Action Decision-making Model，LADM），通过与传统驾驶决策模型（经典跟车模型和换道模型）对比，分析结果显示LADM模型更具通用性。最后开展了实车试验，采集20名驾驶人在自由行驶、跟车行驶和邻车切入3种工况下的试验数据，分析计算并检验了不同驾驶人行车过程的理论最小作用量和实际作用量。试验结果表明：驾驶人在驾驶过程中的实际作用量与最小作用量之间无显著性差异，体现出驾驶人在行车过程中对安全和高效具有共性追求，验证了驾驶人决策机制遵循最小作用量原理。     

基于通信的列车控制系统移动数据通信关键问题的探讨

首先分析了基于通信的列车控制(Communication Based Train Control,CBTC)系统移动数据通信的现状、特点、要求;然后分析了无线数据平台使用的技术:直接序列扩频、全双工、时分复用多址、码分复用多址、差错控制、移动性管理、数据通信协议;最后分析了CBTC信息内容和数据安全传输方法。     

高速公路昼夜行车的部分特性指标对比分析

以行车试验为基础，通过与白天行车状况的比较，探索夜间在高速公路行车时，在直线路段上车速与驾驶员心率之间的变化特点以及在平曲线路段上车速与半径之间的变化特点。     

珊瑚礁岩土地区打入式钢管桩沉桩规律

针对珊瑚礁岩土复杂工程特性所带来的钢管桩沉桩收锤标准难确定、沉桩施工高程难统一等难题,研究了珊瑚礁岩土地区钢管桩沉桩规律。采用统计分析方法对钢管桩在珊瑚礁岩土中的终锤贯入度、沉桩锤击总数以及入土深度进行对比。结合现场试验测试和可打性分析,指出打入式钢管桩易穿透珊瑚礁岩土、所需锤击总数偏少的主要原因是受珊瑚礁岩土软硬分布和土颗粒破碎影响。研究结果表明:钢管桩可以穿透高标贯击数(100击以上)的珊瑚礁岩土;常规的工程设计施工经验直接应用于珊瑚礁岩土容易造成较大的偏差,应通过现场载荷试验校核。     

打桩施工对邻近河道的影响

打桩施工过程会对周围建筑物及环境产生一定影响。对打桩施工的影响机理及沉桩过程模拟进行理论分析,并结合上海地区河道陆域范围内某新建厂房项目,运用孔穴扩张理论及动力有限元法,计算分析打桩施工对邻近河道的影响。动力有限元的计算结果表明,打桩施工对河道护岸影响较小,且与孔穴扩张理论的计算结论基本一致。同时提出预防和减轻打桩施工影响的设计、施工、监测措施,可为类似工程提供借鉴。     

本安电缆在海洋工程中的应用研究

目前海洋工程项目存在对本安电缆特性认识不足、本安电缆施工不够规范等问题,基于理论用于指导施工的原则,文章对本安电缆的防爆原理和性能特点进行分析研究,给出了确定最大实际敷设长度的理论方法,以及敷设基本原则,这对本安电缆施工具有指导意义,将使本安电缆在海洋工程中的应用更加科学规范。     

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

Modelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.