现代控制技术在远程机械施工管理中的应用

为了解决野外大型工程远程机械施工流动性强、不易监控调度和管理的问题,应用计算机检测技术、卫星定位技术和移动网络通讯技术,以Visual Basic作为开发语言,通过调度中心和主控计算机对采集到的远程机群数据进行分析处理,对整个远程施工机群进行动态位置和状态参数的显示和集中智能监控调度,达到了远程施工机群的动态监控和智能调度管理.     

沥青混凝土路面机群施工配置技术的现状与发展

1沥青混凝土路面机群施工配置技术研究回顾 在公路机械化施工中,机群施工配置是形成合理的机械化施工能力的基础,受到国内外学者与企业的普遍重视.     

基于智能体的智能机群分层递阶控制研究

为实现工程机械施工机群的智能化,构建了2种人机智能体;提出将分层递阶智能控制应用于施工机群智能化过程中,将施工机群分为3个控制层次,在不同控制层次上用相应智能程度的智能体;并以公路施工机群为实例阐述了智能机群分层递阶控制系统的构成方案。     

沥青转运车的使用与机群性能和效率的变化

沥青混合料转运车在高速公路施工中的广泛应用,必然导致公路施工机群配置技术的相应变革。构建了转运摊铺机组的数学模型并从转运车的储料功能、同摊铺机的柔性连接、对骨料和温度均匀调节作用方面对公路施工中机群性能与效率影响进行分析。     

基于机群智能化施工要求的智能压路机设计

通过分析机群智能化施工对压路机智能化的要求,开发出了可实现无级变频变幅的激振器。在此基础上设计了能自动检测与控制自身技术状态、运行参数、作业质量,并可与机群智能化施工控制中心及其他相关机械进行无线通讯联系的智能压路机。     

西部高原地区沥青混凝土路面施工机群选型研究(8)

针对西部高原地区特点,从机群的静态配置和动态配置2个角度提出了在西部高原地区沥青混凝土路面机械化施工中,进行路面机群施工配置的方法;并分析了配置的原因,使施工机群在适应高原施工的前提下,系统运行相对稳定,效率更高.     

机械化施工机群配置优化问题的研究

对机械化施工机群配置优化问题进行了详细分类，分析了机群配置优化的一般过程，并归纳了机群优化的一般方法。     

西部高原地区沥青混凝土路面施工机群选型研究(6)

结合西部高原环境特点,详细分析了沥青混凝土路面机械化施工中机群施工的工作规律,并建立了数学模型,为进行机群选型配套组合及系统评价提供了理论基础.     

沥青路面施工机群工作参数的合理匹配方法研究

保持恒定连续的摊铺速度是沥青路面施工质量保证的基本要求。确定了路面施工机群参数合理匹配的原则，分析了机群参数合理匹配的图解方法，并给出了压路机的施工工艺参考方案。     

高等级公路路面施工机械最优配置

分析了路面施工机械机群配置间的关系，配置原则和评价指标，利用微机及ＦｏｘＰｒｏ２．６关系型数据库系统，实现路面施工机械机群的最优配置。     

纯电动汽车动力锂电池SOC估计策略综述

电池管理系统(BMS)采用了防止电池过放电和过充,提供电池均衡控制,能够实现新能源汽车动力锂电池的最佳利用和保护。电池管理系统实时精准估算电池电荷状态(SOC)是提高电动汽车续航里程和延长寿命的关键。然而,SOC不能直接测量,动力电池的充、放电又是一个复杂过程,导致目前现有的SOC估算策略很难精确地估算出实时在线SOC值。因此,如何提高SOC估算精度是当下BMS领域的研究热点。本文通过对各种SOC估算方法进行文献综述,分析和总结各个SOC估算方法的原理及优缺点,提出SOC估计策略未来发展趋势。     

公路工程造价快速测算方法及其应用

在总结和分析各种工程造价估测方法的基础上，构建了基于模糊理论和造价管理系统之上的造价快速估测模型，并给出了其计算步骤和估测实例，可用于公路工程造价的快速测算。     

镍氢动力电池SOC估算方法

SOC（State of Charge,电池充电状态）估算是电动汽车电池管理系统的重要功能,准确有效的SOC估算对推动电动汽车核心技术的发展具有重要意义。文章介绍了镍氢电池工作的基本原理及电池管理系统的基本结构等方面技术,阐述了在对电动汽车SOC进行估算的8种方法,并比较各方法在应用中存在的优缺点,指出Ah计量法是目前最常用的方法,且常与其他方法组合使用。     

电动汽车SOC估计算法与电池管理系统的研究

在安时计量方法的基础上,采用基于折算库仑效率的卡尔曼滤波算法估计蓄电池荷电状态(SOC),并将此方法应用于HEV6580混合动力电动汽车镍氢电池管理系统。系统实现的功能包括:数据监测、数据显示、CAN通信、SOC估计、热管理和安全报警。经电池试验台模拟工况试验验证,电池管理系统各子系统达到设计要求且工作稳定。改进SOC估计方法解决了传统安时计量法不能估计初始SOC、难于准确测量库仑效率的问题,为电池管理系统稳定工作提供保证。     

Vehicle velocity estimation for real-time dynamic stability control

A new approach is proposed for nonlinear asymptotic observers based on the cascade observer system with a fusion of sensor signals. In the observers, the characteristic of the vehicle dynamic system, the nonlinear tire force estimation, load transfer estimation, and road ramp angle compensation are considered. The errors in the observation of vehicle velocity were diminished, and the computation cost was decreased for a real-time microcontroller. Simulation and real vehicle test results validate the higher accuracy of the velocity estimation by the proposed observers under complicated handling maneuver conditions.     

Road disturbance estimation for the optimal preview control of an active suspension systems based on tracked vehicle model

This research investigates stochastic estimation of a look-ahead sensor scheme using the optimal preview control for an active suspension system of a full tracked vehicle (FTV). In this scheme, wheel disturbance input to the front wheels are estimated using the dynamic equations of the system. The estimated road disturbance input at the front wheels are utilized as preview information for the control of subsequently following wheels of FTV. The design of optimal preview control is used as a classical linear quadratic Gaussian problem by combining dynamics of the original system and estimation of previewed road inputs. The effectiveness of the preview controller is evaluated by comparing the estimated information with the measured information for different road profiles, where Kalman filter is used for the state-variables estimation of the FTV. This research also considers the reduced order estimation using commonly available sensors in order to decrease the number of sensors and measurements. The simulation results’ using an active suspension system with different preview information shows that the proposed system can be beneficial for the improvement of ride comfort of tracked vehicles without using any specialized sensors for preview information calculation.     

考虑动力学模型系统误差补偿的智能车GNSS/IMU组合定位算法

为了解决智能车动态组合定位过程中，因动力学模型与实际模型之间存在偏差导致滤波精度下降的问题，针对智能车全球导航卫星系统(GNSS)/惯性测量单元(IMU)组合定位系统，结合非线性预测滤波(NPF)和自适应滤波的优点，提出了一种考虑动力学模型系统误差实时估计和补偿的自适应非线性预测滤波(ANPF)算法。首先，根据NPF算法原理，通过最小化预测观测残差与系统误差的加权平方和，估计动力学模型系统误差；其次，结合自适应滤波原理，利用状态预测残差向量构造自适应因子，设计了一种自适应扩展卡尔曼滤波(AEKF)算法，用于估计系统状态向量，并通过自适应因子抑制动力学模型系统误差和线性化误差对系统状态估计精度的影响，克服NPF对系统状态估计精度有限的缺陷；再次，对动力学模型系统误差的估计误差和由动力学模型系统误差引起的系统噪声的等效协方差阵进行了分析和推导，以补偿动力学模型系统误差对系统状态估计的影响；最后，通过车载GNSS/IMU组合定位系统试验，从算法精度、鲁棒性和实时性方面对提出的算法和其他滤波算法的性能进行了验证和对比分析。研究结果表明:提出的自适应算法继承了NPF算法简易性和高实时性的优点，同时克服了NPF算法估计精度有限的缺陷，具有较好的滤波解算精度，水平定位精度小于1.0 m，算法单次平均执行时间约为0.013 9 ms，在精度和实时性的平衡方面显著优于其他滤波方法。     

EPS中更精确的电机转速估计方法研究

电动助力转向系统中需要用到准确的助力电机转速信号,因此本文提出一种变内阻变反电动势系数的助力电机转速估计方法.通过测试直流电机内阻和反电动势系数随电机电流的变化曲线,从而得到电机转速更精确的估计表达式.通过对比试验验证可知,本文提出的电机转速估计方法能够显著提高估计精度.     

Neural-network multiple models filter (NMM)-based position estimation system for autonomous vehicles

A highly accurate and reliable vehicle position estimation system is an important component of an autonomous driving system. In generally, a global positioning system (GPS) receiver is employed for the vehicle position estimation of autonomous vehicles. However, a stand-alone GPS does not always provide accurate and reliable information of the vehicle position due to frequent GPS blockages and multipath errors. In order to overcome these problems, a sensor fusion scheme that combines the data from the GPS receiver and several on-board sensors has been studied. In previous researches, a single model filter-based sensor fusion algorithm was used to integrate information from the GPS and on-board sensors. However, an estimate obtained from a single model is difficult to cover the various driving environments, including urban areas, off-road areas, and highways. Thus, a multiple models filter (MMF) has been introduced to address this limitation by adapting multiple models to a wide range of driving conditions. An adaptation of the multiple model is achieved through the use of the model probability. The MMF combines several vehicle models using the model probabilities, which indicate the suitability of the current driving condition. In this paper, we propose a vehicle position estimation algorithm for an autonomous vehicle that is based on a neural network (NN)-based MMF. The model probabilities are determined through the NN. The proposed position estimation system was evaluated through simulations and experiments. The experimental results show that the proposed position estimation algorithm is suitable for application in an autonomous driving system over a wide range of driving conditions.     

Suspension system state estimation using adaptive Kalman filtering based on road classification

This paper provides a new method to solve the problem of suspension system state estimation using a Kalman Filter (KF) under various road conditions. Due to the fact that practical road conditions are complex and uncertain, the influence of the system process noise variance and measurement noise covariance on the estimation accuracy of the KF is first analysed. To accurately estimate the road condition, a new road classification method through the vertical acceleration of sprung mass is proposed, and different road process variances are obtained to tune the system’s variance for the application of the KF. Then, road classification and KF are combined to form an Adaptive Kalman Filter (AKF) that takes into account the relationship of different road process noise variances and measurement noise covariances under various road conditions. Simulation results show that the proposed AKF algorithm can obtain a high accuracy of state estimation for a suspension system under varying International Standards Organisation road excitation levels.