大视觉智能巡线车控制系统开发

针对常规的智能车传感器固定,视觉范围小,影响智能车的运行速度和准确性问题,提出了基于双舵机的大视觉智能巡线车控制系统。采用激光传感器、将路径识别传感器安装在车架上随动舵机的轮盘上,MCU通过控制随动摆头舵机来带动传感器的运动,使智能车的路径识别角度范围增大。经试验证明,此控制系统紧密结合双舵机的特点,充分发挥装在车架上的随动舵机带动路径识别传感器的优势,增大了智能车巡线过程中能识别到的路径范围,使智能车模实现高速稳定行驶。在不"丢线"的情况下,速度得到较大的提升,能适应各种赛道。     

基于SD卡的智能车调试系统设计

介绍了一种基于SD卡的摄像头智能车调试系统。摄像头智能车数据量大。利用SD卡将小车在实际跑的过程中的图像信息、状态信息和控制信息储存下来供离线分析,捕捉到了很多错误,为小车在跑道上平稳快速的运行提供了保障。     

基于红外反射式传感器的循迹车系统设计

采用红外反射式传感器设计了一种循迹车系统。硬件方面,介绍了红外循迹电路、电机驱动电路和测温模块的设计方法;软件方面,给出了循迹模块算法和目标物识别算法。经实验验证,循迹车能在黑色引导线上稳定可靠地运行,并在偏离导线时自动纠偏。     

Robust MIMO disturbance observer analysis and design with application to active car steering

A multi‐input–multi‐output extension of the well‐known two control degrees‐of‐freedom disturbance observer architecture that decouples the problem into single‐input–single‐output disturbance observer loops is presented in this paper. Robust design based on mapping D‐stability and the frequency domain specifications of weighted sensitivity minimization and phase margin bound to a chosen controller parameter space is presented as a part of the proposed design approach. The effect of the choice of disturbance observer Q filter on performance is explained with a numerical example. This is followed by the use of structured singular values in the robustness analysis of disturbance observer controlled systems subject to structured, real parametric and mixed uncertainty in the plant. A design and simulation study based on a four wheel active car steering control example is used to illustrate the methods presented in the paper. Copyright © 2009 John Wiley & Sons, Ltd.     

基于PIC单片机的智能循迹小车设计

介绍了一种基于PIC单片机的智能循迹小车的硬件和软件设计。该智能循迹小车以PIC16F877A单片机为主控芯片,采用单光束红外光电传感器RPR221作为检测元件、恒压恒流桥式驱动芯片L298N作为小车驱动芯片,使小车能按预定的轨道稳定地行驶,能正确地识别路径、避障,速度和路程的显示较准确,具有一定的抗干扰能力。     

基于模糊控制的轿车空调温度控制系统研究

轿车空调是一个复杂的热力系统,由鼓风机、循环风门、混合风门、蒸发器和加热器等部分构成,其热负荷主要包括太阳辐射、车室内外温差、乘客热负荷和车速等.由热力学原理建立轿车空调系统的非线性模型,提出一种基于模糊推理的控制方法来实现轿车空调系统的温度控制,并利用型号为N15的轿车空调系统的参数进行Matlab仿真.仿真结果表明...     

Predictive models for emission of hydrogen powered car using various artificial intelligent tools

This paper investigates the use of artificial intelligent models as virtual sensors to predict relevant emissions such as carbon dioxide, carbon monoxide, unburnt hydrocarbons and oxides of nitrogen for a hydrogen powered car. The virtual sensors are developed by means of application of various Artificial Intelligent (AI) models namely; AI software built at the University of Tasmania, back-propagation neural networks with Levenberg–Marquardt algorithm, and adaptive neuro-fuzzy inference systems. These predictions are based on the study of qualitative and quantitative effects of engine process parameters such as mass airflow, engine speed, air-to-fuel ratio, exhaust gas temperature and engine power on the harmful exhaust gas emissions. All AI models show good predictive capability in estimating the emissions. However, excellent accuracy is achieved when using back-propagation neural networks with Levenberg–Marquardt algorithm in estimating emissions for various hydrogen engine operating conditions with the predicted values less than 6% of percentage average root mean square error.     

基于Arduino控制板的数据采集智能小车的控制系统设计与实现

为实现具有环境数据采集功能的智能小车控制,设计并实现了基于Ardu-no控制板的控制系统。选用Arduino Mega 2560控制板为控制核心,搭配DS1-B20温度传感器、DHT11湿度传感器、MQ-2烟雾传感器、超声波测距传感器、E-P8266Wifi通讯模块组成硬件系统;采用PWM脉宽调制技术调节小车行进速度,PID算法调节小车的行进方向。调试结果表明,小车具有基于Wifi的无线遥控操作功能,能够对起所处环境中的温度、湿度、烟雾数据进行实时测量、采集和传输,并能自动检测前方障碍物。证明了基于Arduino控制板的控制系统在环境数据采集智能小车中应用的可行性。     

基于主成份分析和支持向量机的车标识别

基于车牌定位算法确定车牌和车标的位置关系,利用Hausdorff距离精确定位车标位置并对其进行数学形态学图像处理,边缘检测,连通域分析.对车标图像进行基于主成分分析的降维处理并对提取到的PCA特征进行归一化处理,最后用C-SVM支持向量机算法对提取到的特征进行识别.实验表明,上述算法具有精度高、识别效率高、速度快等优点...     

基于混沌自适应变异粒子群算法的铁路空车调配*

求解大规模的空车调配方案的最优解是一个非常困难的问题,为求解此类问题,提出了一种混沌自适应变异粒子群算法。该算法利用混沌的遍历性来初始化粒子群以增强群体的多样性,根据群体适应度方差调整变异概率的大小,并通过调整惯性权重因子以提高整个群体的全局和局部搜索能力。本文将该算法用于铁路空车调配,建立了以空车总走行距离最小为目标的数学模型,并在此基础上设计了相应的算法。算例结果表明该算法的寻优结果和寻优效率要优于蚁群算法和标准粒子群算法。