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。     

巡线导航智能车的路径优化

针对现实应用中的巡线导航智能车道路检测范围小、路径规划困难的问题,提出增加传感器随动舵机的巡线导航智能车路径优化算法。安装随动摆头舵机带动传感器运动,增大传感器的路径识别范围。采用局部路径优化的方法,构建一个导航传递关系,用以在传感器数据和智能车行驶控制之间建立映射。实验结果表明,增加传感器随动舵机的巡线导航智能车在采用路径优化算法之后视觉范围增大,能改善巡线导航智能车的行驶性能,提高了寻线的可靠性,增大了巡线速度。     

H.264中全零块预判的新方法

为了避免全零DCT(离散余弦变换)系数进行变换与量化产生的冗余计算,从H.264中的DCT公式与量化原理出发,结合视频图像残差系数的分布规律,在分析现有算法存在的误判与漏判的基础上,提出了一种预先判断全零DCT系数块的方法。实验结果表明,与文献中的经典算法相比较,新提出的方法应用在中低比特率的视频序列中可以减少12%~37%的平均编码时间,平均压缩率提高了6%~10%。     

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...     

BAIS: A Bayesian Artificial Immune System for the effective handling of building blocks

Significant progress has been made in theory and design of Artificial Immune Systems (AISs) for solving hard problems accurately. However, an aspect not yet widely addressed by the research reported in the literature is the lack of ability of the AISs to deal effectively with building blocks (partial high-quality solutions coded in the antibody). The available AISs present mechanisms for evolving the population that do not take into account the relationship among the variables of the problem, potentially causing the disruption of high-quality partial solutions. This paper proposes a novel AIS with abilities to identify and properly manipulate building blocks in optimization problems. Instead of using cloning and mutation to generate new individuals, our algorithm builds a probabilistic model representing the joint probability distribution of the promising solutions and, subsequently, uses this model for sampling new solutions. The probabilistic model used is a Bayesian network due to its capability of properly capturing the most relevant interactions among the variables. Therefore, our algorithm, called Bayesian Artificial Immune System (BAIS), represents a significant attempt to improve the performance of immune-inspired algorithms when dealing with building blocks, and hence to solve efficiently hard optimization problems with complex interactions among the variables. The performance of BAIS compares favorably with that produced by contenders such as state-of-the-art Estimation of Distribution Algorithms.     

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.     

可配置宏的快速FPGA布局算法

提出一种可配置宏模块的快速FPGA布局算法。用解析模型确定所有宏模块及基本逻辑模块的理想位置,通过局部扩散得到一个合理的初始布局方案,用低温模拟退火进一步优化,确定各模块的最终位置。以平方线网总长度为目标函数,与VPR算法相比,该算法能较好地处理宏模块,大大降低布局所耗费的时间,不影响最终布局方案的质量。     

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。