基于光学鼠标传感器的转速测量方法研究

提出了一种基于光学鼠标传感器的转速测量方法.分析了转速测量原理,解决了光学鼠标传感器ADNS-3080的软件初始化和镜头对焦等关键问题,设计了基于光学鼠标传感器的转速测量试验系统.试验结果表明:这种方法能准确判别转向,对于本试验系统,在1 000 r/min范围内具有良好的线性度和快速响应性.该方法适用于低速转动部件的转速测量与转向判别.     

基于力矩信息的拖拉机转向动态PID控制方法

针对拖拉机驾驶机器人自主转向系统在转向过程中的阻尼具有非线性和时变性的特点,同时为了提高转向系统的控制性能,设计了基于力矩信号检测的拖拉机转向控制系统,其由力矩传感器、STM32处理器、工控机、驱动器及直流电机等组成.首先通过仿真研究不同力矩时的最优PID控制参数,给出不同力矩时的最优PID参数规律,再通过力矩传感器检测的力矩信号动态调整PID参数来控制电机的转向.仿真及实验结果均表明,基于力矩信号反馈的拖拉机动态PID转向控制方法能够有效控制拖拉机的方向,响应快、超调小、适应性强.     

激光自动寻迹智能车设计与改进

以一种模型汽车为硬件平台,以单片机为核心控制单元,激光传感器为检测手段,设计制作一种自动寻迹智能车控制系统。系统采用双排激光传感器探测路径,快速准确地提取赛道信息,并结合闭环PID算法,控制舵机的转向和电机的转速,使小车能够沿着固定的跑道高速稳定行驶。重点介绍了系统的硬件电路设计及传感器的布局和控制策略。通过多次测试和试验,相比于传统的单舵机控制方案,灵敏的双舵机控制系统能很好地满足智能车对路径自动识别功能和抗干扰能力的要求,速度调节响应时间快,稳态误差小,具有较好的动态性能和良好的鲁棒性。     

基于LPV观测器的永磁同步电机反推控制

针对永磁同步电机(Permanent Magnet Synchronous Motor,PMSM)无速度传感器转速跟踪控制精度问题,提出了一种基于线性变参数(Lineeo Parameteo Varying,LPV)转速观测器的永磁同步电机反推控制方法。该方法首先根据PMSM的LPV模型,推导出电机的凸胞形顶点方程;然后利用Lyapunov稳定性理论,获得了基于线性矩阵不等式(Lineeo Matrix Inequality,LMI)的观测器设计方法,构造了PMSM的LPV观测器,实现对电机转速及定子交轴电流的重构;最后运用反推控制策略,设计电机闭环系统控制器,实现对电机转速的高精度跟踪控制。仿真结果表明,该方法相较与传统PI矢量控制,跟踪精度高、响应快、抗负载扰动强,对实现PMSM的无速度传感器高精度转速跟踪控制具有重要意义。     

基于PID算法的直流电机PWM调速控制器设计

为提高对直流伺服电机转速的控制精度,将控制器硬件电路设计成闭环控制系统,通过位置传感器采样获取伺服电机转速并反馈给单片机系统,引入基于PID控制的算法对单片机进行编程控制,设计了一款能通过反馈控制自动修正直流电机转速的调速控制器,文中给出了控制器的硬件电路设计方法和系统软件的编程算法。该控制器具有成本低、控制精度高、调速范围宽、响应速度快等特点,通过简单硬件接口就能将其嵌入到的各种小功率直流电机控制产品中加以应用。     

自动循迹小车的位置和速度控制

为了实现小车快速平稳的循迹运动,采用5个红外光电传感器检测轨道的偏差,前部安装一个万向轮,通过后部左轮和右轮的速度差来控制小车的转向。分析了小车转弯时的运动轨迹,讨论了左轮和右轮速度与转弯半径等参数之间的关系;设计了位置+速度的串级PID控制模型,采用速度最快策略进行速度分配,确保小车在转向时快速流畅,使小车能够迅速、平稳、准确地沿赛道轨迹运动。该小车的设计方案和串级控制模型也可应用于无人车间自动搬运的AGV小车、自主移动机器人等服务机器人的循迹及定位控制。     

自动寻迹智能车设计

随着控制技术及计算机技术的发展,智能车系统在工业生产和日常生活中扮演重要的角色,而自动寻迹是智能车安全行驶的一个重要特征。本文设计的自动寻迹智能车系统由控制算法模块、电源管理模块、路径识别模块、后轮电机驱动模块、转向舵机控制模块、速度检测模块以及电池监控模块组成,实现了自动寻迹行驶功能,转向准确稳定,能够完全快速通过各种弯道、十字交叉路口,并能够准确的自动停车,具有良好的自主道路识别能力和稳定性。     

智能观测器在伺服系统控制中的应用

研究优化伺服控制系统策略,永磁同步电动机( PMSM)的伺服系统优化,可改善电动机系统的稳定性和响应特性.通过提高伺服系统定位精度和抗干扰能力,有效保证机器运行效率.针对传统有速度传感器矢量控制增加了系统复杂度和成本的问题,为优化伺服系统控制结构,提出了一种采用神经网络观测器的伺服系统无速度传感器矢量控制策略.系统中不需要安装传感器来检测PMSM转子位置/速度信号,而是利用神经网络观测器从电机反电动势信号中估算转子位置/速度,从而优化了系统整体结构,减小了系统复杂度.通过对PMSM的伺服系统无速度传感器矢量控制系统的建模与仿真测试,结果表明,所设计的神经网络观测器能够准确估算转子位置/速度,控制系统能够精确跟踪给定转速指令,改进了伺服系统优化控制问题,为实际应用提供了参考.     

基于免疫模糊PID的小型农业机械路径智能跟踪控制

为实现复杂环境下小型农业机械田间作业时的路径跟踪控制,提出了基于免疫模糊PID(比例-积分-微分)的智能路径跟踪控制方法.首先,路径跟踪控制被分解为自动直线导航和自动转向控制任务,并分别构建了能够实现自动导航的模糊控制器和基于免疫模糊PID控制的自动转向方法.该设计在无人驾驶高速插秧机硬件系统基础上,开发了基于双激光源定位技术、电子罗盘和角度传感器的自动导航控制系统.其次,根据自动导航控制系统构造和工作原理,提出了直线和曲线路径跟踪的方法.最后,利用Matlab/Simulink仿真平台和插秧机的运动学模型对所设计的路径跟踪控制原理和模糊控制器进行了有效性验证,同时完成了包括直线和曲线的路径跟踪试验.当插秧机以1 m/s的速度进行直线跟踪时,最大跟踪偏差只有4 cm,平均跟踪偏差为0.84 cm;当以同样的速度做曲线跟踪时,曲线路径跟踪时的最大偏差为0.6 m,平均跟踪偏差控制在12 cm以内.仿真和试验结果表明,该套控制系统能够有效地控制无人驾驶高速插秧机按预定路径行走.     

多路径识别的智能循迹机器人设计与实现

智能循迹机器人在多传感器信号融合处理、多路径识别方面尚存缺陷。设计了一种具有多路径识别和测速反馈功能的自主循迹机器人,机器人采用AT89S52单片机作为控制核心,通过红外光电传感器实时检测路径情况,槽型光电传感器检测机器人运行速度,利用优先级判决法处理传感器反馈给单片机的信号,控制机器人的速度及转向。试验结果表明,设计的机器人能够很好地解决多种路径识别与冲突问题,可以自主准确、快速地实现对黑色引导线的判断和处理,使机器人根据引导线走向稳定地行驶。     

湖水环境下自主监测船的GPS导航设计

建立导航系统所需的全局静坐标系、局部静坐标系、船体动坐标系和自主船运动学模型。模拟人的驾驶技术,建立巡航和定区域节能2种导航模式,设计推理规则。分析实测的电子罗盘和GPS数据误差原因。为提高定位精度,设计海明窗FIR数字滤波器以及巡航面积计算方法。基于VC++开发导航软件,在自主研发的监测船上开展实验。结果表明,FIR对电子罗盘信号的滤波效果优于GPS信号,巡航模式可实现大范围监测,定区域模式对重点区域进行监测时能耗较低。     

遥控水质采样的自适应空气动力无人船系统设计

遥控水质采样与监测无人船作为一种新型水域环境监测平台,具有体积小、成本低、使用方便等优点,可应用于复杂水域的水质采样与监测.传统的无人船利用水中的螺旋桨提供动力,易被水草和杂物缠绕而失去动力,因此设计了一种利用空气中的螺旋桨提供动力的遥控水质采样与监测无人船.系统通过加速度传感器、超声波测距传感器、GPS导航仪等实现自主航行和水质采样,通过无线通信将采样到的数据传输到上位机中,实现水质的自动采样与监测,同时利用一种污染源在线追踪方法,可使无人船对污染源进行自动追踪.     

智能车辆的滑模轨迹跟踪控制

智能车辆是集各种机械装置、传感器、计算机于一体的复杂非线性系统,其轨迹跟踪控制器是研究的关键技术之一。针对高速自主导航智能车辆轨迹跟踪控制器鲁棒性、精确性和实时性的高要求,在智能车辆结构组成与运动模型基础上,设计了一种滑模变结构控制器。通过控制智能车辆的线速度和角速度实现智能车辆对任意路径的跟踪,并用Matlab进行了仿真实验。结果验证了该方法的有效性和可靠性。     

Autonomous collision detection and avoidance for ARAGON USV: Development and field tests

This study addresses the development of algorithms for multiple target detection and tracking in the framework of sensor fusion and its application to autonomous navigation and collision avoidance systems for the unmanned surface vehicle (USV) Aragon. To provide autonomous navigation capabilities, various perception sensors such as radar, lidar, and cameras have been mounted on the USV platform and automatic ship detection algorithms are applied to the sensor measurements. The relative position information between the USV and nearby objects is obtained to estimate the motion of the target objects in a sensor‐level tracking filter. The estimated motion information from the individual tracking filters is then combined in a central‐level fusion tracker to achieve persistent and reliable target tracking performance. For automatic ship collision avoidance, the combined track data are used as obstacle information, and appropriate collision avoidance maneuvers are designed and executed in accordance with the international regulations for preventing collisions at sea (COLREGs). In this paper, the development processes of the vehicle platform and the autonomous navigation algorithms are described, and the results of field experiments are presented and discussed.     

Guidance and nonlinear control system for autonomous flight of minirotorcraft unmanned aerial vehicles

Small unmanned aerial vehicles (UAVs) are becoming popular among researchers and vital platforms for several autonomous mission systems. In this paper, we present the design and development of a miniature autonomous rotorcraft weighing less than 700 g and capable of waypoint navigation, trajectory tracking, visual navigation, precise hovering, and automatic takeoff and landing. In an effort to make advanced autonomous behaviors available to mini‐ and microrotorcraft, an embedded and inexpensive autopilot was developed. To compensate for the weaknesses of the low‐cost equipment, we put our efforts into designing a reliable model‐based nonlinear controller that uses an inner‐loop outer‐loop control scheme. The developed flight controller considers the system's nonlinearities, guarantees the stability of the closed‐loop system, and results in a practical controller that is easy to implement and to tune. In addition to controller design and stability analysis, the paper provides information about the overall control architecture and the UAV system integration, including guidance laws, navigation algorithms, control system implementation, and autopilot hardware. The guidance, navigation, and control (GN&C) algorithms were implemented on a miniature quadrotor UAV that has undergone an extensive program of flight tests, resulting in various flight behaviors under autonomous control from takeoff to landing. Experimental results that demonstrate the operation of the GN&C algorithms and the capabilities of our autonomous micro air vehicle are presented. © 2009 Wiley Periodicals, Inc.     

自主飞行机器人导航系统设计

自主飞行机器人系统是以微型直升机模型为载体的复杂系统。在该系统中导航系统采集各传感器数据得到机器人当前飞行姿态、空间位置以及相应的监控信息,控制模块依此监控信息按照给定策略计算并发出控制信号,实现飞行机器人的自主控制。本文对自主飞行机器人导航系统设计及功能实现做出了详细阐述。首先,给出了本自主飞行机器人的系统构造;其次,给出了导航系统的硬件组成部分以及各部分所完成的功能任务;最后阐述了导航系统的功能实现,包括飞行姿态和空间位置的获取。     

Positioning accuracy improvement of laser navigation using UKF and FIS

This paper presents positioning improvement of a laser navigation system (LNS) using the unscented Kalman filter (UKF) and fuzzy inference system (FIS) for an automatic guided vehicle (AGV). The existing AGVs mainly used a magnetic system or an inductive system as a guidance system. However, those systems have high initial facility cost and are difficult to maintain according to changes of environment, and it can drive only the designated path by sensors which are installed on. The laser guidance system is developed to solve these problems, but it also has limitations which are slow response time and low accuracy. Therefore, we propose a sensor fusion method for the AGV. The sensors used in this paper are encoders, a gyro and the LNS, and they are fused by the UKF and FIS. To analyze the performance of the proposed system, we designed a fork-type AGV for ourselves and performed the experiment that was repeated 10 times under the same working conditions. In experimental results, we verified that the proposed method could improve positioning accuracy of the LNS effectively. In addition, it was appropriate to apply a real AGV system for autonomous driving.     

Terrain‐aided navigation for long‐endurance and deep‐rated autonomous underwater vehicles

Terrain‐aided navigation (TAN) is a localisation method which uses bathymetric measurements for bounding the growth in inertial navigation error. The minimisation of navigation errors is of particular importance for long‐endurance autonomous underwater vehicles (AUVs). This type of AUV requires simple and effective on‐board navigation solutions to undertake long‐range missions, operating for months rather than hours or days, without reliance on external support systems. Consequently, a suitable navigation solution has to fulfil two main requirements: (a) bounding the navigation error, and (b) conforming to energy constraints and conserving on‐board power. This study proposes a low‐complexity particle filter‐based TAN algorithm for Autosub Long Range, a long‐endurance deep‐rated AUV. This is a light and tractable filter that can be implemented on‐board in real time. The potential of the algorithm is investigated by evaluating its performance using field data from three deep (up to 3,700 m) and long‐range (up to 195 km in 77 hr) missions performed in the Southern Ocean during April 2017. The results obtained using TAN are compared to on‐board estimates, computed via dead reckoning, and ultrashort baseline (USBL) measurements, treated as baseline locations, sporadically recorded by a support ship. Results obtained through postprocessing demonstrate that TAN has the potential to prolong underwater missions to a range of hundreds of kilometres without the need for intermittent surfacing to obtain global positioning system fixes. During each of the missions, the system performed 20 Monte Carlo runs. Throughout each run, the algorithm maintained convergence and bounded error, with high estimation repeatability achieved between all runs, despite the limited suite of localisation sensors.     

低功耗水下自主导航系统设计

以微处理器586 -engine为控制核心,采用模型辅助的自主导航算法设计了一种水下自主导航系统.该系统能够实时采集多个外部传感器数据并对数据进行实时解算,同时满足低成本、低功耗和长航时的要求.搭建了整个系统硬件平台,通过陆上跑车实验对整个实时导航系统进行了满足一定精度的简单实验验证,验证了系统的实时性和可行性.     

自动驾驶的自适应解析模糊控制方法研究

该文在Michael等人提出的车辆系统动力学模型犤7犦的基础上进行自动驾驶的仿真研究,提出了基于模糊控制的车辆驾驶算法。通过构造自适应的解析模糊控制器,空旷道路的汽车自动驾驶问题得到了较好的解决。该控制算法具有收敛快、超调小,准确调整定位巡航速度并根据路况信息自动矫正方向、沿指定路线前进等特点。这种算法不但可以应用于车辆辅助安全驾驶系统,也可以应用于机器人的自主导航等方面。