A lane based obstacle avoidance method for mobile robot navigation

This paper presents a new local obstacle avoidance method for indoor mobile robots. The method uses a new directional approach called the Lane Method. The Lane Method is combined with a velocity space method i.e., the Curvature-Velocity Method to form the Lane-Curvature Method(LCM). The Lane Method divides the work area into lanes, and then chooses the best lane to follow to optimize travel along a desired goal heading. A local heading is then calculated for entering and following the best lane, and CVM uses this local heading to determine the optimal translational and rotational velocities, considering some physical limitations and environmental constraint. By combining both the directional and velocity space methods, LCM yields safe collision-free motion as well as smooth motion taking the physical limitations of the robot motion into account.     

一种悬浮扑翼机器人的视觉自主避障方法研究

提出了一种基于视觉感知的悬浮扑翼机器人自主避障方案,以满足室内障碍物环境中低速、安全和长航时飞行要求。 首先,借鉴鸟类视觉避障原理,提出了一种基于光流视觉检测的避障方法,用于识别动、静态障碍物;其次,设计了一种氦气球与 扑翼结合的飞行机器人,利用氦气浮力提供主要升力,扑翼运动提供推进力和次要升力,并设计了飞行控制方法以满足稳定飞 行及避障的要求;最后,制作了机器人样机,测试了其飞行性能和自主避障能力,并与其他障碍物检测算法进行了对比。 结果表 明,样机的直飞航向偏移为 5. 52°,转弯速度可达 23°/ s,并以 6. 1 ms 的单帧检测时间达到 77% 的避障成功率,证明其能够实现 低速悬浮飞行和自主避障,为其开展室内探测任务奠定了基础。     

基于BP神经网络机器人实时避障算法

针对二维静态环境下智能机器人避障及路径规划问题,提出了基于BP神经网络的机器人实时避障算法。首先,用多个扇区表示机器人周围的环境,利用激光雷达探测每个扇区内障碍物的距离信息,以每个扇区内障碍物的距离信息为输入,利用BP神经网络计算该扇区被选择为避障方向的得分;然后,利用各扇区中点坐标与当前时刻距障碍物最近扇区中点坐标之间的欧氏距离,计算机器人在当前位姿条件下各扇区被选中作为避障方向的条件概率;最后,将使得得分与条件概率之积最大的扇区作为机器人的避障方向。实验结果表明:所提算法的收敛时间比栅格方法降低了50%以上,机器人的避障轨迹与人工势场方法相比更短,能较好地应用于复杂多障碍物场景。     

Symmetric caging formation for convex polygonal object transportation by multiple mobile robots based on fuzzy sliding mode control

In this paper, the problem of object caging and transporting is considered for multiple mobile robots. With the consideration of minimizing the number of robots and decreasing the rotation of the object, the proper points are calculated and assigned to the multiple mobile robots to allow them to form a symmetric caging formation. The caging formation guarantees that all of the Euclidean distances between any two adjacent robots are smaller than the minimal width of the polygonal object so that the object cannot escape. In order to avoid collision among robots, the parameter of the robots radius is utilized to design the caging formation, and the A⁎ algorithm is used so that mobile robots can move to the proper points. In order to avoid obstacles, the robots and the object are regarded as a rigid body to apply artificial potential field method. The fuzzy sliding mode control method is applied for tracking control of the nonholonomic mobile robots. Finally, the simulation and experimental results show that multiple mobile robots are able to cage and transport the polygonal object to the goal position, avoiding obstacles.     

基于PLC的中立柱喷胶机器人系统设计

针对汽车内饰件中立柱人工喷胶的缺点,设计了一种基于机、电、气结合适用于普桑与2000两种车型中立柱喷胶生产的机器人及周边设施,介绍了机器人系统的构成及控制的实现方法。     

基于神经网络的机器人位置逆解

本文运用神经网络求解机器人运动学位置逆解。通过深入分析基于Ｋｏｈｏｎｅｎ网络原理和Ｗｉｄｒｏｗ－Ｈｏｆ误差修正的Ｍ．Ｒ．Ｓ．自组织神经网络及机器人运动学特性，创新了自组织神经网络训练算法。对ＰＵＭＡ５６０机器人的计算机仿真结果表明：本算法在自组织能力和定位控制精度方面有大幅度提高。     

基于PSoC的智能吸尘机器人控制系统的设计

主要研究了基于PSoC片上系统的智能吸尘机器人控制系统的设计，详细论述了控制系统的组成，推导了机器人的运动控制方程，设计了传感器检测电路。该控制系统灵活运用PSoC片上系统内部集成的PWM模块、计数模块和定时模块等来完成机器人的运动控制，通过运用红外传感电路和机械结构相结合的方法来检测外部环境信息。     

一类纯滞后系统Fuzzy-Dahlin控制策略的研究

针对工业控制过程中普遍存在的大惯性、纯滞后、时变性、非线性对象的控制问题,提出用模糊控制器与Dahlin算法相结合的控制策略.仿真结果表明:该方法对此类控制具有超调小(可达到无超调)、调节时间短、鲁棒性好等优点.     

基于模型预测的感应电机弱磁控制研究

考虑工业场合对感应电机高速运行的实际需求,分析了感应电机在不同区域运行时的特点,特别对电机高速运行时的弱磁特性进行了研究,提出了一种基于模型预测算法的感应电机弱磁控制方法.首先预测被控对象未来的状态,再由滚动优化求出最优控制变量.仿真结果表明,基于模型预测算法的弱磁控制方法有更快的响应速度和较强的鲁棒性.     

基于模型预测控制的灯光调节系统设计

高压钠灯是人工温室常用的光源，控制比较困难。结合模型预测控制策略，设计了高压钠灯连续调光二级控制系统。实验结果表明，在存在干扰情况下，系统仍能获得满意的控制效果。     

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper presents a robust adaptive integral backstepping control strategy with friction compensation for realizing accurate and stable control of opto-electronic tracking system in the presence of nonlinear friction and external disturbance. With the help of integral control term to decrease the steady-state error of the system and combining robust adaptive control approach with the backstepping design method, a novel control method is constructed. Nonlinear modified LuGre observer is designed to estimate friction behavior. Robust adaptive integral backstepping control strategy is developed to compensate the changes in friction behavior and external disturbance of the servo system. The stability of the opto-electronic tracking system is proved by Lyapunov criterion. The performance of robust adaptive integral backstepping controller is verified by the opto-electronic tracking system with modified LuGre model in simulation and practical experiments. Compared to the adaptive integral backstepping sliding mode control method, the root mean square of angle error is reduced by 26.6% when the proposed control method is used. The experiment results demonstrate the effectiveness and robustness of the proposed strategy.     

一种液压挖掘机并联式混合动力系统结构及控制策略

为了能够从理论上对混合动力液压挖掘机进行研究,并从中找出有利于系统节能和降低排放的有效途径,以山河智能SWE230液压挖掘机为原型,分析其动力系统各元件特性,并利用AMEsim软件建立并联式混合动力挖掘机系统的整机仿真模型.通过试验采集挖掘机在典型工况下的功率谱.根据挖掘机的工况特点,提出1种基于工况预测的准定工作点控制策略,并应用于仿真模型.仿真结果表明,所提出的控制策略能有效减小超级电容荷状态(SOC)波动,稳定发动机工作点,提高燃油经济性.     

Adaptive backstepping sliding mode control with fuzzy monitoring strategy for a kind of mechanical system

A novel adaptive backstepping sliding mode control (ABSMC) law with fuzzy monitoring strategy is proposed for the tracking-control of a kind of nonlinear mechanical system. The proposed ABSMC scheme combining the sliding mode control and backstepping technique ensure that the occurrence of the sliding motion in finite-time and the trajectory of tracking-error converge to equilibrium point. To obtain a better perturbation rejection property, an adaptive control law is employed to compensate the lumped perturbation. Furthermore, we introduce fuzzy monitoring strategy to improve adaptive capacity and soften the control signal. The convergence and stability of the proposed control scheme are proved by using Lyaponov′s method. Finally, numerical simulations demonstrate the effectiveness of the proposed control scheme.     

Force control of a piezoelectric actuator based on a statistical system model and dynamic compensation

Piezoelectric actuators present a promising option if actuation of small-sized robot joints is considered. They develop forces of several newtons at velocities in the cm/s range while their dimensions and weight remain small as compared to electric motors. This work presents an approach for modelling a class of contemporary, non-resonant translatory piezoelectric actuators. The proposed modelling approach results in three motor models of increasing complexity independently of the low-level piezoelectric properties of the driving elements. The basic model establishes a static relation between motor velocity and drive frequency for a free-moving motor. The second model is a non-linear extension of the first model which introduces external load forces. The final model introduces time-dependent aspects by employing system identification techniques. The final model is used to develop a force compensation mechanism which restores linearity in the motor operation even in the loaded case. Based on the linearised model, standard control design techniques are applicable to design an explicit force controller. Limits on the performance of the controller are derived. The actual performance of the controller is evaluated both in simulations and experiments by pulling on tendons of different elasticity.     

基于SOPC的自寻迹切割机器人运动控制系统研究

针对自寻迹移动切割机器人控制为非完整系统控制的特点,构建了机器人运动模型,并分析了其运动规律,设计了一种基于可编程片上系统(SOPC)技术的切割机器人运动控制系统.该系统以EP3C55芯片为控制核心,采用模糊控制策略实现了切割机器人的运动控制.实验结果表明,该控制系统运行平稳,控制响应快,能够满足切割机器人的实时控制的...     

基于神经动力学的轮式移动机器人跟踪与稳定统一控制

为了对轮式移动机器人(WMR)进行光滑、鲁棒、稳定的轨迹跟踪控制,分析了生物激励神经动力学原理,研究了非线性模型预测控制策略,提出了一种基于神经动力学思想的模型预测终端控制方法。首先,针对传统控制方法存在的初始速度跳变问题,利用神经动力学在信息处理方面的优良特性,设计了神经动力学控制模块;然后,根据模型预测控制原理给出了一个优化控制模块;最后,设计了终端域和线性反馈终端控制器来保证系统的全局渐近稳定性。仿真结果表明:利用所设计的控制方法进行曲线跟踪时,被控WMR系统收敛到参考轨迹的时间可从12s降到5s,初始线速度/角速度分别从[-3,4]m/s和[-5,6]rad/s缩小到[0,2]m/s和[-3,3]rad/s,且系统输出有界光滑,使WMR在完成轨迹跟踪的同时实现了全局渐进稳定。由于文中核心算法的推导过程不受WMR运动学模型限制,故该研究结论亦可应用于其他结构的移动机器人。     

基于内模电流控制的交流感应电机矢量控制系统

从同步速d—q坐标系下异步感应电机动态模型和异步电机矢量解耦控制的基本原理出发,引入了内模控制方法,设计了基于转子磁链定向和内模控制的定子电流调节器。考虑到实际系统中异步感应电机磁场会随着电机负载（转矩）变化而呈不同程度的饱和而导致电机参数的非线性,分析了电流内模控制器对这种非线性参数的鲁棒性。在此基础上建立了整个异步感应电机矢量控制仿真系统,并分别对忽略磁路饱和和考虑磁路饱和两种情况下的系统进行了仿真和分析。仿真和分析结果表明,电流内模控制调节器在模型匹配和失配下均能提供良好的转矩动、静态解耦效果。     

直流无刷电动机的汽车电动助力转向系统控制策略研究

介绍了管柱助力式电动助力转向系统的组成与工作原理,设计了基于直流无刷电动机的电动助力转向系统控制策略及助力特性。在对直流无刷电动机结构与工作方式深入研究的基础上,设计了助力电动机的转矩控制策略。开发了基于Freescale微控制器MC56F8346的汽车电动助力转向系统控制器,在试验台上进行了转向助力试验。试验结果表明,所设计的控制策略有效,能够获得良好的助力特性。     

基于规则进化模糊系统的步行方向意图识别

为了准确识别下肢功能障碍患者自主步行康复训练过程中的方向意图,提出了一种能够兼顾使用者个体差异及安全状态的新型步行方向意图识别方法。首先论述了康复训练机器人结构及患者前臂对机器人支撑板的压力和步行方向意图的关系。为保证患者安全地向任意方向行走,提出根据膝盖旋转角度推理安全步态的先决条件下,基于距离型模糊推理算法设计具有稀疏前件规则库的步行方向意图识别方法;然后为减小因个体差异、非稳定模糊规则引起的识别误差,提出规则进化算法实时优化模糊推理规则库。最后将该算法进行了多方向模糊推理实验与步行康复训练机器人压力控制实验,实验表明该算法可以准确识别下肢功能患者的任意步行方向意图并提高了步行的安全性,提出的步行方向意图识别方法可以应用在下肢功能障碍人士的日常起居与康复训练中。     

基于粒子群优化的混合模型预测控制研究

为解决复杂非线性系统的控制精度不高,稳定性难于保证等问题、将预测控制技术应用于非线性控制中,提出了一种基于粒子群优化和混合模型的预测控制算法.混合模型预测控制算法使用模糊聚类和最小二乘法建立了系统的复合模犁,由带压缩因子的粒子群算法优化获得了非线性控制系统的控制量,在非线性模型上对模糊预测控制算法和混合模型预测控制算法...