Contouring error modeling and simulation of a four-axis motion control system

A layered modeling method is proposed to resolve the problems resulting from the complexity of the error model of a multi-axis motion control system. In this model, a low level layer can be used as a virtual axis by the high level layer. The first advantage of this model is that the complex error model of a four-axis motion control system can be divided into several simple layers and each layer has different coupling strength to match the real control system. The second advantage lies in the fact that the controller in each layer can be designed specifically for a certain purpose. In this research, a three-layered cross coupling scheme in a four-axis motion control system is proposed to compensate the contouring error of the motion control system. Simulation results show that the maximum contouring error is reduced from 0.208 mm to 0.022 mm and the integration of absolute error is reduced from 0.108 mm to 0.015 mm, which are respectively better than 0.027 mm and 0.037 mm by the traditional method. And in the bottom layer the proposed method also has remarkable ability to achieve high contouring accuracy.

Modeling and Tracking Control for Piezoelectric Actuator Based on a New Asymmetric Hysteresis Model

This paper presents a new asymmetric hysteresis model and its application in the tracking control of piezoelectric actuators. The proposed model is based on a coupled-play operator which can avoid the conventional Prandtl-Ishlinskii(CPI)model's defects, i.e., the symmetric property. The high accuracy for modeling asymmetric hysteresis is validated by comparing simulation results with experimental measurements. In order to further evaluate the performance of the proposed model in closed-loop tracking application, two different hybrid control methods which experimentally demonstrate their performance under the same operating conditions, are compared to validate that the hybrid control strategy with proposed hysteresis model can mitigate the hysteresis more effectively and achieve better tracking precision. The experimental results demonstrate that the proposed modeling and tracking control strategy can realize efficient control of piezoelectric actuator.

Fuzzy logic for large mining bucket wheel reclaimer motion control—from an engineer's perspective

The bucket wheel reclaimer(BWR) is a key piece of equipment which has been widely used for stacking and reclaiming bulk materials(i.e.iron ore and coal) in places such as ports,iron-steel plants,coal storage areas,and power stations from stockpiles.BWRs are very large in size,heavy in weight,expensive in price,and slow in motion.There are many challenges in attempting to automatically control their motion to accurately follow the required trajectories involving uncertain parameters from factors such as friction,turbulent wind,its own dynamics,and encoder limitations.As BWRs are always heavily engaged in production and cannot be spared very long for motion control studies and associated developments,a BWR model and simulation environment closely resembling real life conditions would be beneficial.The following research focused mainly on the implementation of fuzzy logic to a BWR motion control from an engineer's perspective.First,the modeling of a BWR including partially known parameters such as friction force and turbulence to the system was presented.This was then followed by the design of a fuzzy logic-based control built on a model-based control loop.The investigation provides engineers with an example of applying fuzzy logic in a model based approach to properly control the motion of a large BWR following defined trajectories,as well as to show possible ways of further improving the controller performance.The result indicates that fuzzy logic can be applied easily by engineers to overcome most motion control issues involving a large BWR.

Parameter Identification and Controller Design for the Velocity Loop in Motion Control Systems

Today the controller commissioning of industrial used servo drives is usually realized in the frequency domain with the open-loop frequency response. In contrast to that the cascaded system of position loop, velocity loop and current loop, which is standard in industrial motion controllers, is described in literature by using parametric models. Several tuning rules in the time domain are applicable on the basis of these parametric descriptions. In order to benefit from the variety of tuning rules an identification method in the time domain is required. The paper presents a method for the identification of plant parameters in the time domain. The approach is based on the auto relay feedback experiment by ?str?m/ H?gglund and a modified technique of gradual pole compensation. The paper presents the theoretical description as well as the implementtation as an automatic application in the motion control system SIMOTION. The identification results as well as the achievable performance on a test rig with a PI velocity controller will be presented.

Fast Ferry Smoothing Motion via Intelligent PD Controller

A novel type of control law was adopted to reduce the vertical acceleration of a fast ferry as well as the motion sickness incidence suffered by the passengers onboard by means of a submerged T-foil.Considering the system changing characteristics under high disturbances,a model-free approach was adopted.In addition,an upgraded proportional-derivative(PD)controller with correction terms resulting from a fast-online estimation of the system dynamics was designed.The overall controller,known as intelligent PD(i-PD)controller,was tested,and the obtained results were compared with those of a classic PD controller.The controllers were also tested in a changing environment and at different operating velocities.The results confirmed the effectiveness of the i-PD controller to smooth the motions with low computational cost control schemes.Furthermore,thanks to ability of the i-PD controller to continually update the estimated dynamics of the system,it showed a better reduction in both vertical motions and the seasickness level of the passengers with the needed robustness under external disturbances and system changing parameters.

海参捕捞机器人运动控制系统的仿真研究

随着水下机器人技术的发展,海参捕捞机器人将逐渐取代费时费力的人工捕捞作业。但是海参捕捞机器人的运动控制精度一直影响其运动稳定性和捕捞效率,一方面是由于海底的作业环境恶劣多变,机器人的结构功能复杂;另一方面是随着海参的累积,机器人的参数发生改变,原有控制模型的控制精度下降。为提高海参捕捞机器人的运动控制精度,在综合考虑机器人各种载荷的基础上,还考虑了海参对机器人造成的干扰,建立更加全面的机器人动力学模型;并运用模型预测控制理论和非线性干扰观测器对机器人的运动过程进行模拟分析。通过分别模拟机器人的定深下潜、运动姿态保持、路径跟踪和载重上升运动过程,定量分析机器人的运动控制精度,最后构建一个系统全面且精度较高的海参捕捞机器人运动控制系统。

基于无模型自适应控制的半潜式平台运动性能研究

南海海域油气资源极其丰富,半潜式平台在该海域工作时常常面临着系泊失效这一重大危险情况。该研究以一艘装配有悬链式系泊系统和DP-3动力定位系统的半潜式平台为研究对象,利用AQWA软件开展系泊失效前后半潜式平台运动性能研究。同时,借助于AQWA软件二次开发功能,将无模型自适应控制作为动力定位系统控制理论,以研究半潜式平台系泊失效后运动性能恢复。数值仿真模拟结果表明,采用无模型自适应控制作为动力定位系统控制理论的半潜式平台能很好地恢复系泊失效后的运动性能,有效地减小系泊失效带来的危害;基于偏格式动态线性化和全格式动态线性化的无模型自适应控制比基于紧格式动态线性化的无模型自适应控制能让半潜式平台更快地恢复运动性能。

基于LADRC的四轮全向Mecanum轮机器人运动控制研究

近年来,移动机器人在巡检作业、智能电器、无人驾驶等领域的作用日益显现。全向移动机器人由于结构的特殊性和运动控制的复杂性,采用经典控制方法如比例积分微分控制、线性二次型调节控制等很难实现稳定的控制。针对全向移动机器人易受到外界扰动及其建模不精确的问题,进行四轮全向Mecanum轮机器人的运动控制系统设计。使用扩张状态观测器进行机器人总扰动的实时估计,完成LADRC控制器的设计。实验结果表明,相较于传统控制方法,基于LADRC的控制器能够更好地抑制外部干扰,减小运动误差,并提高机器人的运动精度和鲁棒性。

A 3D Spectroscopic Vision-based Control Technique for Bilateral Systems

Visual servoing is an active and popular area of research among roboticists.Eventhough visual servoing techniques enhance the perfomance,the associated systems still use traditional methods for their input control.Many research activities and applications have been carried out to implement effective and precise controlling of bilateral systems.This paper presents a 3D spresctroscope-based control technique for bilateral systems.The effectiveness of the available master side designs are evaluated against gesture-based techniques.Joystick control,Electromyography(EMG),Voice control,Haptic control,Exoskeleton control,Gesture and Brain Control Interface(BCI)are identified in the litreature as available bilateral inputs.In the present technnique,Leap Motion Controller(LMC)has been introduced(LMC)to extract the human hand gestures and their parameters.Then these parameters are convereted into respective joint sapce angles using the presented mathematical model.The mathematical models for fingertip mapping,inverse kinematics,dynamics and trajectory generation are implemented and studied.Wolfman Mathematica 10 and MATLAB simulation framework are used to validate the mathematical models,simulations and developed control algorithms.The developed system has sucesfully imitated the fingertip motion.In particular,the system has been able to imitate the figretip motion with a deviation of 6.7%in X axis,5.5%in Y axis and7.9%in Z axis with respect to the expected position.

基于工业环境下复合机器人设计

为适应复杂的工业环境,设计了一种STC8A系列单片机为控制核心的多功能复合机器人。通过分析四轮全向移动机器人的运动模型,构建了偏差耦合控制与模糊PID控制算法,提升了机器人移动平台的平稳性;利用寻迹和OpenMV OV9650图像识别模块识别AprilTag信息,实现机器人巡航和物料的精准抓取与操作。测试结果表明:所设计的复合机器人具有良好的稳定性,能很好地完成抓取工作。

足式机器人SLIP模型向上跳跃台阶的运动控制

由于存在地势起伏,台阶对足式机器人运动稳定性会带来较大挑战.弹簧负载倒立摆模型(SLIP)作为研究足式机器人的优良模板,能否完成向上跳跃台阶的动作与其腿部摆角,起跳位置和跳跃高度都有密切的关系.由于调整模型腿部摆角规律容易引发运动失效,故本文在算法中引入虚拟弹簧腿,根据虚拟弹簧腿的运行规律确定合理起跳位置,根据起跳位置来控制系统跳跃高度进而完成跳跃台阶的动作.最后利用仿真软件进行多组仿真,结果表明本文算法对起跳区间划分合理,对起跳高度控制精准,能够实现SLIP模型跳跃台阶前后的稳定运动.

应用鲸鱼智能优化算法的钻锚机器人控制定位

为了解决钻锚机器人控制定位误差大的问题,提出了应用鲸鱼智能优化的钻锚机器人精确控制定位方法。根据钻锚机器人的运动结构,对各个关节运动范围标定,建立钻锚机器人运动数学模型。分析钻锚机器人前移控制和后移控制误差产生原因,计算前移控制和后移控制的距离差值。基于分数阶微积分控制器控制定位误差,利用鲸鱼智能优化方法寻找最佳控制参数,搜索个体和最优解向量,通过空间维度映射迭代优化获得最优控制定位结果,实现精准控制定位。测试结果证明所提方法能够保证钻锚机器人控制误差迭代速度较快,误差较小,可以进一步提升钻锚和挖掘精度。

自动驾驶汽车的无偏移非线性模型预测控制研究

为解决自动驾驶汽车运动控制中基于横向-纵向耦合结构的无偏移非线性模型预测控制(OF-NMPC)的稳态误差问题,使用无迹卡尔曼滤波器观察控制器状态和扰动,并纳入模型预测和参考值计算以消除稳态误差。仿真和实车试验结果表明,所提出的OF-NMPC算法可有效消除稳态误差,提高系统的动态性能。

双腿轮式机器人运动控制研究

双腿轮式机器人融合了足式与轮式机器人的优势,展现出灵活性和高速高效特性。然而,精确控制这类机器人需要对运动学和动力学模型进行深入研究。此外,欠驱动、强耦合和非线性等控制难点也进一步加剧了机器人运动与自平衡控制的挑战。为应对这一挑战,本文提出一种分布式动力学建模策略,分别对机器人的躯干及腿轮子系统进行了精确建模,保留了机器人的所有动力学特性,并据此设计出以腿轮末端输出力矩为控制目标的力矩解算器。随后,基于该模型创新性地提出一种全身力矩控制框架,旨在实现双腿轮式机器人的运动控制和动态平衡。为进一步提升其运动性能,还规划了多运动模式,并成功地集成到全身力矩控制系统中。在MATLAB环境下搭建了双腿轮式机器人仿真实验平台,设计了两种行走实验方案和3种不同跳跃高度的仿真实验。实验结果表明,行走速度误差小于0.09 m/s,跳跃高度误差控制在0.02 m以内,验证了控制策略的可行性。最后,搭建了腿轮融接型复合结构的双腿轮式机器人,成功开展了机器人多运动模式实验,进一步证实了机器人在运动控制作业中的高精度表现,其躯干高度误差在3.38 mm以内,俯仰角误差不超过0.04 rad。研究结果充分验证了分布式全身动力学建模的精确性以及全身力矩控制系统的有效性,为双腿轮式机器人的运动控制研究提供了新的理论框架和实践指导。

仿生机器鱼步态控制及闭环运动控制方法综述

鱼类所具有的推进效率高、机动性强、环境扰动小等优点引发了国内外学者对仿生机器鱼的研究。底层步态控制方法和闭环运动控制方法是当前机器鱼控制研究的两大热点。按照推进模式的分类方法概述各类机器鱼的样机研制情况以及其性能优劣,介绍机器鱼的推进机理及其水动力学研究进展,进而重点探讨轨迹逼近方法和中枢模式发生器这2种底层步态控制思路,综述机器鱼的典型闭环运动控制方法。中枢模式发生器具有更强的灵活性、稳定性和可操作性,易于引入反馈项而实现闭环控制,在机器鱼底层步态控制中占主导地位。针对机器鱼的显著特点改进后的基于学习的控制方法与多种方法相结合后的混合控制方法具有更为广阔的发展前景,符合仿生机器鱼智能化的发展方向。根据工作条件和运动要求建立合理的步态控制系统以及准确高效的闭环运动控制系统可提高机器鱼的整体性能。

船舶类量化神经网络自适应运动控制方法研究

研究船舶类航向自适应运动控制方法有助于加快解决船舶在海上通讯带宽受限情况下航向跟踪检测困难和控制效果差的问题。基于RBF神经网络,采用一种经典非线性运动解析模型来描述通信信号输入量化过程,无限逼近于航向控制系统中的未知非线性项来消除隐性不确定项因子对控制系统的影响,与此同时模型中所设计的RBF自适应量化控制器不需要对先验信息进行量化参数处理,不仅可以保证有效跟踪和控制的同时,还可以减轻通信的传输负担、减少执行频次和降低系统控制幅度。本文基于Lyapunov稳定性理论证明了所提出的带有输入量化的RBF神经网络自适应闭环控制系统的稳定性,并在Matlab Simulink环境中构建仿真模型分析,论证了所设计的运动控制方法的有效性。

双控制方式下带缆水下机器人轨迹跟踪与水动力响应分析

本文首先根据已有的脐带缆运动控制方程,通过引入与工作船和水下机器人连接点处边界条件和速度耦合关系,与水下机器人运动方程一同构成完整的水动力数学模型;其次提出收放脐带缆的前馈-反馈控制方法和调节螺旋桨转速的增量式PID(Proportion Integration Differentiation)算法,建立完整的带缆水下机器人系统水动力与控制模型;最后对所提出的模型进行数值模拟实验验证与运动控制操纵下的水动力分析。数值计算结果表明:本文所提出的带缆水下机器人系统水动力与控制模型是有效可靠的,垂向运动控制过程中,水下机器人纵摇角、横摇角和沉深的模拟值与实验值最大误差分别为2°、1°和-50 mm;对于PID算法,调节螺旋桨转速控制水下机器人对给定位置信息的跟踪响应效果,模拟值与实验值相差均不大;轨迹跟踪模拟计算沿水平(X轴)方向和竖直(Z轴)方向最大相对误差分别为10%和15%;水下机器人竖向(沿Z轴)运动主要由调节脐带缆长度的前馈-反馈策略决定,沿水平(X轴)方向的运动主要由调节螺旋桨转速的PID算法控制;机器人水动力载荷受其周围流场变化的影响,而流场的变化主要由机器人运动速度的改变和螺旋桨转速变化二者共同决定。

单气腔仿肺软体驱动器建模与变形特性分析

放射治疗是肺癌治疗的重要手段,然而肺部肿瘤随呼吸运动产生的空间运动,对精准放疗提出了挑战。针对目前精准放射治疗中图像引导带来的对健康组织的额外辐射剂量问题,本文提出一种利用仿肺软体驱动器体外模拟肺的呼吸变形运动,通过建模预测肺部肿瘤运动的思路,从而减少患者接受额外辐射剂量,实现精准并安全的放射治疗。首先利用CT图像精准完成肺部的外形还原,然后得到的肺部随呼吸的变形运动规律。在此基础上,设计了肺部空腔的形态,并通过变形仿真验证了结构设计的合理性,据此制作了仿肺软体驱动器样机。最后,通过给予离散化的周期性气压来模拟实际的呼吸状态对样机进行了测试。实验结果表明:所设计仿肺软体驱动器可以按预期完成呼吸变形模拟,最大气压为45 kPa,最大轴向位移为5.9 mm,最大径向位移为3.4 mm。仿肺软体驱动器具有良好的呼吸运动变形模拟效果,可以为肺部肿瘤精准安全放射治疗提供借鉴。

波浪环境下无人水面艇改进MFAC节能控制方法

无人水面艇在运动过程中容易受到环境干扰的影响,尤其在受到波浪干扰后会导致其运动控制偏离期望路径,进而造成能量浪费。针对波浪环境下固定参数控制器对环境适应能力较弱的问题,将模糊控制方法与重定义无模型自适应控制方法相结合,设计了一种模糊重定义无模型自适应控制(FRMFAC)算法,该算法可以根据不同波浪的干扰情况自行调整控制参数;同时,采用视线法与FRMFAC算法对无人艇进行路径跟随控制;最后,通过与固定参数控制方法进行对比试验,验证了该算法的有效性和节能效果。对比仿真结果可知,使用可变参数的控制方法可以在有效提高无人艇对环境适应能力的同时起到节能效果。

基于运动轨迹预测控制的机械臂目标捕捉策略

利用机械臂捕捉运动中的物体是一项极具挑战性的任务,目标的轨迹预测、落点计算以及机械臂的运动控制需要在毫秒级内完成。为此提出一种基于运动轨迹预测控制的机械臂目标捕捉策略。首先,结合长短时记忆神经网络和卡尔曼滤波算法,设计一种改进的运动轨迹预测模型,实现动态目标的落点计算。其次,采用基于模型预测方法的位置视觉伺服控制,用于实时修正机械臂的姿态,从而实现运动物体的捕捉。最后,建立基于UR5机械臂的仿真与实验环境,验证所提方案的有效性。