Multibody Dynamic Modeling and Simulation of a Tailless Folding Wing Morphing Aircraft

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Design and Implementation of a Bionic Robotic Hand

Design and Implementation of a Bionic Robotic Hand

Design and Implementation of a Bionic Robotic Hand with Multimodal Perception Based on ModelPredictive Controlline 1:line 2:Abstract—This paper presents a modular bionic robotic hand system based on Model Predictive Control (MPC). The system's main controller is a six-degree-of-freedom STM32 servo control board, which employs the Newton-Euler method for a detailed analysis of the kinematic equations of the bionic robotic hand, facilitating the calculations of both forward and inverse kinematics. Additionally, MPC strategies are implemented to achieve precise control of the robotic hand and efficient execution of complex tasks.To enhance the environmental perception capabilities of the robotic hand, the system integrates various sensors, including a sound sensor, infrared sensor, ultrasonic distance sensor, OLED display module, digital tilt sensor, Bluetooth module, and PS2 wireless remote control module. These sensors enable the robotic hand to perceive and respond to environmental changes in real time, thereby improving operational flexibility and precision. Experimental results indicate that the bionic robotic hand system possesses flexible control capabilities, good synchronization performance, and broad application prospects.Keywords-Bionic robotic hand; Model Predictive Control (MPC); kinematic analysis; modular designI. INTRODUCTIONWith the rapid development of robotics technology, the importance of bionic systems in industrial and research fields has grown significantly. This study presents a bionic robotic hand, which mimics the structure of the human hand and integrates an STM32 microcontroller along with various sensors to achieve precise and flexible control. Traditional control methods for robotic hands often face issues such as slow response times, insufficient control accuracy, and poor adaptability to complex environments. To address these challenges, this paper employs the Newton-Euler method to establish a dynamic model and introduces Model Predictive Control (MPC) strategies, significantly enhancing the control precision and task execution efficiency of the robotic hand.The robotic hand is capable of simulating basic human arm movements and achieves precise control over each joint through a motion-sensing glove, enabling it to perform complex and delicate operations. The integration of sensors provides the robotic hand with biological-like "tactile," "auditory," and "visual" capabilities, significantly enhancing its interactivity and level of automation.In terms of applications, the bionic robotic hand not only excels in industrial automation but also extends its use to scientific exploration and daily life. For instance, it demonstrates high reliability and precision in extreme environments, such as simulating extraterrestrial terrain and studying the possibility of life.II.SYSTEM DESIGNThe structure of the bionic robotic hand consists primarily of fingers with multiple joint degrees of freedom, where each joint can be controlled independently. The STM32 servo acts as the main controller, receiving data from sensors positioned at appropriate locations on the robotic hand, and controlling its movements by adjusting the joint angles. To enhance the control of the robotic hand's motion, this paper employs the Newton-Euler method to establish a dynamic model, conducts kinematic analysis, and integrates Model Predictive Control (MPC) strategies to improve operational performance in complex environments.In terms of control methods, the system not only utilizes a motion-sensing glove for controlling the bionic robotic hand but also integrates a PS2 controller and a Bluetooth module, achieving a fusion of multiple control modalities.整整整整如图需要预留一个图片的位置III.HARDWARE SELECTION AND DESIGN Choosing a hardware module that meets the functional requirements of the system while effectively controlling costs and ensuring appropriate performance is a critical consideration prior to system design.The hardware components of the system mainly consist of the bionic robotic hand, a servo controller system, a sound module, an infrared module, an ultrasonic distance measurement module, and a Bluetooth module. The main sections are described below.A.Bionic Mechanical StructureThe robotic hand consists of a rotating base and five articulated fingers, forming a six-degree-of-freedom motion structure. The six degrees of freedom enable the system to meet complex motion requirements while maintaining high efficiency and response speed. The workflow primarily involves outputting different PWM signals from a microcontroller to ensure that the six degrees of freedom of the robotic hand can independently control the movements of each joint.B.Controller and Servo SystemThe control system requires a variety of serial interfaces. To achieve efficient control, a combination of the STM32 microcontroller and Arduino control board is utilized, leveraging the advantages of both. The STM32 microcontroller serves as the servo controller, while the Arduino control board provides extensive interfaces and sensor support, facilitating simplified programming and application processes. This integration ensures rapid and precise control of the robotic hand and promotes efficient development.C.Bluetooth ModuleThe HC-05 Bluetooth module supports full-duplex serial communication at distances of up to 10 meters and offers various operational modes. In the automatic connection mode, the module transmits data according to a preset program. Additionally, it can receive AT commands in command-response mode, allowing users to configure control parameters or issue control commands. The level control of external pins enables dynamic state transitions, making the module suitable for a variety of control scenarios.D.Ultrasonic Distance Measurement ModuleThe US-016 ultrasonic distance measurement module provides non-contact distance measurement capabilities of up to 3 meters and supports various operating modes. In continuous measurement mode, the module continuously emits ultrasonic waves and receives reflected signals to calculate the distance to an object in real-time. Additionally, the module can adjust the measurement range or sensitivity through configuration response mode, allowing users to set distance measurement parameters or modify the measurement frequency as needed. The output signal can dynamically reflect the measurement results via level control of external pins, making it suitable for a variety of distance sensing and automatic control applications.IV. DESIGN AND IMPLEMENTATION OF SYSTEMSOFTWAREA.Kinematic Analysis and MPC StrategiesThe control research of the robotic hand is primarily based on a mathematical model, and a reliable mathematical model is essential for studying the controllability of the system. The Denavit-Hartenberg (D-H) method is employed to model the kinematics of the bionic robotic hand, assigning a local coordinate system to each joint. The Z-axis is aligned with the joint's rotation axis, while the X-axis is defined as the shortest distance between adjacent Z-axes, thereby establishing the coordinate system for the robotic hand.By determining the Denavit-Hartenberg (D-H) parameters for each joint, including joint angles, link offsets, link lengths, and twist angles, the transformation matrix for each joint is derived, and the overall transformation matrix from the base to the fingertip is computed. This matrix encapsulates the positional and orientational information of the fingers in space, enabling precise forward and inverse kinematic analyses. The accuracy of the model is validated through simulations, confirming the correct positioning of the fingertip actuator. Additionally, Model Predictive Control (MPC) strategies are introduced to efficiently control the robotic hand and achieve trajectory tracking by predicting system states and optimizing control inputs.Taking the index finger as an example, the Denavit-Hartenberg (D-H) parameter table is established.The data table is shown in Table ITABLE I. DATA SHEETjoints, both the forward kinematic solution and the inverse kinematic solution are derived, resulting in the kinematic model of the ing the same approach, the kinematic models for all other fingers can be obtained.The movement space of the index finger tip is shownin Figure 1.Fig. 1.Fig. 1.The movement space at the end of the index finger Mathematical Model of the Bionic Robotic Hand Based on the Newton-Euler Method. According to the design, each joint of the bionic robotic hand has a specified degree of freedom.For each joint i, the angle is defined as θi, the angular velocity asθi, and the angular acceleration as θi.The dynamics equation for each joint can be expressed as:τi=I iθi+w i(I i w i)whereτi is the joint torque, I i is the joint inertia matrix, and w i and θi represent the joint angular velocity and acceleration, respectively.The control input is generated by the motor driver (servo), with the output being torque. Assuming the motor input for each joint is u i, the joint torque τi can be mapped through the motor's torque constant as:τi=kτ∙u iThe system dynamics equation can be described as:I iθi+b iθi+c iθi=τi−τext,iwhere b i is the damping coefficient, c i is the spring constant (accounting for joint elasticity), and τext,i represents external torques acting on the joint i, such as gravity and friction.The primary control objective is to ensure that the end-effector of the robotic hand (e.g., fingertip) can accurately track a predefined trajectory. Let the desired trajectory be denoted as y d(t)and the actual trajectory as y(t)The tracking error can be expressed as:e(t)=y d(t)−y(t)The goal of MPC is to minimize the cumulative tracking error, which is typically achieved through the following objective function:J=∑[e(k)T Q e e(k)]N−1k=0where Q e is the error weight matrix, N is the prediction horizon length.Mechanical constraints require that the joint angles and velocities must remain within the physically permissible range. Assuming the angle range of the i-th joint is[θi min,θi max]and the velocity range is [θi min,θi max]。

医学多模态可解释模型-概述说明以及解释

医学多模态可解释模型-概述说明以及解释

医学多模态可解释模型-概述说明以及解释1.引言1.1 概述概述部分的内容:医学多模态可解释模型是指使用多种医学数据来源,并结合相关的可解释模型,来解释和预测医学问题的方法。

近年来,随着医学技术的不断发展和医学数据的快速积累,使用多模态医学数据进行诊断、预测和治疗成为了一种趋势。

多模态医学数据包括但不限于电子病历数据、医学影像数据、基因组学数据等,这些数据来源不仅包含了丰富的信息,还能够提供不同角度的医学表征。

然而,由于多模态医学数据的复杂性和高维度,单一模态的分析和建模方法往往难以充分挖掘数据中的潜在规律和关联信息。

为了更好地利用多模态医学数据,可解释模型被引入其中。

可解释模型是一种能够提供人们理解其决策过程的机器学习模型,通过对模型内部的隐含特征和规律进行可解释性分析,使得医学专家和患者能够理解模型的预测结果,并从中获得有意义的信息。

因此,本文将重点研究医学多模态可解释模型的方法和应用。

首先,我们将介绍多模态医学数据的概念和特点,包括数据的来源、类型和处理方法。

然后,我们将详细介绍可解释模型的基本原理和常用算法,探讨其在医学领域中的应用场景和价值。

通过综合多模态医学数据和可解释模型的优势,我们将能够更准确地预测和诊断疾病,为医学研究和临床实践提供有力支持。

总之,医学多模态可解释模型的出现为医学研究和临床实践带来了新的机遇和挑战。

通过综合利用多模态医学数据和可解释模型的能力,我们可以更好地理解和解释医学问题,为患者提供更准确、个性化的诊疗方案。

本文将对该领域的研究进行深入探讨,希望能够为医学界的同仁和研究人员提供一定的参考和启发。

1.2 文章结构文章结构部分的内容主要是介绍和解释整篇文章的组织结构和各个部分的内容安排。

在本篇文章中,整体结构可以分为引言、正文和结论三个部分。

在引言部分,我们会首先给出对本篇文章主题的概述,简要介绍医学多模态可解释模型的背景、意义和应用领域。

接着,我们会详细介绍文章的结构,即下文将涉及的各个具体部分,以及它们在整个文章中的位置和作用。

生物大分子的动力学模拟和模型构建

生物大分子的动力学模拟和模型构建

生物大分子的动力学模拟和模型构建随着计算机技术的不断提高和生物实验技术的发展,生物大分子的动力学模拟和模型构建成为了生物学、生物医学工程学等领域的研究热点之一。

它们不仅可以揭示生物大分子的作用机制、结构和功能,还可以为设计药物和材料提供重要的参考依据。

本文将从动力学模拟和模型构建两个方面入手,介绍这一领域的基本概念、方法和应用。

一、动力学模拟动力学模拟是指利用计算机模拟生物大分子在空间及时间上的运动和相互作用规律的过程。

生物大分子的运动轨迹可以通过分子动力学(Molecular Dynamics,MD)模拟得到。

MD模拟是一种计算力学方法,它将生物大分子看作是由原子或分子构成的粒子系统,通过牛顿定律描述其受力、受力后的运动及其之间的相互作用。

MD模拟过程中,生物大分子被放在一个加热和等温系统中,同时受到保形模拟和长程库仑相互作用的作用。

由于牛顿定律是数学上精确的,MD模拟的计算结果可以准确地反映生物大分子的动态行为。

MD模拟的成功应用需要选择或开发合适的势函数和模型。

势函数是用来描述原子或分子之间相互作用的势能函数。

常见的势函数包括力场和量子力学势。

力场是利用经验参数和经验势函数进行拟合的近似函数,适用于大尺度分子的模拟。

量子力学势则是基于量子力学描述相互作用的势能函数,适用于小分子和催化反应等微观过程的模拟。

MD模拟还需要选择适当的计算方法,如时间步长、温度控制和统计学分析等。

时间步长是MD模拟中的最小时间单位。

根据时间步长的大小,可以将MD方法分为传统MD和快速MD两种。

传统MD方法时间步长较小,需要在较长时间内运行,计算时间较长;而快速MD方法时间步长较大,可以在较短时间内完成,但结果的精度会有所下降。

温度控制是MD模拟中的重要环节,主要有恒温、恒压和恒容控制等方法。

统计学分析可以对模拟结果进行分析和预测,如径迹分析、动力学分析和构象分析等。

MD模拟在生物大分子研究中的应用广泛,如蛋白质折叠、分子识别、分子对接和膜蛋白结构预测等方面。

舰载机弹射起飞结构动态响应分析方法与应用

舰载机弹射起飞结构动态响应分析方法与应用

第52卷第6期2020年12月Vol.52No.6Dec.2020南京航空航天大学学报Journal of Nanjing University of Aeronautics&Astronautics舰载机弹射起飞结构动态响应分析方法与应用杨莹1,唐克兵1,方雄1,姚小虎2(1.航空工业成都飞机工业(集团)有限责任公司,成都,610092;2.华南理工大学土木与交通学院,广州,510641)摘要:舰载机在弹射起飞过程中,载荷大、加速度大、距离短、时间短,且受大气扰动、航母运动的影响,存在复杂的强非线性多学科动力学耦合问题。

文中建立了舰载飞机‑弹射系统简耦多体动力学模型,考虑在舰面摇晃载荷、侧风载荷作用下,利用ADAMS动力学仿真软件对舰载机弹射起飞进行刚柔耦合多体动力学仿真分析,获得弹射起飞过程中飞机机体过载传递路径和应变分布。

通过仿真分析与相关文献中试验数据进行对比表明,这种仿真方法能够高效模拟强非线性复杂载荷耦合下的舰载机弹射起飞过程,为舰载机弹射起飞全过程研究及机身结构设计提供参考。

关键词:舰载机;弹射起飞;刚柔耦合多体动力学;动态响应中图分类号:V212文献标志码:A文章编号:1005‑2615(2020)06‑0957‑06Dynamic Response Analysis Method and Application of Shipboard AircraftTake⁃Off StructureYANG Ying1,TANG Kebing1,FANG Xiong1,YAO Xiaohu2(1.AVIC Chengdu Aircraft Industrial(GROUP)Co.Ltd.,Chengdu,610092,China;2.School of Civil Engineering and Transportation,South China University of Technology,Guangzhou,510641,China)Abstract:In the process of ejection take-off,shipboard aircraft are subject to large load,large acceleration,short distance and short time,as well as the influence of atmospheric disturbance and shipboard movement. There is a complex strong nonlinear multidisciplinary dynamics coupled problem.A simple multi-body dynamic model of shipboard aircraft-ejection system is established.Under the action of ship surface shaking load and cross wind load,ADAMS is used to conduct rigid-flexible coupled multi-body dynamic simulation analysis of shipboard airframe ejection take-off,and the overload transfer path and strain distribution of airframe during ejection take-off are obtained.Through the comparison between the simulation analysis and the experimental data in the related literature,it is shown that this simulation method can effectively simulate the ejection take-off process of shipboard aircraft under the strong nonlinear and complex load coupling,providing reference for the whole process research of shipboard aircraft ejection take-off and the design of fuselage structure.Key words:shipboard aircraft;ejection take‑off;rigid-flexible coupled multi-body dynamics;dynamic responseDOI:10.16356/j.1005‑2615.2020.06.015基金项目:国家自然科学基金(11372113,11472110,11672110)资助项目。

空间柔性机械臂的动力学建模和分析

空间柔性机械臂的动力学建模和分析

工学硕士学位论文
空间柔性机械臂的动力学建模和分析
硕 士 研 究 生: 魏 导

师: 梁廷伟 高工
申 请 学 位 : 工学硕士 学 科: 一般力学与力学基础
所 在 单 位: 航天学院 答 辩 日 期: 2013 年 7 月 授予学位单位 : 哈尔滨工业大学
Classified Index: O326,V11 U.D.C: 531.3, 34.1- III -
哈尔滨工业大学工学硕士学位论文



要 ..........................................................................................................................I 论 ........................................................................................................... 1
- II -
哈尔滨工业大学工学硕士学位论文
determined. Through the frequency equation presented in this article, a study on the variation of natural frequencies with the time-varying elbow angles is performed using a two-link flexible manipulator with a set of typical geometrical parameters and material constants. Thus we can predict the tendency and range of the system natural frequency. Keywords: flexible manipulator, flexible-joint, natural frequencies , global mode shapes

基于Matlab和VR技术的移动机器人建模及仿真

基于Matlab和VR技术的移动机器人建模及仿真

文章编号:100422261(2004)0120039204基于Matlab 和VR 技术的移动机器人建模及仿真Ξ葛为民1,2,曹作良2,彭商贤1(1.天津大学机械工程学院,天津300072;2.天津理工学院机械工程学院,天津300191)摘 要:利用Matlab 建立移动机器人的动力学模型,在虚拟现实(VR )环境下,实时仿真移动机器人路径跟踪的运动特性,为基于Internet 的机器人遥操作试验搭建了仿真平台.实验结果表明,虚拟模型准确地模拟了真实移动机器人的动力学特征;通过对模型的参数修改,为实现对真实机器人的最优控制和设计提供了可信的参考方案.关键词:Matlab ;虚拟现实;移动机器人;遥操作中图分类号:TP242.2 文献标识码:ADynamic modeling and simulation of mobilerobot based on matlab and VR technologyGE Wei 2min 1,2,C AO Zuo 2liang 2,PE NG Shang 2xian 1(1.School of Mechanical Eng.,T ianjin University ,T ianjin 300072,China ;2.School of Mechanical Eng.,T ianjin Institute of T echnology ,T ianjin 300191,China )Abstract :This paper proposes an approach that develops a dynam ic m odel of a m obile robot taking advantage of the M atlab.M eantime ,in a developed virtual reality environment ,the built m odel simulates the m otion of path tracking and obstacle av oidance.Furtherm ore ,it provides a platformfor experiments of m obile robot teleoperation.The experi 2mental results approve that ,the virtual m odel represents the dynam ic properties of real robot accurately and ,w ith the change of parameters of the virtual m odel ,it helps to find out the optim ization methods of controlling and designing the m obile robot indeed.K eyw ords :M atlab ;virtual reality ;m obile robot ;teleoperation 在当今工业现代化的高速发展时期,特别是自动化设备在各个领域的广泛应用,移动机器人(AG V )的应用越来越显示出它的重要性和优越性.AG V 的重要特征是它的可移动性,对这种可移动性的控制是AG V 研制的核心问题.课题组研制的T UT -1型AG V 采用3种传感器(磁导航传感器、CC D 摄像机、超声波传感器)跟踪磁条来对AG V 进行引导和避障,经过这3种传感器的信息融合,测算出AG V 的位置和运动方向作为反馈与给定的运动状态进行比较,来调整AG V 下一步的运动[1]. 在天津市自然科学基金的资助下,课题组利用T UT -1这个平台开展基于Internet 的AG V 遥操作系统的研究.为模拟AG V 的运动特性,利用Matlab 进行AG V 的动力学建模.同时,在虚拟现实环境下,利用Matlab 模型仿真AG V 的路径跟踪,研究和探索AG V 最优的控制和配置方案.1 实验和建模过程 如图1所示,T UT -1移动机器人在室内进行导航和避障的实验[2].AG V 通过磁导航传感器和CC D 摄像机跟踪磁条引导前进,当AG V 接近墙壁时,通过超声传感器引导.AG V 将实时采集到的磁条位置信息作为反馈,与给定的磁条标准位置信息进行比较来调整Ξ收稿日期:2003212225 基金项目:天津市自然科学基金资助项目(023615011) 第一作者:葛为民(1968— ),男,讲师,博士研究生 第20卷第1期2004年3月天 津 理 工 学 院 学 报JOURNA L OF TIAN JIN INSTITUTE OF TECHN OLOG Y V ol.20N o.1Mar.2004AG V 下一步的运动,达到实时控制AG V 跟踪磁条的目的.图1 TUT 21移动机器人Fig.1 TUT 21mobile robot 在仿真环境下,利用虚拟现实(VirtualReality )建模工具W orldUp 构建了AG V 运行的虚拟仿真环境场景,基于Matlab 构建AG V 仿真模型,通过模拟AG V 的动力学特性,来模拟AG V 的运动特行,通过在线修改虚拟AG V 的特性参数,来研究控制AG V 运动的最佳方案. 图2为AG V 车体结构简图[3].图2 车体结构简图Fig.2 Sketch of AGV body structure AG V 两后轮为驱动轮,分别由两台电机驱动,每台电机与后轮各构成一个速度闭环,为恒速输出.在工作载荷内,调节两电机的输入电压即可调节两后轮的转速;AG V 两前轮为随动轮,仅起到支撑车体的作用而无导向作用. 仿真算法原理是比较每一时刻AG V 所在位置的坐标值和终止坐标点的差别来计算处理两个坐标点之间的x 、y 值之间的误差,以当前AG V 姿态角和终止位置姿态角的差值作为输入量,来计算下一步AG V 的位移,也就是输出下一步AG V 到达的坐标和姿态角,从而控制AG V 向终点行进. 图3为AG V 运动学建模流程图.图3 AGV 动力学模型流程图Fig.3 F low ch art of the AGV dyamics model 现就其中的主要模块建模过程介绍如下[4]: 1)误差计算模块:本模块的作用是进行误差计算,通过比较机器人所在坐标点和终止坐标点的差别来计算处理两个坐标点之间的xy 值的误差和角度误差本模块接收5个信号(初始点的xy 坐标值,终止点的xy 坐标值,和角度值),输出两个信号(坐标值误差,角度误差). 初始点的y 坐标值与终止点的y 坐标值通过sum 模块进行求和运算,算出两个坐标值的差值,同时终止点的y 坐标值通过g oto 模块传出,同样的,对两个x 坐标值进行计算,求出差值.把计算出来的y 坐标值的差值与x 坐标值的差值通过T rig onometry 模块求出两值相除所得数的反正切函数,也就是求出倾斜角的弧度,所得值通过gain 模块与-1相乘,再通过sum 模块与角度值求出差值,所得差值通过Abs 模块求出绝对值,然后和π值比较(Relational operator 模块,如果满足条件,返回值为1),如果小于或等于π值,则直接与差值相乘,如果大于π值,则乘以2π然后和差值的绝对值相减,然后再与差值通过sign 模块所得的值相乘,最后两值相加,即为角度值的误差值. 2)PI D 控制模块:误差计算模块输出两个信号・04・天 津 理 工 学 院 学 报 第20卷 第1期 thetaError 和xyError ,两个信号分别通过PI D 控制模块,通过闭环回路控制,分别得出DeltaU 和Uavg ,计算公式为: theta-gain =theta-gain-pr 3theta-error (t -1)+theta-gain -int 3tinc 3sum (theta-error )+(theta-gain-der/tinc )3theta-error (t -1); y-gain =y -gain-pro 3y-error (t -1)+y-gain-int 3tinc 3sum (y -error )+(y-gain-der/tinc )3y -error (t -1); M ove-U (t -1)=theta-gain 3theta-gain-mult +y-gain 3y-gain-mult ; Delta-U (t -1)=sign (M ove-U (t -1))3in (abs (M ove-U (t -1)),23Max-M otor-V oltage ); U (t -1)=(23Max-M otor-V oltage -abs (Delta-U (t -1)))/2; 图4为PI D 控制在Matlab/Simulink下的仿真结构图.图4 PI D 控制模块仿真结构图Fig.4 Diagram of PI D simulation structure 3)扭矩计算模块:此模块用于计算AG V 轮子的扭矩,输入参数为“步进转速模块”的输出量、电动机本身的性能参数和减速器的传动比来算出扭矩,公式如下: Mn2(t -1)=G earbox-Ratio 3(K a 3U2(t -1)-K b 3omega-d2(t -1)); Mn1(t -1)=G earbox-Ratio 3(K a 3U1(t -1)-K b 3omega-d1(t -1)); 图5为扭矩计算在Matlab/Simulink 下的仿真模型结构图. 4)线性移动计算模块:此模块利用AG V 的物理参数,重量、轮子半径、轮子和地面摩擦力和在3)中输出的扭矩计算AG V 的速度和加速度.计算公式为: Accel-veh (t )=(Mn2(t -1)+Mn1(t -1)-23Front-Wheel-Friction (t -1)3Wheel-Radius )/(Mass-veh 3Wheel-Radius ); Vel-veh (t )=Vel-veh (t -1)+Accel-veh (t -1)3tinc ; Disp-veh (t )=Vel-veh (t )3tinc +0.53Accel-veh (t-1)3tinc^2; 图6为AG V 线性移动在Matlab/Simulink 下的仿真结构图.图5 扭矩计算模块仿真结构图Fig.5 Diagram of torque calc simulation structure图6 线性移动模块仿真结构图Fig.6 Diagram of linear motion calc simulation2 仿真运行 仿真系统运行环境为操作系统Windows2000Serv 2er ,虚拟现实插件为Micros oft VRM L Viewer 2.0,仿真建模和科学计算软件为Matlab Release13(Matlab V6.5/Simulink V5.0),运行界面见图7. 为检验虚拟AG V 的运行情况,现将磁条的位置坐标建立数据库,输入模型中作为路径跟踪的基准,用图形同时输出磁条路径和虚拟AG V 跟踪磁条运行的轨迹,用以直观比较.图8为经过一个周期运转后的轨迹图,左图为磁条基准路径,右图为虚拟AG V 的运行轨迹.・14・ 2004年3月 葛为民,等:基于Matlab 和VR 技术的移动机器人建模及仿真图7 仿真运行界面Fig.7 I nterface ofsimulation(a)(b )图8 基准路径和跟踪路径的比较Fig.8 Comp arison of the stand ardp ath and tracking p ath3 结 论 从图8的(b )图中可以看出,虚拟AG V 模型的运动轨迹基本上与(a )图的磁条轨迹相吻合,证明AG V 建模算法准确,参数选择合理,可以按照此参数配置修改真实AG V 属性各项指标,达到最优轨迹跟踪控制. 总之,利用Matlab 在虚拟现实环境下构建AG V 虚拟模型,达到了以下设计目标: 1)完成了真实AG V 与虚拟AG V 的匹配,真实反映了AG V 的运动学和动力学特征,为对AG V 的遥操作奠定了实现基础; 2)通过在线修改虚拟AG V 参数,快速检验对AG V的控制策略和最优配置的影响,同时减少了修改真实样机时间的延迟,降低了修改配置真实样机的费用.如AG V 的载荷问题,速度改变问题,传动比改变问题等造成的控制稳定性. 3)基于虚拟现实的仿真平台,由于VRM L 文件的特殊性,利于在Internet 上的运行分布式控制,故本仿真平台为基于Internet 的AG V 的遥操作进行了有益的尝试.参 考 文 献:[1] Weimin G e ,Zuolian Cao ,Shangxian Peng.Web -based teler 2obotics system in virtual reality environment [A].Proceedings of the SPIE Intelligent R obots and C om puter Vision C on ference [C].US A :SPIE Oct ,2003.[2] Weimin G e ,Zuoliang Cao ,Shangxian Peng.A T elerobotic Sys 2tem Based on Virtual Reality T echnique [A ].Proceedings of Virtual Reality Application in Industry [C ].US A :SPIE ,Oct ,2003.[3] 赵新华,曹作良.可移动机器人的运动学模型与控制原理[J ].机器人,1994,16(4):215—218.[4] 王沫然.S imulink 4建模及动态仿真[M].北京:电子工业出版社,2002.・24・天 津 理 工 学 院 学 报 第20卷 第1期 。

刚柔耦合动力学模型

刚柔耦合动力学模型

刚柔耦合动力学模型
刚柔耦合动力学模型是一种模拟柔性物体在刚性结构体上运动和互动的模型。

它是基于多体动力学和弹性理论的复杂模型,通常用于机器人的机械臂、手指、足部等柔性部件的控制和仿真。

在这个模型中,刚性部件和柔性部件之间相互作用,并且对于柔性物体,则采用比较精确地黎曼曲面理论表示。

动力学模型包含了刚性部件的质量、几何结构、摩擦和约束力以及柔性物体的刚度、阻尼和粘滞阻尼。

在这个模型中,刚性结构体可以被表示成结构体中的多个质点,这些点可以通过使用牛顿运动定律和质点系统动力学方程进行运动学和动力学分析。

而柔性物体则可以通过有限元分析进行数值求解和建模,并考虑其非线性本质。

这个模型的分析使得我们可以预测柔性物体在刚性结构体上的运动和应变情况。

刚柔耦合动力学模型的成功建立与应用,为控制机器人手指、足部等柔性部件的制造和控制提供了有效的数学工具。

在现代机器人领域,一些先进的机器学习算法和控制方法已经被成功地应用到刚柔耦合动力学模型中,使得机器人系统的性能和精度得到了大幅提升。

同时,这个模型也为金属材料、塑料材料等柔性材料的应用和制造提供了有力的理论参考。

总之,刚柔耦合动力学模型对于研究和控制复杂机器人和柔性材料产生了重要的价值,为领域的发展奠定了坚实的理论基础。

柔性多体动力学建模

柔性多体动力学建模

柔性多体动力学建模、仿真与控制近二十年来,柔性多体系统多力学(the dynamics of the flexible multibody systems)的研究受到了很大的关注。

多体系统正越来越多地用来作为诸如机器人、机构、链系、缆系、空间结构和生物动力学系统等实际系统的模型。

huston认为:“多体动力学是目前应用力学方面最活跃的领域之一,如同任何发展中的领域一样,多体动力学正在扩展到许多子领域。

最活跃的一些子领域是:模拟、控制方程的表述法、计算机计算方法、图解表示法以及实际应用。

这些领域里的每一个都充满着研究机遇。

”多柔体系统动力学近年来快速发展的主要推动力是传统的机械、车辆、军械、机器人、航空以及航天工业现代化和高速化。

传统的机械装置通常比较粗重,且*作速度较慢,因此可以视为由刚体组成的系统。

而新一代的高速、轻型机械装置,要在负载/自重比很大,*作速度较高的情况下实现准确的定位和运动,这是其部件的变形,特别是变形的动力学效应就不能不加以考虑了。

在学术和理论上也很有意义。

关于多柔体动力学方面已有不少优秀的综述性文章。

在多体系统动力学系统中,刚体部分:无论是建模、数值计算、模拟前人都已做得相当完善,并已形成了相应的软件。

但对柔性多体系统的研究才开始不久,并且柔性体完全不同于刚性体,出现了很多多刚体动力学中不呈遇到的问题,如:复杂多体系统动力学建模方法的研究,复杂多体系统动力学建模程式化与计算效率的研究,大变形及大晃动的复杂多体系统动力学研究,方程求解的stiff数值稳定性的研究,刚柔耦合高度非线性问题的研究,刚-弹-液-控制组合的复杂多体系统的运动稳定性理论研究,变拓扑结构的多体系统动力学与控,复杂多体系统动力学中的离散化与控制中的模态阶段的研究等等。

柔性多体动力学而且柔性多体动力学的发展又是与当代计算机和计算技术的蓬勃发展密切相关的,高性能的计算机使复杂多体动力学的仿真成为可能,特别是计算机的功能今后将有更大的发展,柔性多体必须抓住这个机遇,加强多体动力学的算法研究和软件发展,不然就不是现代力学,就不是现代化。

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= =
d / dt d 2 / dt 2
A T
= = =
aerodynamic thrust components of respective variable in body coordinates
i =0,1,2,3,4 = fuselage, right inner and outer wing, left inner and outer wing
= = control surface deflection angle angle between thrust and ob xb
Superscripts (.) (..) Subscripts
2 American Institute of Aeronautics and Astronautics
The purpose of this paper is to model and simulate a tailless folding wing morphing aircraft in wing folding process. The wing area and other parameters of morphing aircraft largely change when morphing, which will affect the aerodynamic force acting on the aircraft and lead to a variation of movements in the morphing process. The dynamic model of tailless folding wing morphing aircraft in wing folding process was founded and the six-DOF nonlinear equation was deduced. Furthermore, the decoupled longitudinal dynamic equation of morphing process was presented by simplifying. And the result of numeric simulation of aerodynamic force in wing folding process shows that the aerodynamic force is approximate to the steady state. The longitudinal response of morphing process in different wing fold angular velocities was numerically simulated by quasi-steady aerodynamic assumption when taking no account of the unsteady aerodynamic effect in small wing fold angular velocities. And the key factors which affect the dynamical characteristics of morphing process of the tailless folding wing morphing aircraft were investigated by quantity. The research results can be as great reference for the flight control system design in morphing and evaluating the morphing flight safety at low altitude of morphing aircraft.
change. Moreover, the aerodynamic force acting on the aircraft also change, which will affect the movement of the morphing aircraft. So wing morphing is a very important process for morphing aircraft. If some kinetic parameters of the morphing aircraft change unexpectedly, the flying quality may be affected and will even threaten the flight safety of the morphing aircraft. So it is necessary to analysis the dynamic response of morphing aircraft in the morphing process. The wing morphing process of morphing aircraft is similar with the process of changing the sweep angle of variable swept wing aircraft. Ref. 3 investigated the dynamic response of a variable swept wing aircraft in the course of changing the sweep angle. But for morphing aircraft, the range of parameters changing will be wider. In the wing morphing process, the morphing aircraft should be regarded as a multibody system which is composed by the fuselage and several moving parts of wing. Thomas M Seigle4 analyzed the modeling of large-scale morphing aircraft in theory. However, few studies have been done on the modeling and simulation of a specific morphing aircraft. This paper focuses on modeling and simulation of a tailless folding wing morphing aircraft during the wing fold process, which can be based as the flight control design for morphing aircraft.
x, y , z
I. Introduction
M
orphing is that the aircraft can adapt the different flight environments and combat missions by changing the aerodynamic configuration, using advanced materials and actuators.1,2 For example, the morpdent, AIAA Student Member, School of Aeronautic Science and Engineering, email: yueting_buaa@ † Professor, School of Aeronautic Science and Engineering, email: bhu_wlx@ ‡ Researcher
Nomenclature
CD 0 CDV CDα CL 0 CLV CLq C Lα
= = = = = = =
drag coefficient at zero CL
ˆ) ∂CD / ∂ (V ∂CD / ∂α ˆ) ∂CL / ∂(V ˆ ∂CL / ∂q ∂CL / ∂α
lift coefficient at zero
Ting Yue * and Lixin Wang † School of Aeronautic Science and Engineering Beijing University of Aeronautics and Astronautics Beijing, China, 100191 Junqiang Ai ‡ The First Aircraft Institute of AVIC-I, Xi’an, China, 710089
AIAA Atmospheric Flight Mechanics Conference 10 - 13 August 2009, Chicago, Illinois
AIAA 2009-6155
Multibody Dynamic Modeling and Simulation of a Tailless Folding Wing Morphing Aircraft
1 American Institute of Aeronautics and Astronautics
Copyright © 2009 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved.
C Lα C Lδ Cm 0 CmV Cmq Cmα Cmα Cmδ c D F Gw g Η I Ii L M m ob xb yb zb og x g y g z g p q S S T V
= = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
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