DGR—5A机器人的动力学及控制

摘要 DGR-5A 机器人是我们自行设计的五自由度全关节型电动机器人.本文讨论了该机器人的动力学及控制问题.为此对机器人的各杆臂建立坐标系并详尽地计算了各杆臂的惯性参数.在推导了该机器人前三个自由度的动力学方程和分析了动力学特性之后,我们提出了一种实时计算量少,易于工程实现的控制方案.最后对该方案进行了仿真研究,仿真的结果是颇为令人满意的.     

一种机器人动力学方程快速计算的三角函数发生器

在机器人动力学方程的快速计算中，三角函数的计算占了很大的计算比例， 如何快速地计算三角函数是动力学方程计算要解决的重要任务之一．本文在简单介绍了CORD IC算法后，提出一种位并行迭代的CORDIC结构来实现三角函数的运算，随后给出了该结构的 FPGA实现及仿真结果，并给出计算速度与精度分析．该结构兼顾芯片面积和计算速度，易于 VLSI实现．     

机器人计算力矩不确定性的神经网络补偿控制*

提出一种由计算力矩控制器和神经网络补偿控制器相结合的控制方案,探讨了用神经网络补偿机器人计算力矩不确定性的方法,推导了网络权值的自适应调整律,并证明了系统的稳定性和误差的收敛性.该方案结构简单、鲁棒性强,且神经网络补偿器有较好的适应性,无须事先知道机器人动力学参数和结构的精确值.对机器人轨迹跟踪的仿真结果表明,所提方案具有很好的鲁棒性和抗干扰能力.     

机器人动力学并行计算

本文综述了并行计算在机器人动力学中的应用.简单介绍了并行计算机的各种结构以及并行算法的一些特点,重点介绍了以牛顿—欧拉方程为动力学初始模型的并行计算任务的调度以及流水线计算,对数递推计算等算法,最后还介绍了一种并行近似计算方法.     

自由漂浮空间机器人回避动力学奇异的轨迹规划

针对非完整约束导致的自由漂浮空间机器人广义雅可比矩阵动力学奇异性问题,提出一种间接求解机器人逆运动学的方案.该方案通过运动方程分解并构造迭代函数,将动力学奇异转换为运动学奇异问题,避免直接求解逆广义雅可比矩阵,解决广义雅可比矩阵动力学参数相关特性问题,仿真结果表明该方法能够有效地实现自由漂浮空间机器人回避动力学奇异的非完整轨迹规划.     

基于凯恩方法的机器人动力学建模与仿真

研究机器人动力学的目的是多方面的.机器人动力学模型的重要应用是设计机器人,因为动力学方程可以用来精确地算出实现给定运动所需要的力(矩).用凯恩方法对六自由度关节臂式机器人进行动力学方程的推导,可以减少计算步骤,提高计算效率.经过对推导的动力学方程仿真可以找到现场使用机器人第三个臂出现故障的原因,因此在机器人具体设计、轨迹规划、动力学优化以及实时控制中可以代入具体的参数,从而时机器人的动力学性能进行分析,并具有通用性.     

一种高效能的机器人模糊控制方案

本文提出一种高效能的模糊控制方案,来提高机器人当存在摩擦力和负载等不确定因素 时以及动力学参数变化时的系统响应特性.该控制方案是由一个模糊逻辑(FL)控制器(主 控制器)和一个传统的微分(D)控制器(辅助控制器)所构成.FL控制器用来提高系统的瞬 态特性和稳态精度,D控制器用来保证系统的稳定性.在这一控制方案基础上,获得理想控 制特性的主要思想是研究和调整语言变量的隶属度函数.模拟结果表明了这一控制方案的 有效性和鲁棒性.此外,这一控制方案具有结构简单且易于实现的优点.     

含弹簧阻尼缓冲机构空间机器人捕获卫星操作的 避撞柔顺强化学习控制

针对空间机器人捕获卫星操作过程中,关节因受冲击载荷而易造成冲击破坏的问题,在关节电机与机械臂之间设计了一种弹簧阻尼缓冲机构.缓冲机构不仅能利用弹簧实现捕获操作过程的柔顺化,利用阻尼器实现碰撞能量的吸收及柔性振动的抑制;还能通过合理设计与之配合的避撞柔顺策略使关节所受冲击力矩限定在安全范围内.首先,分别利用含耗散力Lagrange方程法与Newton-Euler法导出了碰撞前的空间机器人与被捕获卫星的分体系统动力学方程;然后,结合Newton第三定律、捕获点的速度约束、各分体的位置约束获得了捕获后的混合体系统动力学方程,且基于动量守恒关系计算了碰撞冲击效应与冲击力;最后,提出了一种结合缓冲机构的避撞柔顺强化学习控制方案,该方案通过实时与动态环境试错交互得到惩罚信号,并利用惩罚信号对控制器进行优化,实现对失稳混合体系统的镇定控制.利用Lyapunov定理证明了系统的稳定性;数值仿真验证了缓冲结构的抗冲击性能及所提策略的有效性.     

采用惯性测量单元的移动机器人轨迹跟踪方法研究

对于非完整移动机器人的轨迹跟踪控制已有很多方法提出,但是这些方法或者是基于动力学模型或者是采用复杂的运动学模型,对于缺少强大计算设备且需要实时控制的工程应用是不适合的.本文针对非完整移动机器人提出了一种基于比例微分(proportional-differential,PD)控制器的实时轨迹跟踪控制方法.该方法运行在40 MHz的嵌入式控制器上的控制周期只有1～2 ms.通过将一个用于直流电机控制的非线性PID速度控制器与提出的轨迹控制器进行集成,实现了一个轮式移动机器人的运动控制.机器人轨迹跟踪实验系统中采用微机电系统(micro electro-mechanical system,MEMS)惯性测量单元检测轮式移动机器人的偏航角,实验结果验证了提出方法的有效性.     

基于对象特征模型描述的舱外自由移动机器人地面实验系统预测控制方法

本文基于对象特征建模的思想,在关节角空间和任务空间建立了空间机器人系统的特征模型,提出了一种基于特征模型的舱外自由移动机器人地面实验系统的单步预测控制方法。该方法不需计算机器人动力学方程,不需在线调整控制器参数,具有很强的适应能力,同时设计简单,使用方便,计算量小,适合工程应用。     

Computer-aided-design tool for simplification of robot dynamic model based on manipulator system performance

The dynamic performance of a robot manipulator is directly dependent on the efficiency of the controller and the dynamic model of the robot. This paper addresses the fundamental issue of how much manipulator dynamics information should be included in the manipulator dynamic model for control such that the manipulator will achieve the desired system performance under a proportional-plus-derivative control scheme. An efficient minimax simplification scheme has been developed which automatically generates simplified closed-form manipulator motion equations in symbolic form while maintaining the desired manipulator system performance under a proportional-plus-derivative controller. The scheme involves the identification and selection of basis functions that represent the dynamic coefficients in the dynamic model. These basis functions consist of a linear combination of the product terms of sinusoidal and polynomial functions of the generalized coordinates and form a Chebyshev set on the workspace of the manipulator. A multi-layered decision scheme is developed for selecting significant basis terms in each layer for each dynamic coefficient. The linear combination of these significant basis terms is then utilized to construct each simplified dynamic coefficient based on the minimax approximation technique. A verification of the proposed scheme on a Stanford robot arm is included for discussion.     

弧焊机器人焊缝跟踪神经网络控制器

介绍了一种能提高高弧焊机器人焊缝跟踪精度的神经网络控制器,通过神经网络的补偿作用,弥补了由于无法知道机器人精确模型所造成的控制上的误差,不同于机器人控制中传统的网络控制器,本文提出并应用了基于笛卡尔空间轨迹控制的机器人焊缝跟踪神经网络,大大简化了控制算法,计算机模拟及实验表明,该控制器非常适用于只的实际焊接,对于现有机器人,无须改变其控制器内部结构,即可应用该技术,与常用的机器人关节力矩控制法相比     

Fast parallel Preconditioned Conjugate Gradient algorithms for robot manipulator dynamics simulation

In this paper fast parallel Preconditioned Conjugate Gradient (PCG) algorithms for robot manipulator forward dynamics, or dynamic simulation, problem are presented. By exploiting the inherent structure of the forward dynamics problem, suitable preconditioners are devised to accelerate the iterations. Also, based on the choice of preconditioners, a modified dynamic formulation is used to speedup both serial and parallel computation of each iteration. The implementation of the parallel algorithms on two interconnected processor arrays is discussed and their computation and communication complexities are analyzed. The simulation results for a Puma Arm are presented to illustrate the effectiveness of the proposed preconditioners. With a faster convergence due to preconditioning and a faster computation of iterations due to parallelization, the developed parallel PCG algorithms represent the fastest alternative for parallel computation of the problem withO(n) processors.     

Adaptive Control of a Two-Link Robot Using Batch Least-Square Identifier

We design a regulation-triggered adaptive controller for robot manipulators to efficiently estimate unknown parameters and to achieve asymptotic stability in the presence of coupled uncertainties. Robot manipulators are widely used in telemanipulation systems where they are subject to model and environmental uncertainties. Using conventional control algorithms on such systems can cause not only poor control performance, but also expensive computational costs and catastrophic instabilities. Therefore, system uncertainties need to be estimated through designing a computationally efficient adaptive control law. We focus on robot manipulators as an example of a highly nonlinear system. As a case study, a 2-DOF manipulator subject to four parametric uncertainties is investigated. First, the dynamic equations of the manipulator are derived, and the corresponding regressor matrix is constructed for the unknown parameters. For a general nonlinear system, a theorem is presented to guarantee the asymptotic stability of the system and the convergence of parameters’ estimations. Finally, simulation results are discussed for a two-link manipulator, and the performance of the proposed scheme is thoroughly evaluated.      

Observer-based adaptive control of robot manipulators: Fuzzy systems approach

This paper presents a fuzzy adaptive control suitable for motion control of multi-link robot manipulators with structured and unstructured uncertainties. When joint velocities are available, full state fuzzy adaptive feedback control is designed to ensure the stability of the closed loop dynamic. If the joint velocities are not measurable, an observer is introduced and an adaptive output feedback control is designed based on the estimated velocities. In the proposed control scheme, we need not derive the linear formulation of robot dynamic equation and tune the parameters. To reduce the number of fuzzy rules of the fuzzy controller, we consider the properties of robot dynamics and the decomposition of the uncertainties terms. The proposed controller is robust against uncertainties and external disturbance. Further, it is shown that required stability conditions, in both cases, can be formulated as LMI problems and solved using dedicated software. The validity of the control scheme is demonstrated by computer simulations on a two-link robot manipulator.     

关于机器人动力学模型的线性化

本文给出了一种沿标称轨迹线性化机器人动力学模型的有效方法.方法具有简单、系统、以及计算量小的优点。对于一个具有6自由度的机器人,用本文的方法建立它的线性化动力学模型(包括沿标称轨迹的广义力计算)所需要的最大计算量为2600个乘法与2711个加法,它可以在微型或小型计算机上实时实现。在计算上,本文的方法是现存所有方法中计算速度最快的.     

Adaptive control of a 3-DOF parallel manipulator considering payload handling and relevant parameter models

Model-based control improves robot performance provided that the dynamics parameters are estimated accurately. However, some of the model parameters change with time, e.g. friction parameters and unknown payload. Particularly, off-line identification approaches omit the payload estimation (due to practical reasons). Adaptive control copes with some of these structural uncertainties. Thus, this work implements an adaptive control scheme for a 3-DOF parallel manipulator. The controller relies on a novel relevant-parameter dynamic model that permits to study the cases in where the uncertainties affect: (1) rigid body parameters, (2) friction parameters, (3) actuator dynamics, and (4) a combination of the former cases. The simulations and experiments verify the performance of the proposed controller. The control scheme is implemented on the modular programming environment Open Robot Control Software (OROCOS). Finally, an experimental setup evaluates the controller performance when the robot handles a payload.     

A Frequency-Dependent Direct Adaptive Scheme for Robot Manipulators: A Case of Model-Following Motion Control

This paper presents a new frequency-dependent direct adaptive scheme for the optimal and/or suboptimal tracking of the motion of a prescribed model. The idea is based on a closed-loop control scheme in which an _{2 H} optimal/suboptimal controller is applied in parallel with a direct adaptive technique to guide a robot manipulator to follow a certain prescribed trajectory. The H ₂ compensators have to be proper and positively bounded with respect to all dynamic frequencies. Robustness issues are addresses in the paper by lumping all the nonlinear dynamic terms such as the centrifugal and Coriolis effects as well as the mechanical and electrical friction forces of the robot arm, into a general unstructured uncertainty term.     

受限机器人神经形态的变结构位置/力控制

本文基于Ｊｅａｎ和Ｆｕ（１９９３）建立的受限机器人模型的降型阶形式，利用变结构系统理论，设计了具有未知动态的受限机器人轨道／力追踪控制，提出的学习方法仅仅利用了机器人动态模型的一般结构，不需要其精确信息，计算迅速，易于实现，仿真结果验证了提出的方法的有效性。