柔性机械手加速度反馈消振的进一步研究

在柔性机械手的控制中,加速度反馈的方法是一种消除末端振动的有效而简便途径。然而,目前对其有效性尚无严格的理论证明和分析。本文将通过非约束模态分析方法对单柔性臂机械手的动力学方程进行了分析,以此为基础对加速度反馈的理论和实现进行了研究和讨论,得到了一些重要的结论。     

机器人的柔性关节机械手控制研究

研究柔性关节机械手的自适应控制策略,当机械手系统参数准确已知时,传统的反演控制算法可以根据状态反馈对柔性关节机械手进行控制.但是机械于模型参数存在误差时,传统的控制方法需要关爷加速度反馈,这将对柔性关节机械手的控制信号将引入噪声,能够破坏系统的动态品质.为解决上述问题,在反演控制算法的基础上引入鲁棒性,提出了鲁棒自适应反演控制算法.在已知模型误差界的条件下,通过神经网络对误差在线自学习,实现了无需模型的柔性关节自适应控制.与传统算法相比,新方法对未知扰动等模型具有鲁棒性及全局稳定性,同时不需要关节加速度信息反馈.     

基于肌力信号与电刺激感觉反馈的多自由度机械手人机交互控制

为使操作者能够灵活控制多自由度机械手并能感受到机械手的抓取力,提出了一种具有双向信息传输能力的可穿戴式人机交互系统及控制方法.该系统利用压力传感器(FSR)阵列采集与操作者手部动作对应的前臂肌力信号,基于SVM(支持向量机)多类分类器算法实现对手部动作的识别,通过发送动作模式码控制机械手动作.另外,基于经皮神经电刺激(TENS)原理,将机械手抓取力信号转变为电刺激信号刺激体表皮肤,实现机械手抓握力向人体的感觉反馈.实验表明,基于肌力信号和SVM分类器的动作模式识别方法可实现对10种手部动作的识别,成功率不低于95%;电刺激感觉反馈可向人体准确反馈抓取力感并实现盲抓取.     

柔性机械手的模糊预测组合控制与实验

本文研究了柔性机械手的关节PID,应变PD和端点加速度反馈的的模糊预测组合控 制及其实验．与其它方法的实验现象和结果比较,表明这种模糊预测组合控制方法能够控制 单杆柔性机械手期望的关节运动,能较好抑制运动过程中的弹性变形和消除端点残余振动．     

七自由度番茄收获机械手动力学研究

在番茄收获机械手动力特性优化控制的研究中,采用Lagrange法,建立了七自由度番茄收获机械手的动力学模型,获得了机械手力、质量和加速度以及力矩、转动惯量和角加速度之间的关系,并得到了机械手在完成指定运动时各关节驱动器所需要的驱动力(力矩).利用Pro/E和ADAMS建立了机械手的虚拟样机,分别在MATLAB和ADAMS中进行了动力学仿真.结果表明,利用动力学模型与虚拟样机得到的各关节作用力(矩)均呈规律性变化,除关节2和关节4外,各关节力(矩)值是相同的.两种方法得到的关节2和关节4力(矩)的最大相对误差为3.76％,验证了动力学模型的正确性和虚拟样机的可靠性,为番茄收获机械手轨迹跟踪控制提供了动力学模型和基于虚拟样机的被控对象,并为优化设计提供了依据.     

模型预测控制方法在机械手操作中的应用综述

文章综述了模型预测控制在机械手中的应用现状,介绍了模型预测控制的发展、特点和主要内容,比较了基于机械手线性模型与非线性模型预测控制的方法及应用,指出了它们各自的优势与局限性,得出了对于多自由度机械手适合采用线性模型预测控制,对于自由度较少的机械手适合采用非线性模型预测控制的结论.     

机器人操作器力控制稳定性分析

当机械手同刚性环境接触时，机械手力控制难于保持系统的稳定性，这就是机械手力控制动态不稳定性，本文分析了机械手力控制未建模特性：１）机械手驱动电机动态特性；２）力传感器动态特性；３）接触环境的动态特性；４）机械手力控制采样控制延迟等对机械手力控制稳定性的影响，最后给出了仿真及实验研究结果。     

基于多感知的空间机械手爪控制研究

对融合了视觉、滑觉、角位移等多种传感器的欠驱动空间机械手爪,研究其对不同形状、质地的物体实现自适应抓取控制。通过传感器反馈控制机械手运动、抓取力,提高机械手的自主能力。在抓取模式选择中,采用基于专家系统的抓取规划,根据物体不同的形状、尺寸选择不同的抓取模式;在抓取力控制中,通过由PVDF制作的滑觉传感器反馈,采用基于滑觉信号的模糊控制方法,对不同质地的物体选择不同的控制参数。通过实验研究验证基于多感知的控制方法对各种物体可以进行可靠的抓取。     

具有固定重力补偿的模糊免疫PD控制在气动人工肌肉驱动机械手位置控制中的应用

为了克服具有固定重力补偿的传统PD控制存在的不足,提高气动机械手位置控制的精度和控制系统的适应性和鲁棒性,借鉴生物免疫反馈响应过程的调节作用和模糊推理逻辑可逼近非线性函数的特性,将模糊控制算法和免疫反馈机理与具有固定重力补偿的传统PD控制算法相结合,提出了具有固定重力补偿的模糊免疫PD控制算法,并将它应用到气动人工肌肉驱动的机械手的固定点位置控制中.实验结果表明,该控制方法的控制性能优于常规的PD控制,具有一定的实际应用价值.     

工业机器人的学习控制

加速度传感器装在机械手手部,各关节的加速度由加速度分解算法得到.然后,提出了一种学习控制法,这种控制法利用加速度误差校正驱动器运动.并提出了一种基于几何级数的极限条件估计学习控制过程收敛条件的理论方法.本文所提出的学习控制理论的有效性通过 PUMA-562 机器人的计算机仿真实验得到了证实.     

操作器的PID自适应控制

利用操作器动力学模型的性质,提出了一种前馈补偿加PID反馈控制的自适应控制方案.由于在控制力矩中引入积分项,使操作器关节的跟踪精度和抗干扰能力得到提高.本文分析了摩擦干扰和执行机构的惯性对控制方案的影响.本文进一步的研究表明:在许多情况下,只需对操作器动力学中耦合和非线性最强的项进行补偿,然后加PID反馈控制,就能取得较好的控制效果.从而简化了控制方案,以PUMA560的前三个关节的参数作模型,对文中的方法进行了仿真.     

3D Surface-Tracking with a robot manipulator

This paper presents incremental research in Surface Tracking with a robot manipulator. Surface tracking is an important operation in self-teaching and exploratory tasks in unknown environments, and to cope with inaccurate workspace and environment modeling. The paper addresses the problem of 3D Surface-Tracking in contact, where the main concern is the angle formed between the end-effector and the surface. This study constitutes a first approach to the more general and important problem of surface following in contact. The new contribution is the 3D tracking operation based on a new alignment control algorithm using real-time contact force feedback. Simulations, and experimental results using the PUMA 560 manipulator demonstrate the feasibility of the proposed algorithm. This paper also presents a method for tool-tip contact frame calibration using forces/moments data.     

On improving the performance with bounded continuous feedback laws

We present controller design methods to smooth the discontinuity resulting from a piecewise linear control law which was proposed to improve the convergence performance for systems with input constraints. The continuous control laws designed in this paper are explicit functions of the state and are easily implementable. We also show that the convergence performance can be further improved by using a saturated high-gain feedback law. The efficiency of the proposed methods is illustrated with the PUMA 560 robot model.     

Adaptive learning with large variability of teaching signals for neural networks and its application to motion control of an industrial robot

Recently, various control methods represented by proportional-integral-derivative (PID) control are used for robotic control. To cope with the requirements for high response and precision, advanced feedforward controllers such as gravity compensator, Coriolis/centrifugal force compensator and friction compensators have been built in the controller. Generally, it causes heavy computational load when calculating the compensating value within a short sampling period. In this paper, integrated recurrent neural networks are applied as a feedforward controller for PUMA560 manipulator. The feedforward controller works instead of gravity and Coriolis/centrifugal force compensators. In the learning process of the neural network by using back propagation algorithm, the learning coefficient and gain of sigmoid function are tuned intuitively and empirically according to teaching signals. The tuning is complicated because it is being conducted by trial and error. Especially, when the scale of teaching signal is large, the problem becomes crucial. To cope with the problem which concerns the learning performance, a simple and adaptive learning technique for large scale teaching signals is proposed. The learning techniques and control effectiveness are evaluated through simulations using the dynamic model of PUMA560 manipulator.     

Experimental study on direct adaptive control of a PUMA 560 industrial robot

The article describes the implementation and experimental validation of a new direct adaptive control scheme on a PUMA 560 industrial robot. The testbed facility consists of a Unimation PUMA 560 six-jointed robot and controller, and a DEC Micro VAX II computer which hosts the RCCL (Robot Control “C” Library) software. The control algorithm is implemented on the Micro VAX which acts as a digital controller for the PUMA robot, and the Unimation controller is effectively bypassed and used merely as an I/O device to interface the Micro VAX to the joint motors. The control algorithm for each robot joint consists of an auxiliary signal generated by a constant-gain PID controller, and an adaptive position-velocity (PD) feedback controller with adjustable gains. The adaptive independent joint controllers compensate for the interjoint couplings and achieve accurate trajectory tracking without the need for the complex dynamic model and parameter values of the robot. Extensive experimental results on PUMA joint control are presented to confirm the feasibility of the proposed scheme, in spite of strong interactions between joint motions. The scheme is also implemented for control of the end-effector motion in Cartesian space. Experimental results validate the capabilities of the proposed control scheme. The control scheme is extremely simple and computationally very fast for concurrent processing with high sampling rates.     

Feedback control of robot Arm with discrete nonlinear model

This article looks at the problem of controlling a robot arm from a discrete time perspective. We develop the theories and obtain results that feedback linearizes the discrete nonlinear system representing a PUMA 560 robot arm. The effect of sampling on the performance of the arm has been studied and shown through experiments. The results presented here, where the nonlinear robot model has been linearized and controlled completely in discrete time, are new to the robotics literature. We shown certain restrictions necessary on the sampling time of the system. The results obtained have been experimentally verified at the Center for Robotics and Automation at Washington University. Because a number of researchers have addressed the problem of loss of feedback linearizability under sampling, it was important to develop theories and obtain results in discrete time that take into consideration the effects due to sampling. This problem has been addressed here. Analysis of the control law under the assumptions of bounded input is performed and a recursive sensitivity function is derived. The results we obtain in discrete time show the dependency of the performance of the arm on the sampling time. It has been seen that with a higher sampling frequency the performance of the arm substantially improves and it is expected that close to 1000 Hz sampling rate, the peak performance of the PUMA 560 robot arm, will be reached.     

A review of: “Design of Modern Control Systems” Edited by D. J. BELL,P. A. COOK and N. MUNRO. (I.E. 13. Control Engineering Series 20) Peter Peregrinus Ltd, 1982, pp. 330. Price £19·50.

A nonlinear state-space model representing the robot dynamics and containing a disturbance term due to gravitational loading is presented. An adaptive model-following control problem satisfying the matching conditions is formulated using a suitable linear time-invariant reference model. The control input is designed to have two components: a non-adaptive linear component to do the task of model-following and a nonlinear unit-vector component based on hyperstability theory to do the adaptive task. An additional integral feedback term is further added and then the overall asymptotic hyperstability is established. Simulation experiments on the first three joints of a PUMA 560 robot manipulator have indicated the potential of our design approach.     

A servo-controlled robot gripper with multiple sensors and its logical specification

The logical specification of a microprocessor-based air-servo-controlled robot hand is presented, as well as its actual implementation. This hand can accommodate a wide variety of workpieces and allows for flexible assembly through the use of an automatic quick-change fingertip. The changeable set of gripper fingers is equipped with sensors, including a tactile force sensor, a crossfire sensor, a proximity sensor, and a slip sensor. A changeable set of gripper fingers with different sensing ranges can cope with certain subranges of the workpiece spectrum. A considerable cost saving is achieved by not changing the gripper itself. This specially designed hardware and software system includes position and force feedback. A PUMA 560 is used to test the success of the entire process.     

基于加速度传感器的机器人极点配置控制

本文为工业机器人提出了一种极点配置控制法．这种控制方法的优点有：一是它的积分作用消除了机器人的微小扰动和稳态误差；二是能任意设置系统的极点，因此能保证闭环系统的稳定性和规定状态变量的暂态响应；三是加入了加速度反馈，抑制了由电枢电感所引起的机械手的振动．最后，给出了ＰＵＭＡ５６２机器人的计算机仿真和实验结果验证了此控制法的有效性．