基于迭代学习的机械手操作空间力/位置混合控制算法

基于对常规机械手操作空间力/位置混合控制算法的简单回顾,及对该算法所遇到 困难的分析,提出了一种基于迭代学习的机械手操作空间力/位置混合控制算法,来改善机械 手同高刚度环境接触时,机械手力/位置混合控制的动态控制性能．给出了学习算法的收敛条 件及其证明.实验表明该算法具有快速的收敛性,能达到很高的力/位置动态控制精度.     

双机械手对称协调力/位混合控制——模型、控制算法与实现

该文研究双手协调运动和力控制方法.基于一组面向对象的广义运动和力向量的定义, 考虑对象动力学,建立了面向对象的双手对称协调运动方程,该运动方程显式地表示了对象的 运动、内力及环境接触力与双手关节力矩间的关系.据此设计出广义工作空间一级的双手对 称协调力/位混合控制算法,并解决了算法的分解与并行实现问题.在两台PUMA562机械手 上进行的实验表明,本文研究的方法,可以在双手协调运动过程中实现对被操作对象的运动、 内力和环境接触力的混合控制.     

一种新的机械手轨迹跟踪鲁棒控制

本文考虑存在外扰及参数不确定情况下机械手轨迹跟踪问题，通过利用机械手固有的结构特性，提出一种基于李雅普诺夫方法的新控制算法．该算法的新颖之处在于无需预先知道参数的不确定范围，并且减弱了对外扰的限制．该算法的渐近跟踪性已被证明．     

冗余驱动并联机械手的混合位置/力自适应控制

针对冗余驱动并联机构研究一种自适应的混合位置/力控制算法.基于并联机构中约束 子流形的几何性质,将冗余驱动并联机构的逆动力学自然投影到位形空间和约束力空间.基于投 影方程,提出一种统一的具有渐进稳定性的自适应混合位置/力控制算法.采用最小二范数准则 求解冗余解问题,实现了实际驱动关节力矩的优化.仿真结果验证了控制方法的有效性.     

机器人的一种离散学习控制算法的研究

本文基于关节空间提出了机器人的一种离散学习控制算法．此算法仅仅利用了部分机器人动力学模型知识，基于关节空间的加速度信号构成迭代学习律，并给出了算法的收敛性证明．仿真表明了算法的有效性．     

相对位置乐观锁机制及在协同编辑中的应用

为了满足实时协同编辑对快速响应、无约束协作的要求，提出了一种基于相对位置的乐观锁机制并发控制算法．该算法引入了编辑锁和读锁，并对加锁的起始位置和编辑操作位置用相对位置表示，当锁申请成功将写进锁表或者操作发送到各协作结点时，再把它们转换成绝对位置，加锁的粒度可根据编者的需要任意选择，编者在加锁请求获得确认前，可预先进行编辑操作．应用实例表明：该算法既保证了数据维护的一致性，又具有快速响应性．     

基于遗传算法的模糊迭代学习控制算法

为了提高被控系统的控制精度及加快迭代域内的收敛速度,提出一种基于遗传算法的模糊PD型迭代学习控制算法。该算法通过模糊TSK模型设计迭代学习控制器,TSK模型中THEN部分的未知参数由遗传算法离线计算确定,进而产生合理的迭代学习律。针对被控系统,设计相应的迭代学习控制算法进行仿真分析,并与传统PD型迭代学习控制算法、模糊PID迭代学习控制算法相比较,进而将该算法用于双关节机械手进行仿真研究,仿真结果表明该算法的有效性。     

基于STM32的力觉反馈装置及用于主从遥操作实现

在遥操作系统中为了增强现实及实现本地力觉信号再现功能以提高精细化操作的目的 ,设计了用于人机交互功能的力反馈装置;该装置为单自由度结构,基于步进电机驱动;利用STM32微控制器采集触觉力信号以及关节位移信号,通过设计基于力误差的控制律调整位置变量实现输出力信号与标准力信号的匹配;为了验证该力反馈装置进行了标准力信号再现实验;并且利用该力反馈装置作为主机械手与单自由度从机械手搭建遥操作装置,进行了力、位置双边跟踪实验验证,实现了主、从机械手力、位置协同一致的目的.     

状态空间模型的双层结构预测控制算法

双层结构预测控制是指先进行设定值优化、再进行设定值跟踪的预测控制.在已有的双层结构动态矩阵控制的基础上,本文给出基于状态空间模型的双层结构预测控制算法.该算法基于干扰模型和新定义的开环预测值,给出了新的开环预测模块.该开环预测模块采用Kalman滤波方法得到操作变量、被控变量的开环动、稳态预测值.基于这些开环预测值,稳态目标计算模块的基本原理同双层结构动态矩阵控制,但是具体细节上遵循状态空间方法.动态控制模块基于稳态目标计算提供的操作变量、被控变量的稳态目标(设定值),采用二次规划算法计算控制作用.仿真算例证实了该算法的有效性.     

操作器力和位置混合控制——算法与系统行为分析

本文首先回顾了力控制技术的发展,对在操作空间一级实现力和位置混合控制的算法做了说明。着重分析了在约束空间中系统的动、静态行为。指出了环境刚度对主动力控制系统动态行为的影响,以及关节摩擦对力控制精度的影响。     

Joint-space orthogonalization and passivity for physical interpretations of dextrous robot motions under geometric constraints

A principle of ‘joint-space orthogonalization’ is proposed as an extended notion of hybrid (force and position) control for robot manipulators under geometric constraints. The principle realizes the hybrid control in a strict sense by letting position feedback signals be orthogonal in joint space to the contact force vector whose components exert at corresponding joints. This orthogonalization is executed via a projection matrix computed in real-time from a Jacobian matrix of the constraint equation in joint coordinates. To show the important role of the principle in control of robot manipulators, two basic set-point control problems are analysed. One is a hybrid PID control problem for robot manipulators under geometric endpoint constraint and another is a coordinated control problem of two arms. It is shown that passivity properties of residual dynamics of robots follow from the introduction of a quasi-natural potential and the joint-space orthogonalization. Various stability problems of PID-type feedback control schemes without compensating for the gravity force and with or without use of a force sensor are discussed from passivity properties of robot dynamics with the aid of the hyper-stability theory.     

Self-calibrated robotic manipulator force observer

In the robotic manipulation context, end-effector contact forces may be difficult to measure mainly due to the tool dynamic interferences such as the inertial forces. In this paper, a whole methodology is proposed to estimate these forces. The new approach is based on a sensor fusion technique that integrates the information of a wrist force sensor, of a 3D accelerometer placed at the robot tool and the joint position sensors measurements. The proposed methodology not only offers a suitable estimator in terms of response and filtering, but also presents a self-calibrating feature that allows an easy integration into any industrial setup. To experimentally validate the performance of the proposed methodology, two different industrial manipulators were used: an ABB robot and a Stäubli robot, both with open control system architectures. An impedance control scheme was used as force/position control law to demonstrate the need and results of the proposed calibration result.     

A force–impedance controlled industrial robot using an active robotic auxiliary device

A strategy to improve the performance of current commercial industrial robots is presented in this paper. This strategy involves cooperation of two robotic manipulators: the robotic controlled impedance device (RCID) and a commercial industrial robot. The RCID is a small six degrees-of-freedom (DOF) high bandwidth force–impedance controlled parallel manipulator, developed at the School of Engineering of the University of Porto (Portugal). The RCID works attached in series with a position controlled commercial industrial robot. Combination of the two manipulators behaves as a single manipulator having the impedance and force control dynamic performance of the RCID, as well as the workspace and trajectory tracking bandwidth of the industrial robot. Force–impedance control of the RCID, and experimental results on typical tasks that involve end-effector contact with uncertain environments of unknown stiffness are presented.     

改进幂次趋近律的机械臂滑模控制律设计

针对机械臂滑模控制中存在的抖振问题,采用趋近律的方法来进行改善,在对机械臂的控制特点和常用的滑模趋近律进行分析的基础上,针对幂次趋近律的缺点,提出了一种改进的幂次趋近律,并对其趋近性能进行了分析;根据机械臂动力学模型和改进的幂次趋近律设计了相应的滑模控制策略,对其控制策略的位置跟踪特性和抖振消除能力等进行了验证;仿真结果表明,该控制策略不仅有效地抑制了机械臂滑模控制中的抖振问题,而且保证了机械臂系统对期望轨迹的快速跟踪性,具有更好的趋近特性和收敛特性。     

On cooperative manipulation of dynamic objects

This paper proposes a decentralized position/internal force hybrid control approach for multiple robot manipulators to cooperatively manipulate an unknown dynamic object. In this approach, each autonomous robot has its own controller and uses its own sensor information in performing the fast cooperation. This approach eliminates a lot of information communications between each robot and reduces numerous computations. The influences of the position and the internal force estimation errors to the overall control system is analyzed. A cooperative identification method for each autonomous robot to identify the object's complex dynamics, cooperatively, is presented. In addition, the trade-off between the unilateral force constraint and the robots' position response is studied. Experiments show the effectiveness of this control approach.     

Adaptive force and position control of manipulators

The article presents simple methods for the design of adaptive force and position controllers for robot manipulators within the hybrid control architecture. The force controller is composed of an adaptive PID feedback controller, an auxiliary signal, and a force feedforward term, and achieves tracking of desired force setpoints in the constraint directions. The position controller consists of adaptive feedback and feedforward controllers as well as an auxiliary signal, and accomplishes tracking of desired position trajectories in the free directions. The controllers are capable of compensating for dynamic cross-couplings that exist between the position and force control loops in the hybrid control architecture. The adaptive controllers do not require knowledge of the complex dynamic model or parameter values of the manipulator or the environment. The proposed control schemes are computationally fast and suitable for implementation in online control with high sampling rates. The methods are applied to a two-link manipulator for simultaneous force and position control. Simulation results confirm that the adaptive controllers perform remarkably well under different conditions.     

A Study on Kinematics and Workspace Determination of a General 6-<Emphasis Type="Underline">P</Emphasis> US Robot

This paper proposes the novel adaptive neural network (ADNN) compliant force/position control algorithm applied to a highly nonlinear serial pneumatic artificial muscle (PAM) robot as to improve its compliant force/position output performance. Based on the new adaptive neural ADNN model which is dynamically identified to adapt well all nonlinear features of the 2-axes serial PAM robot, a new hybrid adaptive neural ADNN-PID controller was initiatively implemented for compliant force/position controlling the serial PAM robot system used as an elbow and wrist rehabilitation robot which is subjected to not only the internal coupled-effects interactions but also the external end-effecter contact force variations (from 10[N] up to critical value 30[N]). The experiment results have proved the feasibility of the new control approach compared with the optimal PID control approach. The novel proposed hybrid adaptive neural ADNN-PID compliant force/position controller successfully guides the upper limb of subject to follow the linear and circular trajectories under different variable end-effecter contact force levels.     

一种受限机械手的自适应力/位置控制方法*

对于受限机械臂,本文提出了一种自适应的力/位置控制方法。其实现是基于给出的新的降阶动力学模型,在反馈信号中引入力的累积误差信号,利用降阶模型的本身特性从而达到自适应力/位置控制的目的。给出的自适应律是通过跟踪误差信号来调节的。仿真结果证实了本方法的正确性。