自适应模糊与CMAC并行的机器人力/位置控制

为提高机器人系统对机器人末端操纵器与外界工作环境接触时，其接触刚度不确定性的自适应能力，在机器人力／位置混合控制的基础上，设计出了一种基于自适应模糊与CMAC并行控制的机器人力控制器，采用小脑模型神经控制器实现前馈控制，实现被控对象的逆动态模型，自适应模糊控制器实现反馈控制，保证系统的稳定性，且抑制扰动。以平面两关节机器人进行仿真，仿真结果表明，系统的自适应能力和力跟踪能力有显著的提高，机械手在其末端操纵器与刚性变化范围较大的外界工作环境接触时，具有较强的适应能力，较好地完成了机器人的力／位置控制。     

基于螺旋理论的机器人自适应运动／力混合控制

本文研究当机器人动力学模型中含有未知惯性参数时机器人适从运动的混合控制，利用旋转理论将与环境接触时的机器人动力学方程分成两个螺旋方程后，提出了一种运动和力的自适应混合控制规律，该规律直接利用的自然约束和人工约束，达到了运动／力的全局稳定跟踪。     

机器人力／位置自适应模糊控制

在机器人力／位置混合控制的基础上，本文提出了一种对力控制回路采用自适应模糊控制的方法，有利于提高系统对机器人末端操纵器五外界工作环境接触时，其接触刚度不确定性的自适应能力，仿真结果表明，该控制方法与常规ＰＩＤ控制相比，系统的自适应能力和鲁棒性有显著的改善。     

附加力外环的机器人力／位置自适应模糊控制

提出一种由神经网络训练模糊控制规则的自适应模糊控制器,并应用附加力外环的机器人力／位置控制。在不改变一般工业机器人原有位置控制的前提下,实现力／位置自适应模糊控制。实验结果表明,该方法可使机器人控制系统对工作环境接触刚度的自适应能力得到显著改善。     

直接在位置控制机器人上实现力/位置自适应模糊控制

本文提出一种直接在位置控制机器人上实现力／位置自适应模糊控制的方案，使得对于一般工业机器人，在不改变其原有位置控制器的前提下，实现力／位置控制．仿真结果表明，该控制方法使机器人控制系统对工作环境接触刚度的自适应能力得到显著的改善．     

受限机器人运动的自适应控制

本文基于[4]中对受限机器人运动方程的变换,给出了一种自适应控制方案,使得在系统某些参数未知的情况下,机器人末端操纵器的位置运动及其与刚性、无摩擦的工作环境之间的接触力渐近趋于期望值,本文中的仿真例子验证了给出的自适应控制方案的有效性。     

面向未知环境基于智能预测的模糊控制器研究

提出了一种新的面向未知环境的智能预测算法，并将此算法应用于机器人力跟踪控制中．该方法利用机械手末端与未知受限环境产生的接触轨迹，通过模糊推理智能地预测阻抗控制模型中的参考轨迹，并根据力误差变化用参考比例因子对其进行调节，以适应未知环境刚度的变化．通过对阻抗模型参数进行模糊调节减少受限运动中的力误差，提高了全局的力控制效果．仿真结果证明了此算法的有效性．     

机器人力控制器的鲁棒性设计及其在装配作业中的应用

当机器人与外界环境发生接触时,接触刚度等不确定性因素对机器人力控制系统的稳定性有很大影响。为了解决这个问题,本文根据变结构控制理论设计了一个机器人力控制器,并提出了一种旋转滑动平面的自适应算法,以加快系统状态的收敛速度和保证收敛过程的鲁棒性。装配实验结果表明,利用本文的方法所设计的机器人力控制器对接触刚度的变化具有很好的鲁棒性。     

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。     

基于变刚度自适应导纳机制的机械臂恒力控制

工业机械臂在诸如打磨抛光等接触式作业任务中对环境刚度信息存在一定的依赖性, 未知环境刚度信息将严重影响机器人的力位控制精度, 使得作业效果难以得到保证. 为解决环境信息不足或未知情况下的力/位置精确控制问题, 本文首先提出了一种新的自适应环境刚度在线估计方法, 针对时变的环境刚度进行实时估计, 由此预测生成后继的机械臂参考轨迹点, 随后提出了一种根据力跟踪误差实时调整末端工具手刚度系数的变刚度导纳恒力控制方法, 并结合李雅普诺夫稳定性理论给出了整体控制律的收敛性证明. 针对刚柔两种末端工具手和多种不同的曲面工件开展了实验研究, 并与传统PID控制方法和传统导纳控制方法进行了对比, 其结果表明本文所提出的复合控制方法可在不同工况条件下实现机器人运动过程中接触力的快速柔顺调节, 并获得4.55%以内的最优力控误差效果, 证明了本文所提出方法的有效性与可行性.     

Distributed Force/Position Consensus Tracking of Networked Robotic Manipulators

In this paper, we address the tracking problem of distributed force/position for networked robotic manipulators in the presence of dynamic uncertainties. The end-effectors of the manipulators are in contact with flat compliant environment with uncertain stiffness and distance. The control objective is that the robotic followers track the convex hull spanned by the leaders under directed graphs. We propose a distributed adaptive force control scheme with an adaptive force observer to achieve the asymptotic force synchronization in constrained space, which also maintains a cascaded closed-loop structure separating the system into kinematic module and dynamic module. A decentralized stiffness updating law is also proposed to deal with the environment uncertainties. The convergence of tracking errors of force and position is proved using Lyapunov stability theory and input-output stability analysis tool. Finally, simulations are performed to show effectiveness of the theoretical approach.      

An exponentially stable adaptive control for force and positiontracking of robot manipulators

The problem of controlling a robot manipulator while the end effector is in contact with an environment of finite but unknown stiffness is considered. An exponentially stable control law is derived starting from a passivity-based position control algorithm. The original position trajectory is scaled along the interaction direction so as to achieve force tracking as well as position tracking along the unconstrained directions. A passivity-based adaptive algorithm is designed to avoid the explicit computation of the scaling factor, which depends on the unknown stiffness of the environment, leading to time-varying PID control actions on the force error     

Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator in the presence of parametric uncertainty

This study is devoted to sensorless adaptive force/position control of robot manipulators using a position-based adaptive force estimator (AFE) and a force-based adaptive environment compliance estimator. Unlike the other sensorless method in force control that uses disturbance observer and needs an accurate model of the manipulator, in this method, the unknown parameters of the robot can be estimated along with the force control. Even more, the environment compliance can be estimated simultaneously to achieve tracking force control. In fact, this study deals with three challenging problems: No force sensor is used, environment stiffness is unknown, and some parametric uncertainties exist in the robot model. A theorem offers control laws and updating laws for two control loops. In the inner loop, AFE estimates the exerted force, and then, the force control law in the outer loop modifies the desired trajectory of the manipulator for the adaptive tracking loop. Besides, an updating law updates the estimated compliance to provide an accurate tracking force control. Some experimental results of a PHANToM Premium robot are provided to validate the proposed scheme. In addition, some simulations are presented that verify the performance of the controller for different situations in interaction.     

基于柔性人机接口的人机协调运动控制方法

为改善基于力信息的人机协调运动中人机交互力,采用了在人机接口中设置弹性元件的方法,建立了具有柔性人机接口的人机交互力学模型。在已有鲁棒自适应阻抗控制方法的基础上进行改进,提出了一种基于柔性人机接口的自适应阻抗控制方法。此控制方法是对阻抗外环位置速度进行比例补偿,对力控制内环采用模糊PID (proportion integral differential)控制,实现改进自适应阻抗算法,从而提高了位置跟随精度,并有效减小了人机交互力。分析了人机接口中弹性元件对控制效果的影响,获得了不同刚度系数时,交互力控制效果和位置跟随精度。在此基础上,建立了试验系统,完成了试验。人机协调运动试验结果显示：应用柔性人机接口和改进后的控制方法具有更好的人机交互力控制效果。标准运动输入试验结果显示：改进后的控制方法具有更好的人机交互力控制效果和更高的位置跟随精度;人机交互力大小、位置跟踪准确性与人机接口刚度系数大小均成正比。     

非结构环境下的机器人自适应变阻抗力跟踪控制方法

针对力跟踪时环境刚度不确定及环境位置动态变化的未知性,提出一种非结构环境下基于自适应变阻抗的力跟踪控制策略.首先,通过建立机器人与环境的接触力模型,分析理想情况下对环境刚度和环境位置的要求;然后,建立新的阻抗模型来适应环境刚度不确定的情况,并根据接触力的变化对阻抗模型参数进行在线自适应调节,用于实时地补偿对环境动态变化的未知性,并对自适应变阻抗的稳定性进行证明;最后,对经典的定阻抗与所提出的自适应变阻抗进行非结构环境下的仿真和物理实验的对比,实验结果表明该策略相比于定阻抗控制能够达到更好的力跟踪效果.     

Position-based force tracking adaptive impedance control strategy for robot grinding complex surfaces system

As a key technology of robot grinding, force control has great influence on grinding effects. Based on the traditional impedance control, a position-based force tracking adaptive impedance control strategy is proposed to improve the grinding quality of aeroengine complex curved parts, which considers the stiffness damping environmental interaction model, modifies the reference trajectory by a Lyapunov-based approach to realize the adaptive grinding process. In addition, forgotten Kalman filter based on six-dimensional force sensor is used to denoise the force information and a three-step gravity compensation process including static base value calculation, dynamic zero update and contact force real-time calculation is proposed to obtain the accurate contact force between tool and workpiece in this method. Then, to verify the effectiveness of the proposed method, a simulation experiment which including five different working conditions is conducted in MATLAB, and the experiment studying the deviation between the reference trajectory and the actual position is carried out on the robot grinding system. The results indicate that the position-based force tracking adaptive impedance control strategy can quickly respond to the changes of environmental position, reduce the fluctuation range of contact force in time by modifying the reference trajectory, compensate for the defect of the steady-state error of the traditional impedance control strategy and improve the surface consistency of machined parts.     

CAD/CAM-based force controller using a neural network-based effective stiffness estimator

In industries manufacturing metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has the ability to control the polishing force acting between an abrasive tool and a work piece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition after considering the effective stiffness in the force control system. However, there is a problem in that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity has a bad influence on the force control stability. In this article, a fine tuning method of the desired damping is considered using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.     

气动人工肌肉拮抗关节的力与刚度独立控制

针对柔性仿生关节难以实现力与刚度独立控制的问题,建立了一种新的气动人工肌肉等效弹簧模型及关节力和刚度模型,设计了一种双输入双输出滑模控制器,来实现对气动人工肌肉拮抗关节力与刚度的独立控制．最后,搭建了气动人工肌肉驱动的拮抗关节实验平台,在关节位置固定和开放两种状况下进行了实验研究,验证所提方法的有效性;在不同负载情况下进行了对比实验,验证所提方法的通用性．所提出的建模和控制方法综合考虑了仿生关节位置、力和刚度相对独立控制,在机器人与人或环境互相作用的场合有很好的应用前景．