Robust Sliding Control of Robotic Manipulators Based on a Heuristic Modification of the Sliding Gain

A task space robust trajectory tracking control is developed for robotic manipulators. A second order linear model, which defines the desired impedance for the robot, is used to generate the reference position, velocity and acceleration trajectories under the influence of an external force. The control objective is to make the robotic manipulator’s end effector track the reference trajectories in the task space. A sliding mode based robust control is used to deal with system uncertainties and external perturbations. Thus, a sliding manifold is defined by a linear combination of the tracking errors of the system in the task space built from the difference between the real and the desired position, velocity and acceleration trajectories in comparison with previous works where the sliding manifold was defined by the desired impedance and the external force. Moreover, the ideal relay has been substituted by a relay with a dead-zone in order to fit in with the actual way in which a real computational device implements the typical sign function in sliding mode control. Furthermore, a higher level supervision algorithm is proposed in order to reduce the amplitude of the high frequency components of the output associated to an overestimation of the system uncertainty bounds. Then, the robust control law is applied to the case of a robot with parametric uncertainty and unmodeled dynamics. The closed-loop system is proved to be robustly stable with all signals bounded for all time while the control objective is fulfilled in practice. Finally, a simulation example which shows the usefulness of the proposed scheme is presented.     

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.     

康复机器人的同步主动交互控制与实现

提出了一种适用于康复机器人的人机交互控制方法. 结合一款具有平面并联结构的上肢康复机器人, 实现了与用户(患者)运动意图同步的、柔顺的主动康复训练. 在训练中, 利用自适应频率振荡器, 从表面肌电信号(Surface electromyography, sEMG)中获取运动模式信息, 然后结合运动模式和期望的正常运动轨迹, 生成与主动运动意图同步的参考训练轨迹. 本文通过仿真和实际实验对所提出的方法进行了验证, 振荡器可以在2~5s内快速实现与用户主动运动意图的同步, 然后利用阻抗控制器给予柔顺的辅助. 通过调节阻抗参数, 可以为患者的运动训练提供不同程度的辅助.     

Model reference adaptive impedance control for physical human-robot interaction

This paper presents a novel enhanced human-robot interaction system based on model reference adaptive control. The presented method delivers guaranteed stability and task performance and has two control loops. A robot-specific inner loop, which is a neuroadaptive controller, learns the robot dynamics online and makes the robot respond like a prescribed impedance model. This loop uses no task information, including no prescribed trajectory. A task-specific outer loop takes into account the human operator dynamics and adapts the prescribed robot impedance model so that the combined human-robot system has desirable characteristics for task performance. This design is based on model reference adaptive control, but of a nonstandard form. The net result is a controller with both adaptive impedance characteristics and assistive inputs that augment the human operator to provide improved task performance of the human-robot team. Simulations verify the performance of the proposed controller in a repetitive point-to-point motion task. Actual experimental implementations on a PR2 robot further corroborate the effectiveness of the approach.     

空间绳系机器人目标抓捕鲁棒自适应控制器设计

针对空间绳系机器人（Tethered space robot,TSR）目标抓捕过程中的稳定控制问题,建立空间绳系机器人系统模型,根据阻抗控制原理,设计基于位置的阻抗控制方法;针对空间绳系机器人系统的模型不确定性问题,利用神经网络对不确定性进行估计补偿,设计鲁棒项对空间系绳干扰和神经网络估计误差的影响进行抑制,在此基础上设计空间绳系机器人目标抓捕鲁棒自适应稳定控制器,并进行稳定性证明.最后对设计的控制器进行仿真验证.作为对比,对无鲁棒项自适应的稳定控制器进行仿真.仿真结果表明,设计的基于阻抗控制的鲁棒自适应控制可以实现对空间绳系机器人目标抓捕过程中的稳定控制,与无鲁棒项自适应的稳定控制器仿真结果相比,本文采用的鲁棒自适应控制方法可以有效地对不确定性进行补偿,控制过程中超调量更小,收敛时间更短,并且控制精度更高.     

多关节机器人的自适应模糊滑模控制

针对多关节机械臂轨迹跟踪控制,提出了一种基于全局快速终端滑模面的自适应模糊滑模控制方法。该方法通过设计合适的自适应律,采用模糊自适应控制调节滑模控制的切换控制增益,实现了对建模误差和不确定干扰的自动跟踪,削弱了抖振。系统不需要对建模误差和干扰进行预估计,并且通过对控制器结构的简化,降低了模糊控制器的维数,减少了计算量。利用李亚普诺夫定理证明了控制系统的稳定性,仿真结果表明了其有效性。     

浸入与不变方法原理及其在非线性自适应控制中的应用

非线性系统理论的实际工程需求和复杂性使其成为控制学科中最具吸引力和挑战性的研究领域,为此介绍了一种新的非线性控制律设计方法——浸入与不变（I&I）理论.该方法首先选择一个比被控系统维数低的（局部）渐近稳定的目标系统,然后设计浸入映射和控制律,使得原系统在控制律作用下的动态轨迹都是目标系统在浸入映射下的像,并且该控制律能够保持目标系统的像为不变吸引流形,且使闭环轨迹有界.针对未知点质量模型,设计了一种新的非线性浸入与不变自适应控制律,实现了对参考指令的精确跟踪.将其与基于确定等价原则的自适应控制律相比较,仿真结果表明,所设计的浸入与不变控制律能够更好地处理带有未知参数的系统.     

Terminal sliding mode fault-tolerant control for aviation redundant hydraulic actuation system based on robust fault reconstruction

This paper investigates the fault-tolerant control problem for a dual-redundant hydraulic actuation system on active/active (A/A) mode subject to servovalve leakage and disturbances. The change in system dynamics caused by servovalve leakage is modeled as an additive time-varying fault. Then, an enhanced iterative learning observer with improved robustness against abrupt unknown input is designed for fault reconstruction. In view of the high relative degree of the plant, an auxiliary variable is adopted to facilitate the controller design. Combined with the fault reconstruction results, an adaptive continuous nonsingular fast terminal sliding mode (NFTSM) fault-tolerant controller is developed, in which an NFTSM manifold is constructed based on the auxiliary variable to achieve fast convergence of trajectory tracking errors. An adaptive continuous reaching law with less chattering is designed to compensate for the influence of the lumped disturbance. Lyapunov stability analysis demonstrates that this method can ensure finite-time convergence of sliding variable and can guarantee the trajectory tracking errors converge to the neighborhood of the origin exponentially. Finally, the effectiveness of the proposed method is verified through a comparative simulation study.     

基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法

设计了一种基于无模型自适应的外骨骼式上肢康复机器人主动交互训练控制方法.在机器人与人体上肢接触面安装力传感器采集人机交互力矩信息作为量化的主动运动意图,设计了一种无模型自适应滤波算法使交互力矩变得平滑而连贯;以人机交互力矩为输入,综合考虑机器人末端点与参考轨迹的相对位置和补偿力的信息,设计了人机交互阻抗控制器,用于调节各关节的给定目标速度;设计了将无模型自适应与离散滑模趋近律相结合的速度控制器完成机器人各关节对目标速度的跟踪.仿真结果表明,该控制方法可以实现外骨骼式上肢康复机器人辅助患者完成主动交互训练的功能.通过调节人机交互阻抗控制器的相应参数,机器人可以按照患者的运动意图完成不同的主动交互训练任务,并在运动出现偏差时予以矫正.控制器在设计实现过程中不要求复杂准确的动力学建模和参数识别,并有一定的抗干扰性和通用性.     

基于指尖力传感器的HIT 机器人灵巧手笛卡尔阻抗控制

介绍了基于5维指尖力／力矩传感器的HIT机器人灵巧手的笛卡尔阻抗控制．当手指与障碍物接触时，呈现出2阶机械阻抗特性．在阻抗控制算法中，不需直接计算加速度，从而避免了因大加速度误差给控制带来的不利因素．结合期望轨迹和实际位置及速度，产生一个参考轨迹，手指跟踪此参考轨迹即可自动获得期望阻抗特性．     

Voltage-based adaptive impedance force control for a lower-limb rehabilitation robot

This paper presents a novel voltage-based adaptive impedance force control for a lower limb rehabilitation robot. The impedance parameters are adaptively regulated by a gradient descent algorithm for adjusting the human force in performing therapeutic exercises. Although the proposed control is based on voltage control strategy, it differs from the common torque control strategy. One of the advantages is that it is free from the dynamical models of the robot and patient. Compared with a torque control scheme, it is simpler, less computational, and more efficient while it considers the actuators. The control approach is verified by stability analysis. Simulation results show the efficiency of the control approach applied on a lower limb rehabilitation robot driven by an electric motor. A comparison on performing isometric exercise shows that the voltage-based adaptive impedance force control is superior to both voltage-based impedance control and torque-based impedance control.     

Adaptive control of redundant multiple robots in cooperative motion

A redundant robot has more degrees of freedom than what is needed to uniquely position the robot end-effector. In practical applications the extra degrees of freedom increase the orientation and reach of the robot. Also the load carrying capacity of a single robot can be increased by cooperative manipulation of the load by two or more robots. In this paper, we develop an adaptive control scheme for kinematically redundant multiple robots in cooperative motion.In a usual robotic task, only the end-effector position trajectory is specified. The joint position trajectory will therefore be unknown for a redundant multi-robot system and it must be selected from a self-motion manifold for a specified end-effector or load motion. In this paper, it is shown that the adaptive control of cooperative multiple redundant robots can be addressed as a reference velocity tracking problem in the joint space. A stable adaptive velocity control law is derived. This controller ensures the bounded estimation of the unknown dynamic parameters of the robots and the load, the exponential convergence to zero of the velocity tracking errors, and the boundedness of the internal forces. The individual robot joint motions are shown to be stable by decomposing the joint coordinates into two variables, one which is homeomorphic to the load coordinates, the other to the coordinates of the self-motion manifold. The dynamics on the self-motion manifold are directly shown to be related to the concept of zero-dynamics. It is shown that if the reference joint trajectory is selected to optimize a certain type of objective functions, then stable dynamics on the self-motion manifold result. The overall stability of the joint positions is established from the stability of two cascaded dynamic systems involving the two decomposed coordinates.     

Data-Driven Human-Robot Interaction Without Velocity Measurement Using Off-Policy Reinforcement Learning

In this paper, we present a novel data-driven design method for the human-robot interaction (HRI) system, where a given task is achieved by cooperation between the human and the robot. The presented HRI controller design is a two-level control design approach consisting of a task-oriented performance optimization design and a plant-oriented impedance controller design. The task-oriented design minimizes the human effort and guarantees the perfect task tracking in the outer-loop, while the plant-oriented achieves the desired impedance from the human to the robot manipulator end-effector in the inner-loop. Data-driven reinforcement learning techniques are used for performance optimization in the outer-loop to assign the optimal impedance parameters. In the inner-loop, a velocity-free filter is designed to avoid the requirement of end-effector velocity measurement. On this basis, an adaptive controller is designed to achieve the desired impedance of the robot manipulator in the task space. The simulation and experiment of a robot manipulator are conducted to verify the efficacy of the presented HRI design framework.      

Direct adaptive impedance control of robot manipulators

This article presents an adaptive scheme for controlling the end-effector impedance of robot manipulators. The proposed control system consists of three subsystems: a simple “filter” that characterizes the desired dynamic relationship between the end-effector position error and the end-effector/environment contact force, an adaptive controller that produces the Cartesian-space control input required to provide this desired dynamic relationship, and an algorithm for mapping the Cartesian-space control input to a physically realizable joint-space control torque. The controller does not require knowledge of either the structure or the parameter values of the robot dynamics and is implemented without calculation of the robot inverse kinematic transformation. As a result, the scheme represents a general and computationally efficient approach to controlling the impedance of both nonredundant and redundant manipulators. Furthermore, the method can be applied directly to trajectory tracking in free-space motion by removing the impedance filter. Computer simulation results are given for a planar four degree-of-freedom redundant robot under adaptive impedance control. These results demonstrate that accurate end-effector impedance control and effective redundancy utilization can be achieved simultaneously by using the proposed controller.     

漂浮基空间机器人自适应RBF 网络终端滑模控制

主要研究漂浮基空间机器人对工作空间连续轨迹跟踪控制问题．针对系统动力学模型中非线性项未知，以及参数不确定性和外界扰动无法估计的情况，提出了基于自适应RBF网络终端滑模控制方法．该方法结合了非线性滑动流形与径向基函数特性，利用自适应RBF网络在线学习系统中的不确定性，使得无需精确的动力学模型亦能保证系统在有限时间内快速稳定．根据Lyapunov方法设计的自适应增益保证闭环控制系统具有全局稳定性，并且有效抑制抖振现象．针对6关节空间机器人的轨迹跟踪控制仿真表明，提出的自适应RBF网络终端滑模控制方法能够基于不完整动力学模型实现高精度轨迹跟踪，且误差在有限时间内快速收敛，系统抖振也得到了有效抑制．     

基于系统浸入和流形不变自适应方法的静止无功补偿器非线性鲁棒自适应控制方法

本文提出一种将系统浸入和流形不变(I&I)自适应控制方法与L2-增益抑制鲁棒控制方法相结合的静止无功补偿器(SVC)的非线性鲁棒自适应控制方法.所提方法首先通过参数估计误差和鲁棒控制律的设计,使得所构造的表示参数估计误差函数的流形不变且吸引,从而使参数估计误差在这一流形上收敛于零.然后,通过所设计的可调参数对参数估计误差的收敛性能进行控制,以此来保证参数估计器对不确定参数的自适应估计能力.最后,采用自适应逆推算法推导鲁棒控制律,并通过使不确定外部扰动满足从输入到输出的耗散性来保证系统对不确定扰动的鲁棒性.仿真结果表明,利用所提方法设计的SVC控制器和参数替换律在参数估计、发电机功角动态响应方面优于传统自适应逆推算法,从而提高了输电系统的稳定水平.     

基于时间延时估计和自适应模糊滑模控制器的双机械臂协同阻抗控制

多机械臂的精准协同控制已成为当前机器人领域的研究难点,为实现双机械臂精准控制,通过建立双机械臂动力学模型,采用时间延时估计简化机械臂动力学模型,在保证控制系统稳定性的前提下,引入自适应模糊滑模控制器实现对估计误差的修正和补偿,设计基于时间延时估计和自适应模糊滑模控制的双机械臂协同阻抗控制器,实现双机械臂协同操作的末端轨迹控制以及接触力精准控制.最后,将该控制器应用于两台六自由度机械臂仿真平台,实现双臂夹取和搬运同一目标物体的操作,通过与其他控制器进行对比实验,表明所设计的控制器具有响应快、无抖震、精度高的特点.     

窄带阻抗匹配下信号传输的偏差控制仿真

窄带信号传输偏差控制是提高信号传输速率和满足大众需求的关键技术,针对当前相关研究成果存在实现后系统运行稳定性控制效果有待优化的问题,提出基于判决反馈的窄带阻抗匹配下信号传输的偏差控制方法。通过窄带信号抽样定理,利用降低采样频率实现窄带阻抗匹配下信号采集,根据采集到的信号,考虑到偏差通常在各种干扰下产生,设计并构建双环判决反馈下自适应滤波器,在偏差控制中,设定系统含有直扩部分,抑制信号传输中的干扰,并以减少信号失真为目的,引入判决反馈电路,判决反馈系统根据传输信号码片获取干扰信号估算值,并将所得估算值当作参照输入至自适应滤波器中,利用参照信号和原始信号的直扩部分相关性减小,使判决反馈可以更好地对窄带阻抗匹配下信号传输干扰进行抑制,并减少信号失真情况,从而实现信号传输偏差控制。实验结果表明,所提方法可以很好地抑制各种干扰,实现了信号传输偏差的高效控制。     

基于阻抗控制的步行康复训练机器人的轨迹自适应

把阻抗控制理论运用到步行康复训练机器人系统中,使用基于位置的阻抗控制模型设计了系统的控制器.提出基于阻抗模型的步态轨迹白适应算法,并分析了系统的误差.在建立的Solid Works、Matlab、Adams虚拟样机联合仿真平台上进行了主动康复训练的仿真实验,并在实物样机上进行了健康训练者的实验.结果表明,该控制器能够实...     

机器人捕捉运动目标的动力学视觉伺服方法

提出一种机器人捕捉运动目标的动力学视觉伺服方法。基于位置阻抗控制器,通过双目立体视觉检测并跟踪运动目标的位置,结合CMAC网络,采用以视觉阻抗的二次型为训练目标的学习型视觉阻抗控制器,用于克服控制器对系统结构参数变化适应性差的缺点,并对阻抗参数进行优化。实验结果表明,该视觉伺服方法在机器人捕捉运动目标时具有良好的动力学特性和轨迹控制效果。