任务空间内空间机械臂的自适应控制

对于本体姿态受控而位置不受控的空间机械臂系统,本文在任务空间内给出了一种自适应控制算。证明了当系统存在参数不确定性时,该算汉不但可以保证末端招待器在任务空间内的位置轨迹跟踪误差渐近收敛,而且还可保证在关节空间的角偏差及角偏差速率渐近收敛。仿真结果验证了算法的有效性。     

机械臂视觉伺服控制系统的设计

针对传统机械臂无法更好的适应外部坏境的变化,提出了基于双目视觉的机械臂伺服控制系统的设计。首先用D-H建模法对机械臂进行运动学分析以及运动学求解,然后利用数字图像处理技术,基于SURF特征完成目标物体的识别以及双目立体视觉的测量,从而为机械臂的动作提供目标物体在空间中的坐标。最后,对目标测距的精度进行了结果验证,并完成抓取动作。     

空间机械臂的鲁棒复合自适应控制

对于本体姿态受控而位置不受控的空间机械臂系统,考虑存在参数不确定性及非参数不确定性,首先建立了系统的估计模型,并且提出了一种鲁棒复合自适应控制方法,其参数适应律由估计误差和跟踪误差共同决定.证明了这种方法不仅可维持系统的全局渐近稳定,而且还可快速收敛和减小跟踪误差.     

基于分层运动分解的飞行机械臂视觉伺服控制

飞行机械臂系统具有主动作业能力, 通过搭载视觉传感器感知周围环境, 系统的自主能力将进一步提高.然而, 考虑到无人机的欠驱动和整个系统的非线性特性, 飞行机械臂系统的视觉伺服控制仍然是一项具有挑战性的工作. 本文在充分考虑机械臂对无人机的力/力矩作用后, 提出了一种基于分层运动分解的飞行机械臂视觉伺服控制方案. 首先, 对飞行机械臂系统的运动学和动力学模型进行分析. 然后, 根据所得的相机运动学模型, 通过基于图像的视觉伺服控制获得相机的期望速度, 进而制定无人机和机械臂的速度分配策略. 在考虑机械臂运动时对无人机产生的力/力矩影响, 设计了底层的飞行控制器. 最后, 在与现有方法的仿真对比中可以看出, 所提方法具有良好的控制性能, 对图像特征点位置的不确定性及图像噪声也表现了较好的鲁棒性     

基于无模型自适应控制的视觉伺服

传统的机器人视觉伺服控制技术需要已知机器人精确的动力学和运动学模型以及机器人的手-眼参数。然而,由于机器人建模、手-眼标定等过程存在一定误差,因此很难精确获得视觉伺服控制模型,从而影响机器人视觉伺服系统的精度和收敛速度。针对这一难题,本文提出一种基于无模型自适应控制方法（MFAC）的机器人视觉伺服技术。利用视觉伺服系统的输入与输出数据,实现自适应视觉伺服控制,即通过MFAC在线估计机器人伺服控制器中的雅各比矩阵,并结合滑模控制器,实现机器人对目标的快速精确跟踪。实验结果表明,本文提出的方法在系统参数变化引起的未知扰动情况下仍能保证伺服控制器平稳收敛,并且能够减小视觉跟踪误差。     

一类空间机械壁的复合自适应控制

对于本体资态受控而位置不受控的空间机械臂系统,其自适应控制通常是从跟踪误差中获得有关参数信息,但除了跟踪误差外,估计中也含有参数信息。本文首先分析了一类空间机械臂系统的动力学特性,建立了系统的估计模型,并且提出了一种复合自适应控制方法,其参数适应由估计误差和跟踪误差共同;证明了这种自适应方法不仅可维持自适应控制系统的全局稳定,而且还可快速收敛和减小跟踪误差,仿真结果也验证了这一特点。     

基于参数不确定机械臂系统的自适应轨迹跟踪控制

针对机械臂系统惯性参数及运动学参数不能准确测量进而影响轨迹跟踪性能的问题,提出一种任务空间自适应轨迹跟踪控制方法,通过定义关节角速度参考误差,并将任务空间的轨迹跟踪误差及运动学参数误差反馈给控制器,以改善系统稳定性,设计电机参数传输矩阵及电机参数自适应率,以抵消电机发热引起参数漂移对跟踪性能影响,并给出了稳定性证明.实验结果表明,该方法能够较好地克服电机参数漂移对跟踪控制性能的影响.     

空间机械臂在轨维修的视觉伺服操控策略

首先分析了空间机械臂/机械手系统在轨旋拧螺钉任务的尺寸链误差．为修正微重力环境和机械臂、模拟维修单机在轨安装导致的位姿偏差,特别是消除机械手抓取电动工具导致的随机误差影响,提出了航天员在轨标定电动工具位姿/全局相机测量电动工具位姿并引入机械臂进行视觉伺服的控制策略．设计了视觉伺服控制器,给出了该控制算法的收敛证明和稳定性分析．通过在轨实施,该策略实现了机械臂/机械手系统拧松螺钉时位姿误差不超过3 mm/2°的任务要求．     

一类空间机械臂系统的自适应控制与鲁棒控制

讨论载体位置与姿态均不受控制的自由浮动空间机械臂系统的控制问题。基于增广列反馈控制模型,提出当载荷参参数不确定时空间机械臂追踪惯性空间期望轨迹的自适应和鲁棒控制方法,通过仿真运算,证实了方法物有效性。     

空间机械臂本体与末端抓手协调运动的自适应控制方法

讨论了载体位置不受控制的漂浮基空间机械臂本体与末端抓手协调运动的自适应控制问题. 对系统的运动学、动力学分析表明, 结合系统动量守恒关系得到的系统动力学方程及协调运动的增广广义Jacobi矩阵可以表示为适当选择的组合惯性参数的线性函数. 以此为基础, 对于系统存在未知参数的情况, 设计了本体姿态与机械臂末端抓手惯性空间轨迹协调运动的自适应控制方案. 上述控制方案的显著优点在于: 不需要测量、反馈飞行器本体的位置、移动速度及移动加速度. 仿真运算, 证实了上述控制方案的有效性.     

Adaptive output feedback tracking control of stochastic nonlinear systems with dynamic uncertainties

In this paper, adaptive output feedback tracking control is developed for a class of stochastic nonlinear systems with dynamic uncertainties and unmeasured states. Neural networks are used to approximate the unknown nonlinear functions. K‐filters are designed to estimate the unmeasured states. An available dynamic signal is introduced to dominate the unmodeled dynamics. By combining dynamic surface control technique with backstepping, the condition in which the approximation error is assumed to be bounded is avoided. Using It ô formula and Chebyshev's inequality, it is shown that all signals in the closed‐loop system are bounded in probability, and the error signals are semi‐globally uniformly ultimately bounded in mean square or the sense of four‐moment. Simulation results are provided to illustrate the effectiveness of the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.     

Adaptive synchronised tracking control for multiple robotic manipulators with uncertain kinematics and dynamics

In this study, a new adaptive synchronised tracking control approach is developed for the operation of multiple robotic manipulators in the presence of uncertain kinematics and dynamics. In terms of the system synchronisation and adaptive control, the proposed approach can stabilise position tracking of each robotic manipulator while coordinating its motion with the other robotic manipulators. On the other hand, the developed approach can cope with kinematic and dynamic uncertainties. The corresponding stability analysis is presented to lay a foundation for theoretical understanding of the underlying issues as well as an assurance for safely operating real systems. Illustrative examples are bench tested to validate the effectiveness of the proposed approach. In addition, to face the challenging issues, this study provides an exemplary showcase with effectively to integrate several cross boundary theoretical results to formulate an interdisciplinary solution.     

含未校准摄像机参数的非完整移动机器人自适应动态反馈跟踪控制

研究基于视觉伺服的不确定非完整移动机器人的跟踪控制问题.基于视觉反馈和状态输入变换,提出一类非完整运动学系统的不确定模型,并运用两个新的变换,对3种不同情况分别设计自适应动态反馈控制器来跟踪不确定系统的期望轨迹.利用李雅普诺夫方法和推广的Barbalat引理,严格证明了误差系统的收敛性.仿真结果验证了所提方法的有效性.     

Visual servoing tracking control of uncalibrated manipulators with a moving feature point

The paper is concerned with the problem of uncalibrated visual servoing robots tracking a dynamic feature point along with the desired trajectory. A nonlinear observer and a nonlinear controller are proposed, which allow the considered uncalibrated visual servoing robotic system to fulfil the desired tracking task. Based on this novel control method, a dynamic feature point with unknown motion parameters can be tracked effectively along with the desired trajectory, even with multiple uncertainties existing in the camera, the kinematics and the manipulator dynamics. By the Lyapunov theory, asymptotic convergence of the image errors to zero with the proposed control scheme is rigorously proven. Simulations have been conducted to verify the performance of the proposed control scheme. The results demonstrated good convergence of the image errors.     

Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.     

Robust adaptive multi-task tracking control of redundant manipulators with dynamic and kinematic uncertainties and unknown disturbances

In this paper, a novel adaptive multi-priority controller for redundant manipulators is proposed to accomplish the multi-task tracking when kinematic/dynamic uncertainties and unknown disturbances exist. Prioritized redundancy resolution in kinematic level is incorporated into this passivity-based control framework. The kinematic and dynamic parameter adaptations are driven by both tracking error and prediction error. Moreover, the tracking information from both primary and subtasks are all utilized to accelerate the parameter estimation when the tasks are independent, whereas the inevitable tracking error of the subtasks due to algorithmic singularities is properly eliminated in the adaptation laws when the tasks are dependent. Potential ill-conditioned solution of the pseudoinverse is avoided using an improved singularity-robust inverse of the projected Jacobian. Along with the improvement of the multi-task tracking performance, smoothness of the commanded torques is still guaranteed for easy application. Measurements of the noisy joint acceleration and task velocity are avoided. The controller is mathematically derived based on Lyapunov stability analysis. Simulation results of the two cases are presented to verify the effectiveness and superiority of the proposed controller.     

Adaptive dynamic surface control for MIMO nonlinear time-varying systems with prescribed tracking performance

In this paper, an adaptive dynamic surface control scheme is proposed for a class of multi-input multi-output (MIMO) nonlinear time-varying systems. By fusing a bound estimation approach, a smooth function and a time-varying matrix factorisation, the obstacle caused by unknown time-varying parameters is circumvented. The proposed scheme is free of the problem of explosion of complexity and needs only one updated parameter at each design step. Moreover, all tracking errors can converge to predefined arbitrarily small residual sets with a prescribed convergence rate and maximum overshoot. Such features result in a simple adaptive controller which can be easily implemented in applications with less computational burden and satisfactory tracking performance. Simulation results are presented to illustrate the effectiveness of the proposed scheme.     

多智能体系统自适应跟踪控制

基于带有非线性动态的二阶多智能体系统,研究了在有动态领导者条件下的跟踪一致性问题。假设跟随者只能获取邻居智能体的相对状态信息,只有一部分跟随者可以获得领导者的位置和速度信息,领导者的控制输入非零且不被任何一个跟随者可知。在通信拓扑为无向连通图的条件下,为了避免全局信息的不确定性,设计了分布式自适应控制协议。将系统的一致性问题转化为误差系统的一致性问题,通过Lyapunov稳定性理论和矩阵理论分析得到了该协议使系统达到一致的充分条件。最后用仿真例子证明了设计方法的有效性。     

Detection and tracking for robotic visual servoing systems

Robot manipulators require knowledge about their environment in order to perform their desired actions. In several robotic tasks, vision sensors play a critical role by providing the necessary quantity and quality of information regarding the robot's environment. For example, “visual servoing” algorithms may control a robot manipulator in order to track moving objects that are being imaged by a camera. Current visual servoing systems often lack the ability to detect automatically objects that appear within the camera's field of view. In this research, we present a robust “figureiground” framework for visually detecting objects of interest. An important contribution of this research is a collection of optimization schemes that allow the detection framework to operate within the real-time limits of visual servoing systems. The most significant of these schemes involves the use of “spontaneous” and “continuous” domains. The number and location of continuous domains are. allowed to change over time, adjusting to the dynamic conditions of the detection process. We have developed actual servoing systems in order to test the framework's feasibility and to demonstrate its usefulness for visually controlling a robot manipulator.     

基于自适应免疫整定的机器人无标定自抗扰视觉伺服控制

研究了机器人无标定自抗扰视觉伺服控制问题. 针对系统中所用的自抗扰控制器参数选取困难问题, 提出了基于自适应免疫算法的自抗扰控制器参数整定方法. 证明了系统中所用的非线性离散二阶扩张状态观测器稳定的充要条件, 并将该条件应用在参数整定过程中. 6 自由度工业机器人的实验结果表明了该方法的可行性和有效性.