Adaptive Jacobian force/position tracking for space free-flying robots with prescribed transient performance

This paper presents a tracking control with guaranteed prescribed performance (PP) for space free-flying robots with uncertain kinematics (Jacobian matrix) and dynamics, uncertain normal force parameter, and bounded disturbances in a compliant contact with a planar surface. Given the orientation of the surface and a nonlinear model of the elastic force, a controller is designed requiring no information on the robot parameters and the disturbances. This controller will guarantee that the tracking errors satisfy PP indexes such as the maximum steady-state errors and overshoots, and the minimum convergence rates. Thus, contact maintenance can be ensured as prescribed. An approximation of the Jacobian is utilized in the presence of uncertain robot kinematics, and PP position/attitude tracking of the free-flying base is achieved in addition to the PP force/position tracking of the manipulator’s fingertip. The proposed controller is based on an error transformation technique, and a directly tunable gain for the transformed error feedback is introduced in the control to trade off between the tracking performance and control effort. Numerical simulations and comparisons demonstrate the effectiveness and superiority of the proposed controller.     

Autonomous mobile robots navigation using RBF neural compensator

This paper presents an approach to adaptive trajectory tracking of mobile robots which combines a feedback linearization based on a nominal model and a RBF-NN adaptive dynamic compensation. For a robot with uncertain dynamic parameters, two controllers are implemented separately: a kinematics controller and an inverse dynamics controller. The uncertainty in the nominal dynamics model is compensated by a neural adaptive feedback controller. The resulting adaptive controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. The analysis of the RBF-NN approximation error on the control errors is included. Finally, the performance of the control system is verified through experiments.     

悬挂伸缩刀具的树障清理空中机器人飞行控制方法

针对输电线路附近的树障进行清理问题,本文提出了一种新型的悬挂伸缩刀具的树障清理空中机器人并进行了仿真和实物验证.首先,对悬挂伸缩刀具的空中机器人进行了伸缩刀具重心变化下的动力学、运动学建模及接触建模.其次,为避免空中机器人接触作业时机器人倾翻的问题,设计了力估计器用于力感知和导纳控制器用于力控制.针对空中机器人非线性强耦合、伸缩刀具时参数摄动及作业时扰动的问题,设计了线性自抗扰控制(LADRC)的机器人位姿控制器.再次,数值仿真验证了导纳控制能有效避免空中机器人接触作业时产生倾翻的问题,以及基于LADRC控制器的位姿控制具有良好的稳定性和抗扰性.最后,通过实物飞行和接触作业测试,进一步验证了本文悬挂伸缩刀具的树障清理空中机器人及其控制方法的有效性.     

Adaptive task-space regulation of rigid-link flexible-joint robots with uncertain kinematics

In this paper, an adaptive control scheme is proposed for the regulation problem of rigid-link flexible-joint (RLFJ) robots with uncertain kinematics. Existing research works in literature on RLFJ robot control assume exact knowledge of the kinematics of robot, and no result that can deal with kinematics uncertainty in RLFJ robot has been proposed so far. This paper presents the first study addressing this problem. The adaptive control scheme proposed can deal with the kinematics uncertainty and uncertainties in both link and actuator dynamics of the RLFJ robot system. A nonlinear observer is designed to avoid the use of acceleration due to the fourth-order overall dynamics. Asymptotic stability of the closed-loop system is shown and sufficient conditions are presented to guarantee the stability. Simulation results are provided to illustrate the effectiveness of the proposed control method.     

基于二阶运动学模型的移动机器人主从跟踪系统的鲁棒控制

In this paper, we study the problem of modeling and controlling leader-follower formation of mobile robots. First, a novel kinematics model for leader-follower robot formation is formulated based on the relative motion states between the robots and the local motion of the follower robot. Using this model, the relative centripetal and Coriolis accelerations between robots are computed directly by measuring the relative and local motion sensors, and utilized to linearize the nonlinear system equations. A formation controller, consisting of a feedback linearization part and a sliding mode compensator, is designed to stabilize the overall system including the internal dynamics. The control gains are determined by solving a robustness inequality and assumed to satisfy a cooperative protocol that guarantees the stability of the zero dynamics of the formation system. The proposed controller generates the commanded acceleration for the follower robot and makes the formation control system robust to the effect of unmeasured acceleration of the leader robot. Furthermore, a robust adaptive controller is developed to deal with parametric uncertainty in the system. Simulation and experimental results have demonstrated the effectiveness of the proposed control method.     

基于神经网络的不确定移动机器人鲁棒自适应跟踪控制

针对含运动学未知参数以及动力学模型不确定的非完整轮式移动机器人轨迹跟踪问题,基于Radical Basis Function(径向基函数)神经网络,提出了一种鲁棒自适应控制器.首先,考虑移动机器人运动学参数未知的情况,提出了一种含自适应参数的运动学控制器,用以补偿参数不确定性导致的系统误差;其次,利用神经网络控制技术,对于机器人在移动中动力学模型不确定问题,提出了一种具有鲁棒性的动力学控制器,使得移动机器人可以在不知道具体动力学模型的情况下跟踪到目标轨迹;最后利用Lyapunov稳定性理论证明了整个系统的稳定性.通过数值仿真验证了所设计的控制器的可行性.     

轮子打滑状态下全向移动机器人轨迹跟踪控制

针对全向移动机器人轨迹跟踪控制中存在未知轮子打滑干扰问题,设计自抗扰反步控制器.首先,建立存在轮子打滑扰动的全向移动机器人的运动学模型;然后,融合自抗扰控制技术与反步控制技术,设计基于全向移动机器人运动学模型的轨迹跟踪控制器,该控制器分别从纵向控制、横向控制及姿态控制上对打滑干扰进行实时估计与补偿;最后,利用Lyapunov定理分析闭环系统的稳定性并通过仿真实验验证了所提出控制算法的有效性和鲁棒性.     

Feedback control for robotic manipulator with an uncertain Jacobian matrix

In most applications of robots, a desired path for the end‐effector is usually specified in task space such as Cartesian space. One way to move the robot along this path is to solve the inverse kinematics problem to generate the desired angles in joint space. However, it is a very time consuming task to solve the inverse kinematics problem. Furthermore, in the presence of uncertainty in kinematics, it is impossible to derive the desired joint angle from the desired end‐effector path and the Jacobian matrix of the mapping from joint space to task space. In this article, a feedback control law using an uncertain Jacobian matrix is proposed for setpoint control of robots. Sufficient conditions for the bound of the estimated Jacobian matrix and stability conditions for the feedback gains are presented to guarantee the stability and passivity of the robots. A gravity regressor with an uncertain Jacobian matrix is also proposed for gravitational force compensation when the gravitational force is uncertain. Simulation results are presented to illustrate the performance of the proposed controllers. ©1999 John Wiley & Sons, Inc.     

Synchronized adaptive control for coordinating manipulators with time‐varying actuator constraint and uncertain dynamics

This paper addresses the control issue for cooperative visual servoing manipulators on strongly connected graph with communication delays, in which case that the uncertain robot dynamics and kinematics, uncalibrated camera model, and actuator constraint are simultaneously considered. An adaptive cooperative image‐based approach is established to overcome the control difficulty arising from nonlinear coupling between visual model and robot agents. To estimate the coupled camera‐robot parameters, a novel adaptive strategy is developed and its superiority mainly lies in the containment of both individual image‐space errors and the synchronous errors among networked robots; thus, the cooperative performance is significantly strengthened. Moreover, the proposed cooperative controller with a Nussbaum‐type gain is implemented to both globally stabilize the closed‐loop systems and realize the synchronization control objective under the existence of unknown and time‐varying actuator constraint. Finally, simulations are carried out to validate the developed approach.     

移动机器人轨迹跟踪的参数自校正控制

根据移动机器人运动学模型,对两轮移动机器人的轨迹跟踪问题进行了研究。在动态反馈线性化的基础上,采用基于广义最小方差法实现参数的自校正并设计控制器,考虑到机器人的动力学约束,采取对移动机器人的线速度和角速度加以限制的控制策略,保证机器人的运动平滑。仿真结果表明了该方法的有效性和正确性。     

An Intelligent Robust Tracking Control for a Class of Electrically Driven Mobile Robots

This paper addresses the problem of designing robust tracking control for a class of uncertain wheeled mobile robots actuated by brushed direct current motors. This class of electrically‐driven mechanical systems consists of the robot kinematics, the robot dynamics, and the wheel actuator dynamics. Via the backstepping technique, an intelligent robust tracking control scheme that integrates a kinematic controller and an adaptive neural network‐based (or fuzzy‐based) controller is developed such that all of the states and signals of the closed‐loop system are bounded and the tracking error can be made as small as possible. Two adaptive approximation systems are constructed to learn the behaviors of unknown mechanical and electrical dynamics. The effects of both the approximation errors and the unmodeled time‐varying perturbations in the input and virtual‐input weighting matrices are counteracted by suitably tuning the control gains. Consequently, the robust control scheme developed here can be employed to handle a broader class of electrically‐driven wheeled mobile robots in the presence of high‐degree time‐varying uncertainties. Finally, a simulation example is given to demonstrate the effectiveness of the developed control scheme.     

轮式机器人滑模轨迹跟踪控制器设计

轮式机器人是一个典型的非完整性系统。由于非线性和非完整特性,很难为移动机器人系统的轨迹跟踪建立一个合适的模型。介绍了一种轮式机器人滑模轨迹跟踪控制方法。滑模控制是一个鲁棒的控制方法,能渐近的按一条所期望的轨迹稳定移动机器人。以之为基础,描述了轮式机器人的动力学模型并在二维坐标下建立了运动学方程,根据运动学方程设计滑模控制器,该控制器使得机器人的位置误差收敛到零。     

全向移动机器人编队分布式控制研究

简单介绍了NuBot机器人的两个主要组成部分：全向视觉和全向运动系统,并给出了运动学分析．基于该机器人平台,提出了D-A和D-D控制两种跟踪算法．通过机器人之间的相对定位和局部通信,实现了多机器人编队的分布式控制,同时,该算法可对机器人朝向进行独立控制．针对不同情况下的编队避障问题,提出了编队变形和编队变换两种方法．仿真和实际机器人实验表明,D-A控制方法能够实现平滑的编队变换;编队变形方法能够在尽量保持原始队形的情况下保证编队顺利避障．     

油浸式变压器内部检测球形机器人的深度悬停控制研究

为实现设计的油浸式变压器内部检测机器人在实际作业过程中能针对深度方向某具体点进行观测,对机器人的深度悬停控制进行研究.通过对机器人控制策略的分析,根据水下机器人动力学理论,建立机器人在变压器油特殊介质的动力学模型.基于鲁棒反演控制方法及滑模自适应控制理论,提出一种鲁棒反演滑模控制方法,采用模糊控制器设计滑模面切换增益,以削弱不确定干扰带来的系统抖振,并通过Lyapunov理论分析证明控制器稳定性.解决了机器人在变压器油中因耦合、外界扰动等造成的深度悬停定点过程自旋及抖动问题,仿真及实验表明了所提出控制器的有效性.     

非完整移动机器人轨迹跟踪自适应控制器设计

针对轮式移动机器人的非完整运动学模型,将自适应反演控制技术和李亚普诺夫稳定性理论应用于机器人轨迹跟踪控制,设计了具有全局渐近稳定性的自适应轨迹跟踪控制器,并在Matlab环境下实现了移动机器人对直线和椭圆2种轨迹追踪的仿真实验.实验表明:该控制方法在轨迹跟踪控制中有较好的航向跟踪效果,对机器人非完整系统模型的非线性特性...     

Neural Network Control of Multifingered Robot Hands Using Visual Feedback

It is interesting to observe that humans are able to manipulate an object easily and skillfully without the exact knowledge of the object, contact points, or kinematics of our fingers. However, research so far on multifingered robot control has assumed that the kinematics and contact points of the fingers are known exactly. In many applications of multifingered robot hands, the kinematics and contact points of the fingers are uncertain and structures of the Jacobian matrices are unknown. In this paper, we propose an adaptive neural network (NN) Jacobian controller for multifingered robot hand with uncertainties in kinematics, Jacobian matrices, and dynamics. It is shown that using NNs, the uniform ultimate boundedness of the position error can be achieved in the presence of the uncertainties. Simulation results are presented to illustrate the performance of the proposed controller.     

滑动与打滑条件下的轮式移动机器人自抗扰跟踪控制

针对具有未知的滑动与打滑的轮式移动机器人(WMR),提出了一种基于自抗扰思想的跟踪控制策略.首先建立了滑动与打滑条件下的轮式移动机器人动力学模型.其次,由反步法设计运动学控制器,基于模型设计线性扩张观测器和动力学控制器,并给出了控制器稳定性分析.最后与积分滑模控制进行了仿真对比,结果表明该控制方法的误差收敛速度更快.观测器能够精确估计滑动与打滑及动力学不确定性对机器人的扰动,提高了轮式移动机器人轨迹跟踪的鲁棒性.     

Robust dynamic surface control of flexible joint robots using recurrent neural networks

A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.     

Approximate Jacobian control with task-space damping for robot manipulators

In this note, we propose two new approximate Jacobian control laws with task-space damping for setpoint control of robot manipulators. The proposed controllers do not require exact knowledge of the Jacobian matrix and dynamics of the robots. We will show that the end-effector's position converges to a desired position in a finite task space even when the kinematics and Jacobian matrix are uncertain. Experimental results are presented to illustrate the performance of the proposed controllers.     

Adaptive Jacobian vision based control for robots with uncertain depth information

This paper presents a simple vision based setpoint controller with adaptation to uncertainty in depth information. Depth uncertainty plays a special role in vision based control as it appears nonlinearly in the overall Jacobian matrix and hence cannot be adapted together with other uncertain kinematic parameters. We propose a novel parameter update law to update the uncertain parameters of the depth. It is proved that system stability can be guaranteed for the vision regulation task in presence of uncertainties in depth information, robot kinematics and dynamics. Simulation results are presented to illustrate the performance of the proposed controller.