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.     

Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model

A neural-network-based adaptive controller is proposed for the tracking problem of manipulators with uncertain kinematics, dynamics and actuator model. The adaptive Jacobian scheme is used to estimate the unknown kinematics parameters. Uncertainties in the manipulator dynamics and actuator model are compensated by three-layer neural networks. External disturbances and approximation errors are counteracted by robust signals. The actuator controller is designed based on the backstepping scheme. Compared with the existing work, the proposed method considers the manipulator kinematics uncertainty, does not need the “linearity-in-parameters” assumption for the uncertain terms in the dynamics of manipulator and actuator, and guarantees the tracking error to be as small as desired. Finally, the performance of the proposed approach is illustrated by the simulation example.     

Constrained motion control of flexible robot manipulators based on recurrent neural networks

In this paper, a neural network approach is presented for the motion control of constrained flexible manipulators, where both the contact force everted by the flexible manipulator and the position of the end-effector contacting with a surface are controlled. The dynamic equations for vibration of flexible link and constrained force are derived. The developed control, scheme can adaptively estimate the underlying dynamics of the manipulator using recurrent neural networks (RNNs). Based on the error dynamics of a feedback controller, a learning rule for updating the connection weights of the adaptive RNN model is obtained. Local stability properties of the control system are discussed. Simulation results are elaborated on for both position and force trajectory tracking tasks in the presence of varying parameters and unknown dynamics, which show that the designed controller performs remarkably well.     

Robust controller design for a class of nonlinear robot manipulators with actuator dynamics

A robust controller is proposed to achieve accurate tracking for a class of nonlinear uncertain robot manipulators with actuator dynamics. A dynamic model is derived that incorporates the manipulator dynamics with the actuator dynamics. The parameter uncertainty in the model is quantified using the linear parameterization technique. The proposed switching controller guarantees the global asymptotic stability. To rectify the chattering caused by the switching action of the controller, the controller is modified into a smoothed form by putting a boundary layer around the sliding surface. It is shown that the smoothing controller guarantees the uniform ultimate boundedness of the actual trajectory. Simulation results show the feasibility and excellent tracking performance of the controller.     

Learning Control of Robot Manipulators in Task Space

Two important properties of industrial tasks performed by robot manipulators, namely, periodicity (i.e., repetitive nature) of the task and the need for the task to be performed by the end‐effector, motivated this work. Not being able to utilize the robot manipulator dynamics due to uncertainties complicated the control design. In a seemingly novel departure from the existing works in the literature, the tracking problem is formulated in the task space and the control input torque is aimed to decrease the task space tracking error directly without making use of inverse kinematics at the position level. A repetitive learning controller is designed which “learns” the overall uncertainties in the robot manipulator dynamics. The stability of the closed‐loop system and asymptotic end‐effector tracking of a periodic desired trajectory are guaranteed via Lyapunov based analysis methods. Experiments performed on an in‐house developed robot manipulator are presented to illustrate the performance and viability of the proposed controller.     

Dynamics and control of a novel 3-DOF parallel manipulator with actuation redundancy

This paper deals with the dynamics and control of a novel 3-degrees-of-freedom （DOF） parallel manipulator with actuation redundancy. According to the kinematics of the redundant manipulator, the inverse dynamic equation is formulated in the task space by using the Lagrangian formalism, and the driving force is optimized by utilizing the minimal 2-norm method. Based on the dynamic model, a synchronized sliding mode control scheme based on contour error is proposed to implement accurate motion tracking control. Additionally, an adaptive method is introduced to approximate the lumped uncertainty of the system and provide a chattering-free control. The simulation results indicate the effectiveness of the proposed approaches and demonstrate the satisfactory tracking performance compared to the conventional controller in the presence of the parameter uncertainties and un-modelled dynamics for the motion control of manipulators.     

Position and force tracking of a two‐manipulator system manipulating a flexible beam

This article discusses the issue of hybrid position and force control of a two‐manipulator system manipulating a flexible beam in trajectory tracking. Unlike our previous approach of set‐point position control in the trajectory tracking, the system coordinates are hard to be regulated to the desired states with nonzero tracking velocities under continuous feedback control. In this study, we design a hybrid position and force tracking controller while using saturation control to compensate for the effect of beam vibration dynamics on the tracking performance. All parameters and states used in the controller are readily available so that the proposed method is feasible to implement. Under the proposed controller, the tracking error asymptotically converges to a predetermined boundary. Simulation results demonstrate the validity of the proposed approach. © 2001 John Wiley & Sons, Inc.     

基于图像的双臂模糊自适应轨迹跟踪控制

在固定相机的监视下,随机位姿的目标物体在双臂系统的控制下执行轨迹跟踪控制任务时,既要考虑目标随机位姿引起的运动学和动力学不确定性问题,还要考虑运动学和动力学的协同问题.针对上述问题,分别采用自适应方法估计目标质心和特征点的位置信息,利用模糊逻辑系统逼近系统的动力学模型,使用分散控制策略处理双臂的协同问题,最后基于位置/力混合控制方法设计基于图像的双臂模糊自适应轨迹跟踪控制器,并采用李亚普诺夫方法证明系统的稳定性.仿真实验验证了所设计控制器的有效性.     

Sliding mode force,motion control,and stabilization of elastic manipulator in the presence of uncertainties

This article considers the question of position and force control of three-link elastic robotic systems on a constraint surface in the presence of robot parameter and environmental constraint geometry uncertainties. The approach of this article is applicable to any multi-link elastic robot. A sliding mode control law is derived for the position and force trajectory control of manipulator. Unlike the rigid robots, sliding mode control of an end point gives rise to unstable zero dynamics. Instability of the zero dynamics is avoided by Controlling a point that lies in the neighborhood of the actual end point position. The sliding mode controller accomplishes tracking of the end-effector and force trajectories on the constrained surface; however, the maneuver of the arm causes elastic mode excitation. For point-to-point control on the constraint surface, a stabilizer is designed for the final capture of the terminal state and vibration suppression. Numerical results are presented to show that in the closed-loop system position and force control is accomplished in spite of payload and constraint surface geometry uncertainty. © 1995 John Wiley & Sons, Inc.     

Sliding mode-based adaptive tube model predictive control for robotic manipulators with model uncertainty and state constraints

In this paper, the optimal tracking control for robotic manipulatorswith state constraints and uncertain dynamics is investigated, and a sliding mode-based adaptive tube model predictive control method is proposed. First, utilizing the high-order fully actuated system approach, the nominal model of the robotic manipulator is constructed as the predictive model. Based on the nominal model, a nominal model predictive controller with the sliding mode is designed, which relaxes the terminal constraints, and realizes the accurate and stable tracking of the desired trajectory by the nominal system. Then, an auxiliary controller based on the node-adaptive neural networks is constructed to dynamically compensate nonlinear uncertain dynamics of the robotic manipulator. Furthermore, the estimation deviation between the nominal and actual states is limited to the tube invariant sets. At the same time, the recursive feasibility of nominal model predictive control is verified, and the ultimately uniformly boundedness of all variables is proved according to the Lyapunov theorem. Finally, experiments show that the robotic manipulator can achieve fast and efficient trajectory tracking under the action of the proposed method.     

Adaptive tracking control of manipulators: Theory and experiments

This paper considers the trajectory tracking problem for uncertain robot manipulators and proposes two adaptive controllers as solutions to this problem. The first controller is derived under the assumption that the manipulator state is measurable, while the second strategy is developed for those applications in which only position measurements are available. The adaptive schemes are very general and computationally efficient since they do not require knowledge of either the mathematical model or the parameter values of the manipulator dynamics, and are implemented without calculation of the robot inverse dynamics or inverse kinematic transformation. It is shown that the control strategies ensure uniform boundedness of all signals in the presence of bounded disturbances, and that the ultimate size of the tracking errors can be made arbitrarily small. Experimental results are presented for a PUMA 560 manipulator and demonstrate that accurate and robust trajectory tracking can be achieved by using the proposed controllers.     

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.     

Adaptive tracking control of rigid manipulators using only position measurements

This article presents a new approach to trajectory tracking control of uncertain rigid manipulators using only position measurements. The proposed control strategy is an adaptive scheme that is very general and computationally efficient, requires virtually no information regarding the manipulator dynamic model, and is implementable without calculation of the robot inverse dynamics or inverse kinematic transformations. It is shown that the controller ensures semiglobal uniform boundedness of all signals in the presence of bounded disturbances, and that the ultimate size of the tracking errors can be made arbitrarily small. Additionally, it is demonstrated that the proposed strategy can be used as the basis for developing controllers for “cascaded” robotic systems, such as manipulators with significant actuator dynamics or joint flexibility. The efficacy of this approach to manipulator control is illustrated through both computer simulations and hardware experiments. © 1997 John Wiley & Sons, Inc.     

A Review of: “Fuzzy Sets and Their Applications”. By C. V. NEGOIT[Abreve] and D. A. RALESCU. (In Rumanian) (Bucharest: Editura Technica, 1974.) [Pp.219.]

This paper presents a new controller for position and force control of robotic devices interacting with passive environments. In this approach, for the manipulator dynamics in joint space, suitable output equations are defined which represent the position control and force control subspaces. The dynamics of the manipulator are projected along these subspaces to obtain the dynamics in the respective subspaces. The resulting dynamics are linearized and decoupled using a nonlinear input-state linearizing controller. For the position control subspace dynamics, desirable stability features are achieved through pole placement design. Along the force control subspace, a soft base is introduced, the compliance effect of which is controlled by an appropriate compensation term. Based on the force feedback information, this compensation is modifed online using an extended dynamics. Assuming a model of the passive environment, aspects of local stability of the controller have been discussed. The theory has been presented for a two-link planar manipulator example, based on which, a numerical simulation is discussed.     

Robust tracking control for rigid robotic manipulators

The problem of robust tracking control using a nominal feedback controller and a variable structure compensator for a rigid robotic manipulator with uncertain dynamics is addressed in this note. It is shown that the effects of large system uncertainties can be eliminated and asymptotic convergence of the output tracking error can be guaranteed by using a variable structure compensator in the closed loop feedback control system for the rigid robotic manipulator     

Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models

Most research so far on robot trajectory control has assumed that the kinematics of the robot is known exactly. However, when a robot picks up tools of uncertain lengths, orientations, or gripping points, the overall kinematics becomes uncertain and changes according to different tasks. Recently, we derived a new adaptive Jacobian tracking controller for robots with uncertain kinematics and dynamics. This note extends the results to include redundant robots and adaptation to actuator parameters. Experimental results are presented to illustrate the performance of the proposed controller.     

受限机器人神经形态的变结构位置/力控制

本文基于Ｊｅａｎ和Ｆｕ（１９９３）建立的受限机器人模型的降型阶形式，利用变结构系统理论，设计了具有未知动态的受限机器人轨道／力追踪控制，提出的学习方法仅仅利用了机器人动态模型的一般结构，不需要其精确信息，计算迅速，易于实现，仿真结果验证了提出的方法的有效性。     

Composite Nonlinear Bilateral Control for Teleoperation Systems With External Disturbances

This paper presents a new composite nonlinear bilateral control method based on the nonlinear disturbance observer (NDOB) for teleoperation systems with external disturbances. By introducing the estimations of NDOB and systems' nominal nonlinear dynamics into controller design, a NDOB based composite nonlinear bilateral controller is constructed to attenuate the influence of disturbance and uncertain nonlinearities. As compared with the existing bilateral control methods which usually achieve force haptic (i.e., contact force tracking) through a passive way, the newly proposed method has two major merits: 1) asymptotical convergence of both position and force tracking errors is guaranteed; 2) disturbance influence on force tracking error dynamics is rejected through the direct feedforward compensation of disturbance estimation. Simulations on a nonlinear teleoperation system are carried out and the results validate the effectiveness of the proposed controller.      

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.