Adaptive Jacobian position/force tracking control of free-flying manipulators

This paper solves the problem of position/force tracking control of a free-flying space manipulator with uncertain kinematics and dynamics. A free-flying manipulator interacting with an uncertain compliant surface is considered. To cope with the uncertainties arising from free-flyer’s kinematics, dynamics and surface stiffness and position, an adaptive Jacobian controller is devised. The convergence of the force and position tracking errors is proved based on Lyapunov stability analysis. Numerical simulation is presented to show the performance of the controller.     

Robust adaptive motion/force tracking control design for uncertain constrained robot manipulators

In the presence of uncertain constraint and robot model, an adaptive controller with robust motion/force tracking performance for constrained robot manipulators is proposed. First, robust motion and force tracking is considered, where a performance criterion containing disturbance and estimated parameter attenuations is presented. Then the proposed controller utilizes an adaptive scheme and an auxiliary control law to deal with the uncertain environmental constraint, disturbances, and robotic modeling uncertainties. After solving a simple linear matrix inequality for gain conditions, the effect from disturbance and estimated parameter errors to motion/force errors is attenuated to an arbitrary prescribed level. Moreover, if the disturbance and estimated parameter errors are square-integrable, then an asymptotic motion tracking is achieved while the force error is as small as the inversion of control gain. Finally, numerical simulation results for a constrained planar robot illustrate the expected performance.     

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.     

Prediction Error Based Adaptive Jacobian Tracking of Robots With Uncertain Kinematics and Dynamics

In this note, we have proposed a prediction error based adaptive Jacobian controller to solve the problem of concurrent adaptation to both uncertain kinematics and dynamics. This controller is composed of a modified computed torque controller and two cushion floor least-square estimators, and in ideal case of perfect knowledge of the robot parameters it leads to linear and decoupled error dynamics. The kinematic and dynamic parameters adaptations are driven by prediction errors. Using input-output stability analysis, we show that the end-effector motion tracking errors converge asymptotically. We have also derived an alternative adaptive Jacobian controller that does not require the invertibility of the estimated inertia matrix. Simulation results are presented to show the performance of the proposed controllers.      

Guaranteeing prescribed performance and contact maintenance via an approximation free robot force/position controller

In this paper, we consider the problem of force/position tracking for a robot with revolute joints in compliant contact with a kinematically known planar surface. A novel controller is designed capable of guaranteeing, for an a priori known nonsingular initial robot condition, (i) certain predefined minimum speed of response, maximum steady state error as well as overshoot concerning the force/position tracking errors, (ii) contact maintenance and (iii) bounded closed loop signals. No information regarding either the robot dynamic model or the force deformation model is required and no approximation structures are utilized to estimate them. As the tracking performance is a priori guaranteed irrespectively of the control gains selection, the only concern is to adopt those values that lead to reasonable input torques. Finally, a comparative simulation study on a 6-DOF robot illustrates the performance 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.     

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.     

全方位轮式下肢康复训练机器人轨迹跟踪控制

全方位轮式下肢康复训练机器人需要跟踪医生给受训者设定的轨迹.机器人自身的不确定性和患者在康复训练时对机器人运行产生的干扰会影响其跟踪性能.本文设计了一种非线性L<,2>鲁棒控制器解决上述扰动抑制问题.给出了下肢康复训练机器人的运动学模型,并由此建立了位置误差系统的动态模型.将机器人的跟踪和不确定干扰抑制归结为L<,2>...     

Task-space neuro-sliding mode control of robot manipulators under Jacobian uncertainties

Cartesian robot control is an appealing scheme because it avoids the computation of inverse kinematics, in contrast to joint robot control approach. For tracking, high computational load is typically required to obtain Cartesian robot dynamics. In this paper, an alternative approach for Cartesian tracking is proposed under assumption that robot dynamics is unknown and the Jacobian are uncertain. A neuro-sliding second order mode controller delivers a low dimensional neural network, which roughly estimates inverse robot dynamics, and an inner smooth control loop guarantees exponential tracking. Experimental results are presented to confirm the performance in a real time environment.     

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.     

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.      

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.     

Adaptive non-singular fast terminal sliding mode based on prescribed performance for robot manipulators

A fast convergent non-singular terminal sliding mode adaptive control law based on prescribed performance is formulated to solve the uncertainties and external disturbances of robot manipulators. First, the tracking error of robot manipulators is transformed by using the prescribed performance function, which improves the transient behaviors and steady-state accuracy of robot manipulators. Then, a novel fast convergent non-singular terminal sliding mode surface is brought up according to the transformed error, and the control law is derived to meet the stability requirements of robot manipulators. In practice, the upper boundary of the lumped disturbances cannot be accurately obtained. Therefore, an adaptive prescribed performance control (PPC) controller to lumped disturbances is brought up to ensure the stability and finite-time convergence of robot manipulators. Finally, the system stability of robot manipulators is proved by the Lyapunov theorem. Simulation results and comparative analysis demonstrate the superiority and robustness of the raised strategy.     

A Neuro-Sliding Mode Control Scheme for Constrained Robots with Uncertain Jacobian

The joint robot control requires to map desired cartesian tasks into desired joint trajectories, by using the ill-posed inverse kinematics mapping. In order to avoid inverse kinematics, the control problem is formulated directly in task space to gives rise to cartesian robot control. In addition, when the robot is constrained due to its kinematic mappings yields a stiff system and an additional complexity arises to implement cartesian control for constrained robots. In this paper, an alternative approach is proposed to guarantee global convergence of force and position cartesian tracking errors under the assumption that the jacobian is not exactly known. A neuro-sliding mode controller is presented, where a small size adaptive neural network compensates approximately for the inverse dynamics and an inner control loop induces second order sliding modes to guarantee tracking. The sliding mode variable tunes the online adaptation of the weights. A passivity analysis yields the energy Lyapunov function to prove boundedness of all closed-loop signals and variable structure control theory is used to finally conclude convergence of position and force tracking errors. Experimental results are provided to visualize the expected performance.     

Multiple-arm space free-flying robots for manipulating objects with force tracking restrictions

Multiple Impedance Control (MIC) is an algorithm that enforces designated impedance at various levels, i.e. on the manipulated object, all cooperating manipulators, and the moving platform of a robotic system. In this paper, a force tracking strategy inspired by a human control system is added to the MIC algorithm and the general formulation is revised to fulfill a desired force tracking strategy for object manipulation tasks. The stability analysis of the MIC algorithm based on the Liapunov Direct Method, besides error analysis, shows that a good tracking of cooperative manipulators and the manipulated object is guaranteed. Next, using MAPLE and MATLAB tools, a system of three manipulators mounted on a space free-flying robot is simulated. The task is moving an object based on given trajectories which come across an obstacle, to examine the performance of the developed control law. The results show that, even in the presence of both external disturbances and an impact due to collision with the obstacle, the response of the MIC algorithm is smooth. Moreover, based on the embedded force tracking strategy, the contact force is confined to follow a desired trajectory. Also, it is shown that decreasing the values of the controller mass matrix elements results in reducing both the object position and force tracking error.     

Convergence and robustness of a discrete-time learning control scheme for constrained manipulators

The constrained motion control is one of the most common control tasks found in many industrial robot applications. The nonlinear and nonclassical nature of the dynamic model of constrained robots make designing a controller for accurate tracking of both motion and force a difficult problem. In this article, a discrete-time learning control problem for precise path tracking of motion and force for constrained robots is formulated and solved. The control system is able to reduce the tracking error iteratively in the presence of external disturbances and errors in initial condition as the robot repeats its action. Computer simulation result is presented to demonstrate the performance of the proposed learning controller. © 1994 John Wiley & Sons, Inc.     

On the decoupling of position and force controllers in constrained robotic tasks

In hybrid control of robot manipulators separate controllers are designed for force and position errors control. Controllers are designed either in task or joint space and their outputs combine to provide input torque to the manipulator. Position and force controllers performance in a constrained robotic task is affected by their interaction to a degree dependent on the controller's ability to reject disturbances. Ideally, decoupling of the two control loops is desired to achieve the best performance in position and force directions. In this article, analysis of control loop interactions is performed for contact and noncontact phases, and controller design requirements are developed to achieve maximum decoupling. Design requirements involve output subspace of each controller leading to control discontinuities for contact and noncontact phases. In the noncontact phase, satisfaction of design requirements leads to a fully linearized and decoupled system. When in contact with the constraining surface, design requirements eliminate disturbances in the force loop, but minimize disturbances in the position loop to an extent dependent on force loop performance. Known hybrid control schemes analysis is performed to reveal existence of control loop interactions in these schemes. Confirmation of theoretical analysis is done through simulation of a three revolute planar manipulator. © 1998 John Wiley & Sons, Inc.     

Perfect position/force tracking of robots with dynamical terminal sliding mode control

According to a given performance criteria, perfect tracking is defined as the performance of zero tracking error in finite time. It is evident that robotic systems, in particular those that carry out compliant task, can benefit from this performance since perfect tracking of contact forces endows one or many constrained robot manipulators to interact dexterously with the environment. In this article, a dynamical terminal sliding mode controller that guarantees tracking in finite‐time of position and force errors is proposed. The controller renders a dynamic sliding mode for all time and since the equilibrium of the dynamic sliding surface is driven by terminal attractors in the position and force controlled subspaces, robust finite‐time convergence for both tracking errors arises. The controller is continuous; thus chattering is not an issue and the sliding mode condition as well the invariance property are explicitly verified. Surprisingly, the structure of the controller is similar with respect to the infinite‐time tracking case, i.e., the asymptotic stability case, and the advantage becomes more evident because terminal stability properties are obtained with the same Lyapunov function of the asymptotic stability case by using more elaborate error manifolds instead of a more complicated control structure. A simulation study shows the expected perfect tracking and a discussion is presented. © 2001 John Wiley & Sons, Inc.     

欠驱动水下机器人三维轨迹跟踪有限时间预设性能控制

本文研究了具有不确定动态和未知时变海洋环境扰动的欠驱动水下机器人(AUVs)三维轨迹跟踪有限时间预设性能控制问题,提出新型预设性能函数和误差映射函数,将受预设性能限制的轨迹跟踪误差转变为非受限的变换后误差;构造新的超螺旋(ST)扩张状态观测器,在有限时间内实时估计AUV不确定动态和未知时变海洋环境扰动引起的总扰动;基于...     

Model-free robot joint position regulation and tracking with prescribed performance guarantees

The problem of robot joint position control with prescribed performance guarantees is considered; the control objective is the error evolution within prescribed performance bounds in both problems of regulation and tracking. The proposed controllers do not utilize either the robot dynamic model or any approximation structures and are composed by simple PID or PD controllers enhanced by a proportional term of a transformed error through a transformation related gain. Under a sufficient condition for the damping gain, the proposed controllers are able to guarantee (i) predefined minimum speed of convergence, maximum steady state error and overshoot concerning the position error and (ii) uniformly ultimate boundedness (UUB) of the velocity error. The use of the integral term reduces residual errors allowing the proof of asymptotic convergence of both velocity and position errors to zero for the regulation problem under constant disturbances. Performance is a priori guaranteed irrespective of the selection of the control gain values. Simulation results of a three dof spatial robotic manipulator and experimental results of one dof manipulator are given to confirm the theoretical findings.