An intermittent controller for robotic manipulator with uncertain dynamics in task space

2020 ◽  
pp. 2150021
Author(s):  
Mihua Ma ◽  
Jianping Cai
Keyword(s):  
Robotic Manipulator ◽  
Joint Space ◽  
Intermittent Control ◽  
Task Space ◽  
End Effector ◽  
Uncertain Dynamics ◽  
Barbalat’S Lemma ◽  
Adaptation Law ◽  
To Receive ◽  
Dynamic Uncertainties

An intermittent controller for robotic manipulator in the presence of dynamic uncertainties was developed in this paper. The adaptation law is designed to deal with the dynamic uncertainties. In task space, for given a desired position, the robot end-effector is able to reach the desired position under the designed intermittent controller. Different from most of the existing works on control of robotic manipulator, the designed controller only needs to receive the information of the desired position in some interval time, but not continuously. In addition, the intermittent control of robotic manipulator is discussed in task space instead of joint space. Based on an extended Barbalat’s Lemma, some simple control gains are obtained. As a direct application, we implement the proposed controller on a two-link robotic manipulator. Numerical simulations demonstrate the effectiveness of the proposed control strategy.

Robust adaptive command filtered control of a robotic manipulator with uncertain dynamic and joint space constraints

Robotica ◽  
2018 ◽  
Vol 36 (5) ◽  
pp. 767-786 ◽  
Author(s):  
Joseph Jean-Baptiste Mvogo Ahanda ◽  
Jean Bosco Mbede ◽  
Achille Melingui ◽  
Bernard Essimbi Zobo
Keyword(s):  
Closed Loop ◽  
Robotic Manipulator ◽  
Joint Space ◽  
Robust Adaptive Control ◽  
Lyapunov Theory ◽  
Loop System ◽  
Support Vector ◽  
Closed Loop System ◽  
Uncertain Dynamics ◽  
Robust Adaptive

SUMMARYThe problem of robust adaptive control of a robotic manipulator subjected to uncertain dynamics and joint space constraints is addressed in this paper. Command filters are used to overcome the time derivatives of virtual control, thus reducing the need for desired trajectory differentiations. A barrier Lyapunov function is used to deal with the joint space constraints. A robust adaptive support vector regression architecture is used to reduce filtering errors, approximation errors and handle dynamic uncertainties. The stability analysis of the closed-loop system using the Lyapunov theory permits to highlight adaptation laws and to prove that all signals of the closed-loop system are bounded. Simulations show the effectiveness of the proposed control strategy.

Controlled Synchronization of Robotic Manipulators in the Task Space

2009 ◽  
Author(s):  
YenChen Liu ◽  
Nikhil Chopra
Keyword(s):  
Trajectory Tracking ◽  
Control Algorithm ◽  
Robotic Manipulators ◽  
Joint Space ◽  
Task Space ◽  
Numerical Example ◽  
Redundant Robots ◽  
Control Schemes ◽  
Heterogeneous Robots ◽  
Dynamic Uncertainties

Due to its practical applicability, recently several algorithms for robot synchronization have been developed in the literature. However, the focus of these control schemes has primarily been on joint-space control and in the absence of communication unreliabilities between the agents. In this paper, we study the problem of task space synchronization and trajectory tracking for heterogeneous robots under dynamic uncertainties. Exploiting passivity based synchronization results developed previously, a new control algorithm is proposed to guarantee task space synchronization for a group of robotic manipulators. Both non-redundant and redundant robots are considered and the proposed scheme is validated by a numerical example.

scholarly journals Adaptive Task-Space Manipulator Control with Parametric Uncertainties in Kinematics and Dynamics

2020 ◽  
Vol 10 (24) ◽  
pp. 8806
Author(s):  
Chih-Chen Yih ◽  
Shih-Jeh Wu
Keyword(s):  
Adaptive Control ◽  
Joint Space ◽  
Design Parameters ◽  
Kinematics And Dynamics ◽  
Parametric Uncertainties ◽  
Task Space ◽  
Estimation Errors ◽  
Barbalat’S Lemma ◽  
Control Scheme ◽  
Adaptive Control Scheme

This paper aims to deal with the problem of robot tracking control in the presence of parametric uncertainties in kinematics and dynamics. We propose a simple and effective adaptive control scheme that includes adaptation laws for unknown constant kinematic and dynamic parameters. In addition, instead of convolution-type filtered differentiation, we designed a new observer to estimate velocity in the task space, and the proposed adaptive control requires no acceleration measurement in the joint space. Using the Lyapunov stability and Barbalat’s lemma, we show that by appropriately choosing design parameters, the tracking errors and estimation errors in task space can asymptotically converge to zero. Through numerical simulation on a two-link robot with a fixed camera, we illustrate the design procedures and demonstrate the feasibility of the proposed adaptive control scheme for the trajectory tracking of robot manipulators.

scholarly journals Task-Space Admittance Controller with Adaptive Inertia Matrix Conditioning

2021 ◽  
Vol 101 (2) ◽  
Author(s):  
Mariana de Paula Assis Fonseca ◽  
Bruno Vilhena Adorno ◽  
Philippe Fraisse
Keyword(s):  
Feedback Linearization ◽  
Inverse Dynamics ◽  
Poor Performance ◽  
Joint Space ◽  
Robot Dynamics ◽  
Reference Trajectory ◽  
Task Space ◽  
End Effector ◽  
Inertia Matrix ◽  
Matrix Conditioning

AbstractWhenrobots physically interact with the environment, compliant behaviors should be imposed to prevent damages to all entities involved in the interaction. Moreover, during physical interactions, appropriate pose controllers are usually based on the robot dynamics, in which the ill-conditioning of the joint-space inertia matrix may lead to poor performance or even instability. When the control is not precise, large interaction forces may appear due to disturbed end-effector poses, resulting in unsafe interactions. To overcome these problems, we propose a task-space admittance controller in which the inertia matrix conditioning is adapted online. To this end, the control architecture consists of an admittance controller in the outer loop, which changes the reference trajectory to the robot end-effector to achieve a desired compliant behavior; and an adaptive inertia matrix conditioning controller in the inner loop to track this trajectory and improve the closed-loop performance. We evaluated the proposed architecture on a KUKA LWR4+ robot and compared it, via rigorous statistical analyses, to an architecture in which the proposed inner motion controller was replaced by two widely used ones. The admittance controller with adaptive inertia conditioning presents better performance than with a controller based on the inverse dynamics with feedback linearization, and similar results when compared to the PID controller with gravity compensation in the inner loop.

Adaptive path-constrained control of a robotic manipulator in a task space

Robotica ◽  
2006 ◽  
Vol 25 (1) ◽  
pp. 103-112 ◽  
Author(s):  
Mirosław Galicki
Keyword(s):  
Degrees Of Freedom ◽  
Path Following ◽  
Lyapunov Stability Theory ◽  
Robotic Manipulator ◽  
Task Space ◽  
End Effector ◽  
State Dependent ◽  
Control Scheme ◽  
Robot Task ◽  
Three Degrees Of Freedom

This study addresses the problem of adaptive controlling of both a nonredundant and a redundant robotic manipulator with state-dependent constraints. The task of the robot is to follow a prescribed geometric path given in the task space, by the end-effector. The aforementioned robot task has been solved on the basis of the Lyapunov stability theory, which is used to derive the control scheme. A new adaptive Jacobian controller is proposed in the paper for the path following of the robot, with both uncertain kinematics and dynamics. The numerical simulation results carried out for a planar redundant three-DOF (three degrees of freedom) manipulator whose end-effector follows a prescribed geometric path given in a two-dimensional (2D) task space, illustrate the trajectory performance of the proposed control scheme.

Task-space dynamics and motion/force control of fixed-base manipulators under reaction null-space-based redundancy resolution

Robotica ◽  
2015 ◽  
Vol 34 (12) ◽  
pp. 2860-2877 ◽  
Author(s):  
Dragomir Nenchev ◽  
Ryohei Okawa ◽  
Hiroki Sone
Keyword(s):  
Null Space ◽  
Joint Space ◽  
Force Balance ◽  
Redundancy Resolution ◽  
Task Space ◽  
End Effector ◽  
Floating Base ◽  
Fixed Base ◽  
Space Dynamics ◽  
Reaction Null Space

SUMMARYThis paper introduces a task-space dynamics formulation for fixed-base serial-link kinematically redundant manipulators and a motion/force controller based on it. The aim is to alleviate joint-space instability problems that have been observed with other motion/force controllers. The dynamics are represented in floating-base coordinates, wherein the end effector is regarded as the floating base. This representation gives rise to a momentum-conserving redundancy resolution scheme based on the reaction null-space (RNS) method used in past studies on free-floating and flexible-base space robots. A generalized inverse is obtained that is shown to satisfy the conditions for dynamic consistency in the sense of the operational space (OS) formulation, but may lead to the joint-space instabilities observed earlier. The proposed controller is based on the pseudoinverse of the coupling inertia matrix and ensures reactionless link motion that does not disturb the force balance at the end effector. The performance of the RNS motion/force controller is examined by comparison to that with an OS motion/force controller. It is shown that while the performance in task-space of both controllers is satisfactory, the joint-space performance of the proposed controller is superior.

Unknown External Force Estimation and Collision Detection for a Cooperative Robot

Robotica ◽  
2019 ◽  
Vol 38 (9) ◽  
pp. 1665-1681 ◽  
Author(s):  
Shirin Yousefizadeh ◽  
Thomas Bak
Keyword(s):  
Contact Force ◽  
Joint Space ◽  
Estimation Methods ◽  
Control Force ◽  
Task Space ◽  
Force Estimation ◽  
End Effector ◽  
User Input ◽  
Safety Issues ◽  
External Forces

SUMMARYIn human–robot cooperative industrial manipulators, safety issues are crucial. To control force safely, contact force information is necessary. Since force/torque sensors are expensive and hard to integrate into the robot design, estimation methods are used to estimate external forces. In this paper, the goal is to estimate external forces acting on the end-effector of the robot. The forces at the task space affect the joint space torques. Therefore, by employing an observer to estimate the torques, the task space forces can be obtained. To accomplish this, loadcells are employed to compute the net torques at the joints. The considered observers are extended Kalman filter (EKF) and nonlinear disturbance observer (NDOB). Utilizing the computed torque obtained based on the loadcells measurements and the observer, the estimates of external torques applied on the robot end-effector can be achieved. Moreover, to improve the degree of safety, an algorithm is proposed to distinguish between intentional contact force from an operator and accidental collisions. The proposed algorithms are demonstrated on a robot, namely WallMoBot, which is designed to help the operator to install heavy glass panels. Simulation results and preliminary experimental results are presented to demonstrate the effectiveness of the proposed methods in estimating the joint space torques generated by the external forces applied to the WallMoBot end-effector and to distinguish between the user-input force and accidental collisions.

scholarly journals Task-space regulation of rigid-link electrically-driven robots with uncertain kinematics using neural networks

2021 ◽  
Vol 54 (1-2) ◽  
pp. 102-115
Author(s):  
Wenhui Si ◽  
Lingyan Zhao ◽  
Jianping Wei ◽  
Zhiguang Guan
Keyword(s):  
Neural Networks ◽  
Asymptotic Stability ◽  
Control Method ◽  
Joint Space ◽  
Robot Kinematics ◽  
Rbf Neural Networks ◽  
Task Space ◽  
Actuator Dynamics ◽  
Rigid Link ◽  
On Line

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

Cooperation and Null-Space Control of Networked Omni-Directional Mobile Manipulators

2020 ◽  
Author(s):  
Michael John Chua ◽  
Yen-Chen Liu
Keyword(s):  
Degrees Of Freedom ◽  
Null Space ◽  
Kinematic Model ◽  
Mobile Manipulator ◽  
Main Task ◽  
Mobile Manipulators ◽  
Robot Arm ◽  
Task Space ◽  
End Effector ◽  
Mobile Base

Abstract This paper presents cooperation and null-space control for networked mobile manipulators with high degrees of freedom (DOFs). First, kinematic model and Euler-Lagrange dynamic model of the mobile manipulator, which has an articulated robot arm mounted on a mobile base with omni-directional wheels, have been presented. Then, the dynamic decoupling has been considered so that the task-space and the null-space can be controlled separately to accomplish different missions. The motion of the end-effector is controlled in the task-space, and the force control is implemented to make sure the cooperation of the mobile manipulators, as well as the transportation tasks. Also, the null-space control for the manipulator has been combined into the decoupling control. For the mobile base, it is controlled in the null-space to track the velocity of the end-effector, avoid other agents, avoid the obstacles, and move in a defined range based on the length of the manipulator without affecting the main task. Numerical simulations have been addressed to demonstrate the proposed methods.

New repetitive motion planning scheme with cube end-effector planning precision for redundant robotic manipulators

Robotica ◽  
2021 ◽  
pp. 1-22
Author(s):  
Limin Shen ◽  
Yuanmei Wen
Keyword(s):  
Theoretical Analysis ◽  
Motion Planning ◽  
Robotic Manipulators ◽  
Robotic Manipulator ◽  
Repetitive Motion ◽  
End Effector ◽  
Difference Formula ◽  
Planning Scheme ◽  
Simulation Results ◽  
The Difference

Abstract Repetitive motion planning (RMP) is important in operating redundant robotic manipulators. In this paper, a new RMP scheme that is based on the pseudoinverse formulation is proposed for redundant robotic manipulators. Such a scheme is derived from the discretization of an existing RMP scheme by utilizing the difference formula. Then, theoretical analysis and results are presented to show the characteristic of the proposed RMP scheme. That is, this scheme possesses the characteristic of cube pattern in the end-effector planning precision. The proposed RMP scheme is further extended and studied for redundant robotic manipulators under joint constraint. Based on a four-link robotic manipulator, simulation results substantiate the effectiveness and superiority of the proposed RMP scheme and its extended one.

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