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 Trajectory Tracking Control for Uncertain Robot Manipulators with Repetitive Motions in Task Space

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Jiutai Liu ◽  
Xiucheng Dong ◽  
Yong Yang ◽  
Hongyu Chen
Keyword(s):  
Trajectory Tracking ◽  
Robot Manipulators ◽  
Tracking Error ◽  
Task Space ◽  
Learning Gain ◽  
Trajectory Tracking Control ◽  
Adaptive Iterative Learning Control ◽  
Tracking Tasks ◽  
Control Schemes ◽  
Composite Energy

This paper aims at the trajectory tracking problem of robot manipulators performing repetitive tasks in task space. Two control schemes are presented to conduct trajectory tracking tasks under uncertain conditions including unmodeled dynamics of robot and additional disturbances. The first controller, pure adaptive iterative learning control (AILC), is based upon the use of a proportional-derivative-like (PD-like) feedback structure, and its design seems very simple in the sense that the only requirement on the learning gain and control parameters is the positive definiteness condition. The second controller is designed with a combination of AILC and neural networks (NNs) where the AILC is adopted to learn the periodic uncertainties that attribute to the repetitive motion of robot manipulators while the add-on NNs are used to approximate and compensate all nonperiodic ones. Moreover, a combined error factor (CEF), which is composed of the weighted sum of tracking error and its derivative, is designed for network updating law to improve the learning speed as well as tracking accuracy of the system. Stabilities of the controllers and convergence are proved rigorously by a Lyapunov-like composite energy function. The simulations performed on two-link manipulator are provided to verify the effectiveness of the proposed controllers. The results of compared simulations illustrate that our proposed control schemes can significantly conduct trajectory tracking tasks.

scholarly journals Robust Task Space Trajectory Tracking Control of Robotic Manipulators

2016 ◽  
Vol 21 (3) ◽  
pp. 547-568 ◽  
Author(s):  
M. Galicki
Keyword(s):  
Trajectory Tracking ◽  
Lyapunov Stability Theory ◽  
Task Space ◽  
Redundant Manipulator ◽  
Trajectory Tracking Control ◽  
Finite Time Convergence ◽  
Robot Trajectory ◽  
Vector Variable ◽  
Control Schemes ◽  
Chattering Free

Abstract This work deals with the problem of the accurate task space trajectory tracking subject to finite-time convergence. Kinematic and dynamic equations of a redundant manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the manipulator when tracking the trajectory by the end-effector. Furthermore, the movement is to be accomplished in such a way as to reduce both the manipulator torques and their oscillations thus eliminating the potential robot vibrations. Based on suitably defined task space non-singular terminal sliding vector variable and the Lyapunov stability theory, we propose a class of chattering-free robust controllers, based on the estimation of transpose Jacobian, which seem to be effective in counteracting both uncertain kinematics and dynamics, unbounded disturbances and (possible) kinematic and/or algorithmic singularities met on the robot trajectory. The numerical simulations carried out for a redundant manipulator of a SCARA type consisting of the three revolute kinematic pairs and operating in a two-dimensional task space, illustrate performance of the proposed controllers as well as comparisons with other well known control schemes.

Comparison of PD-Based Controllers for Robotic Manipulators

2004 ◽  
Author(s):  
P. R. Ouyang ◽  
W. J. Zhang
Keyword(s):  
Trajectory Tracking ◽  
Simple Structure ◽  
Feedforward Control ◽  
Robotic Manipulators ◽  
Learning Control ◽  
Tracking Performance ◽  
Pd Control ◽  
Tracking Tasks ◽  
Control Schemes ◽  
Previous Iteration

PD control is widely used in industrial robotic manipulators because of its simple structure and acceptable performance. In this paper, the PD-based control schemes for the trajectory tracking of the robotic manipulators are addressed. The fixed gain PD control, the nonlinear gain PD (NPD) control, the adaptive PD learning control (PD-LC), and the adaptive NPD learning control (NPD-LC) are applied for the trajectory tracking of both serial and parallel robotic manipulators. The PD-LC and NPD-LC controllers can be used to improve the tracking performance for the repeatable tracking tasks in an iterative mode. The PD-LC and NPD-LC consists of a PD/NPD control as the basic feedback control and an additional feedforward control term directly inherited from the previous iteration of the same control task. A comparative study of four PD-based controllers is conducted to understand how different control schemes will affect the trajectory tracking performance, and the results are shown in this paper. Case studies are presented to demonstrate the validity of the PD-LC and NPD-LC algorithms.

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.

Controlled Synchronization of Mechanical Systems

2008 ◽  
Author(s):  
Nikhil Chopra ◽  
YenChen Liu
Keyword(s):  
Trajectory Tracking ◽  
Time Delays ◽  
Control Algorithm ◽  
Mechanical Systems ◽  
Tracking Algorithm ◽  
Numerical Example ◽  
Output Synchronization

In this paper we study the problem of synchronization and trajectory tracking in mechanical systems. Exploiting output synchronization results developed previously, a control algorithm is developed to guarantee output synchronization in addition to trajectory tracking in mechanical systems. The classical Slotine-Li adaptive trajectory tracking algorithm is modified to synchronize mechanical systems following a common trajectory. The robustness of the proposed scheme to time delays in communication is also discussed. A numerical example is presented to verify the efficacy of the proposed results.

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.

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