Adaptive visual servoing scheme free of image velocity measurement for uncertain robot manipulators

Automatica ◽  
2013 ◽  
Vol 49 (5) ◽  
pp. 1304-1309 ◽  
Author(s):  
Fernando Lizarralde ◽  
Antonio C. Leite ◽  
Liu Hsu ◽  
Ramon R. Costa
Keyword(s):  
Velocity Measurement ◽  
Visual Servoing ◽  
Robot Manipulators ◽  
Image Velocity ◽  
Uncertain Robot

Adaptive Visual Servoing of Robot Manipulators without Measuring the Image Velocity

2008 ◽  
Vol 41 (2) ◽  
pp. 4108-4113
Author(s):  
Fernando Lizarralde ◽  
Liu Hsu ◽  
Ramon R. Costa
Keyword(s):  
Visual Servoing ◽  
Robot Manipulators ◽  
Image Velocity

Adaptive 3D Visual Servoing without Image Velocity Measurement for Uncertain Manipulators

2011 ◽  
Vol 44 (1) ◽  
pp. 14584-14589 ◽  
Author(s):  
Antonio C. Leite ◽  
Alessandro R.L. Zachi ◽  
Fernando Lizarralde ◽  
Liu Hsu
Keyword(s):  
Velocity Measurement ◽  
Visual Servoing ◽  
Image Velocity

scholarly journals Fault Diagnosis and Fault-Tolerant Control of Uncertain Robot Manipulators Using High-Order Sliding Mode

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Mien Van ◽  
Pasquale Franciosa ◽  
Dariusz Ceglarek
Keyword(s):  
Fault Diagnosis ◽  
Velocity Measurement ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Fault Tolerant Control ◽  
High Order ◽  
Component Faults ◽  
Uncertain Robot

A robust fault diagnosis and fault-tolerant control (FTC) system for uncertain robot manipulators without joint velocity measurement is presented. The actuator faults and robot manipulator component faults are considered. The proposed scheme is designed via an active fault-tolerant control strategy by combining a fault diagnosis scheme based on a super-twisting third-order sliding mode (STW-TOSM) observer with a robust super-twisting second-order sliding mode (STW-SOSM) controller. Compared to the existing FTC methods, the proposed FTC method can accommodate not only faults but also uncertainties, and it does not require a velocity measurement. In addition, because the proposed scheme is designed based on the high-order sliding mode (HOSM) observer/controller strategy, it exhibits fast convergence, high accuracy, and less chattering. Finally, computer simulation results for a PUMA560 robot are obtained to verify the effectiveness of the proposed strategy.

Consensus in networks of uncertain robot manipulators without using neighbors’ velocity information

Robotica ◽  
2021 ◽  
pp. 1-17
Author(s):  
Seyed Mostafa Almodarresi ◽  
Marzieh Kamali ◽  
Farid Sheikholeslam
Keyword(s):  
Information Exchange ◽  
Lyapunov Functions ◽  
Robot Manipulators ◽  
Adaptive Methods ◽  
Convergence Time ◽  
Parameter Search ◽  
Velocity Information ◽  
Simulation Results ◽  
Uncertain Robot ◽  
Asymptotic Synchronization

Abstract In this paper, new distributed adaptive methods are proposed for solving both leaderless and leader–follower consensus problems in networks of uncertain robot manipulators, by estimating only the gravitational torque forces. Comparing with the existing adaptive methods, which require the estimation of the whole dynamics, presented methods reduce the excitation levels required for efficient parameter search, the convergence time, and the complexity of the regressor. Additionally, proposed schemes eliminate the need for velocity information exchange between the agents. Global asymptotic synchronization is shown by introducing new Lyapunov functions. Simulation results are provided for a network of 10 4-DOF robot manipulators.

Adaptive neural controller for visual servoing of robot manipulators with camera-in-hand configuration

2012 ◽  
Vol 26 (8) ◽  
pp. 2313-2323 ◽  
Author(s):  
Jungmin Kim ◽  
Naveen Kumar ◽  
Vikas Panwar ◽  
Jin-Hwan Borm ◽  
Jangbom Chai
Keyword(s):  
Visual Servoing ◽  
Robot Manipulators ◽  
Neural Controller ◽  
Adaptive Neural Controller

scholarly journals Adaptive Neural Output Feedback Control for Uncertain Robot Manipulators with Input Saturation

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Rong Mei ◽  
ChengJiang Yu
Keyword(s):  
Feedback Control ◽  
Output Feedback ◽  
Disturbance Observer ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Input Saturation ◽  
Output Feedback Control ◽  
System State ◽  
Control Scheme ◽  
Uncertain Robot

This paper presents an adaptive neural output feedback control scheme for uncertain robot manipulators with input saturation using the radial basis function neural network (RBFNN) and disturbance observer. First, the RBFNN is used to approximate the system uncertainty, and the unknown approximation error of the RBFNN and the time-varying unknown external disturbance of robot manipulators are integrated as a compounded disturbance. Then, the state observer and the disturbance observer are proposed to estimate the unmeasured system state and the unknown compounded disturbance based on RBFNN. At the same time, the adaptation technique is employed to tackle the control input saturation problem. Utilizing the estimate outputs of the RBFNN, the state observer, and the disturbance observer, the adaptive neural output feedback control scheme is developed for robot manipulators using the backstepping technique. The convergence of all closed-loop signals is rigorously proved via Lyapunov analysis and the asymptotically convergent tracking error is obtained under the integrated effect of the system uncertainty, the unmeasured system state, the unknown external disturbance, and the input saturation. Finally, numerical simulation results are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for uncertain robot manipulators.

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