scholarly journals Robust Region Tracking Control for a Robot Manipulator

2021 ◽  
Author(s):  
xiangyun Li ◽  
QI LU ◽  
Zhaoyang Chen ◽  
Qinlin Yang ◽  
Kang Li
Keyword(s):  
Trajectory Tracking ◽  
Model Uncertainty ◽  
Tracking Control ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Human Robot Interaction ◽  
Moving Target ◽  
Robot Interaction ◽  
Target Region ◽  
Region Tracking

n this work, the uncertainty and disturbance estimator (UDE)-based robust region reaching controller for a robot manipulator is developed to achieve the moving target region trajectory tracking and complaint human robot interaction inside the target region. The regional feedback error is derived from the potential function to drive the robot manipulator end effector converging into the target region. Under the back-stepping control framework, the UDE is fused into the region tracking control law to estimate and compensate the model uncertainty and external disturbance. Both simulation and experimental studies are carried out with a universal robots (UR) 10 manipulator to demonstrate the effectiveness of the proposed method for moving target trajectory tracking, model uncertainty and external disturbance rejection, and compliant human robot interaction within the target region.

scholarly journals Robust Region Tracking Control for a Robot Manipulator

2021 ◽  
Author(s):  
xiangyun Li ◽  
QI LU ◽  
Zhaoyang Chen ◽  
Qinlin Yang ◽  
Kang Li
Keyword(s):  
Trajectory Tracking ◽  
Model Uncertainty ◽  
Tracking Control ◽  
Robot Manipulator ◽  
External Disturbance ◽  
Human Robot Interaction ◽  
Moving Target ◽  
Robot Interaction ◽  
Target Region ◽  
Region Tracking

n this work, the uncertainty and disturbance estimator (UDE)-based robust region reaching controller for a robot manipulator is developed to achieve the moving target region trajectory tracking and complaint human robot interaction inside the target region. The regional feedback error is derived from the potential function to drive the robot manipulator end effector converging into the target region. Under the back-stepping control framework, the UDE is fused into the region tracking control law to estimate and compensate the model uncertainty and external disturbance. Both simulation and experimental studies are carried out with a universal robots (UR) 10 manipulator to demonstrate the effectiveness of the proposed method for moving target trajectory tracking, model uncertainty and external disturbance rejection, and compliant human robot interaction within the target region.

scholarly journals A Robust Region Control Approach for Simultaneous Trajectory Tracking and Physical Human-Robot Interaction

2021 ◽  
Author(s):  
Xiangyun Li ◽  
QI LU ◽  
Jiali Chen ◽  
Kang Li
Keyword(s):  
Trajectory Tracking ◽  
Human Subject ◽  
Robot Manipulator ◽  
Coupling Effect ◽  
Experimental Studies ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Target Region ◽  
Control Approach ◽  
Region Tracking

In this work, the uncertainty and disturbance estimator (UDE)-based robust region tracking controller for a robot manipulator is developed to achieve the moving target region trajectory tracking and the compliant human-robot interaction simultaneously. Utilizing the back-stepping control approach, the UDE is seamlessly fused into the region tracking control framework to estimate and compensate the model uncertainty and external disturbance, such as unknown payload, unmodeled joint coupling effect and friction. The regional feedback error is derived from the potential function to drive the robot manipulator end-effector converging into the target region, where the robot manipulator can be passively manipulated based on the needs of human to achieve the compliant physical human-robot interaction. Extensive experimental studies are carried out with a universal robots 10 manipulator to validate the effectiveness of the proposed method for moving region trajectory tracking, handling unknown payload and compliant physical human-robot interaction. The superior robustness of the proposed approach is demonstrated by comparison with the existing controller under the adverse effect of unknown payload. The humanrobot interaction is achieved in a shared autonomy manner with the cooperation of the manipulator and the human subject to accomplish the temperature measurement task, where the variation in human-subject height and the complexity of aiming the thermometer are successfully accommodated.

scholarly journals Modified Linear Active Disturbance Rejection Control for Uncertain Robot Manipulator Trajectory Tracking

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hongjun Hu ◽  
Shungen Xiao ◽  
Haikuo Shen
Keyword(s):  
Trajectory Tracking ◽  
Disturbance Rejection ◽  
Tracking Control ◽  
Feedforward Control ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
External Disturbance ◽  
Angular Acceleration ◽  
Trajectory Tracking Control ◽  
Active Disturbance Rejection

To solve the problems of model uncertainties, dynamic coupling, and external disturbances, a modified linear active disturbance rejection controller (MLADRC) is proposed for the trajectory tracking control of robot manipulators. In the computer simulation, MLADRC is compared to the proportional-derivative (PD) controller and the regular linear active disturbance rejection controller (LADRC) for performance tests. Multiple uncertain factors such as friction, parameter perturbation, and external disturbance are sequentially added to the system to simulate an actual robot manipulator system. Besides, a two-degree-of-freedom (2-DOF) manipulator is constructed to verify the control performance of the MLADRC. Compared with the regular LADRC, MLADRC is significantly characterized by the addition of feedforward control of reference angular acceleration, which helps robot manipulators keep up with target trajectories more accurately. The simulation and experimental results demonstrate the superiority of the MLADRC over the regular LADRC for the trajectory tracking control.

Soft magnetic skin for super-resolution tactile sensing with force self-decoupling

2021 ◽  
Vol 6 (51) ◽  
pp. eabc8801
Author(s):  
Youcan Yan ◽  
Zhe Hu ◽  
Zhengbao Yang ◽  
Wenzhen Yuan ◽  
Chaoyang Song ◽  
...  
Keyword(s):  
External Disturbance ◽  
Super Resolution ◽  
Tactile Sensor ◽  
Human Robot Interaction ◽  
Tactile Sensors ◽  
Dexterous Manipulation ◽  
Robot Interaction ◽  
Flexible Film ◽  
Challenging Tasks ◽  
External Forces

Human skin can sense subtle changes of both normal and shear forces (i.e., self-decoupled) and perceive stimuli with finer resolution than the average spacing between mechanoreceptors (i.e., super-resolved). By contrast, existing tactile sensors for robotic applications are inferior, lacking accurate force decoupling and proper spatial resolution at the same time. Here, we present a soft tactile sensor with self-decoupling and super-resolution abilities by designing a sinusoidally magnetized flexible film (with the thickness ~0.5 millimeters), whose deformation can be detected by a Hall sensor according to the change of magnetic flux densities under external forces. The sensor can accurately measure the normal force and the shear force (demonstrated in one dimension) with a single unit and achieve a 60-fold super-resolved accuracy enhanced by deep learning. By mounting our sensor at the fingertip of a robotic gripper, we show that robots can accomplish challenging tasks such as stably grasping fragile objects under external disturbance and threading a needle via teleoperation. This research provides new insight into tactile sensor design and could be beneficial to various applications in robotics field, such as adaptive grasping, dexterous manipulation, and human-robot interaction.

Trajectory tracking control of robot manipulators using a neural-network-based torque compensator

1998 ◽  
Vol 212 (5) ◽  
pp. 361-372 ◽  
Author(s):  
Q Li ◽  
S K Tso ◽  
W J Zhang
Keyword(s):  
Neural Network ◽  
Trajectory Tracking ◽  
Tracking Control ◽  
High Performance ◽  
Robot Manipulator ◽  
Robot Manipulators ◽  
Lyapunov Stability Theory ◽  
System Stability ◽  
Trajectory Tracking Control ◽  
Modelling Uncertainties

In this paper, an adaptive neural-network-based torque compensator is developed for the trajectory-tracking control of robot manipulators. The overall control structure employs a classical non-linear decoupling controller for actuating torque computation based on an approximated robot dynamic model. To suppress the effects of uncertainties associated with the estimated model, a supplementary neural network algorithm is developed to generate compensation torques. The weight adaptation rule for this neuro-compensator is derived on the basis of the Lyapunov stability theory. Both global system stability and the error convergence can then be guaranteed. Simulation studies on a two-link robot manipulator demonstrate that high performance of the proposed control algorithm could be achieved under severe modelling uncertainties.

Robust Trajectory Tracking Control of Autonomous Quad-Rotor UAV Based on Double Loop Frame

2011 ◽  
Vol 467-469 ◽  
pp. 1421-1426
Author(s):  
Zhi Cheng Hou ◽  
X. Gong ◽  
Y. Bai ◽  
Y.T. Tian ◽  
Q. Sun
Keyword(s):  
Trajectory Tracking ◽  
Tracking Control ◽  
Control Method ◽  
External Disturbance ◽  
Double Loop ◽  
Trajectory Tracking Control ◽  
Stability Augmentation ◽  
Aerial Vehicle ◽  
Simulation Results ◽  
Control Quantity

This paper deals with the under-actuated characteristic of a quad-rotor unmanned aerial vehicle (UAV). By designing the double loop configuration, the autonomous trajectory tracking is realized. The model uncertainty, external disturbance and the senor noise are also taken into consideration. Then the controller is put forward in the inner loop. An optimal stability augmentation control (SAC) method is used to stabilize the horizon position and keep it away from oscillation. By calculating the nonlinear decouple map, control quantity is converted to the speeds of the four rotors. At last some simulation results and the prototype implementation prove that the control method is effective.

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