Robust Force Control of a Robot Manipulator

1990 ◽  
Author(s):  
D. M. Dawson ◽  
F. L. Lewis
Keyword(s):  
Force Control ◽  
Robot Manipulator

scholarly journals Force control design for a robot manipulator attached to a UAV

2018 ◽  
Vol 51 (30) ◽  
pp. 548-553 ◽  
Author(s):  
Konstantinos Gkountas ◽  
Dimitris Chaikalis ◽  
Anthony Tzes
Keyword(s):  
Force Control ◽  
Robot Manipulator ◽  
Control Design

On the Problem of Constrained Path Following For Manipulators

1988 ◽  
Vol 110 (4) ◽  
pp. 443-448
Author(s):  
A. Sankaranarayanan ◽  
M. Vidyasagar
Keyword(s):  
Collision Avoidance ◽  
Force Control ◽  
Robot Manipulator ◽  
Sufficient Conditions ◽  
Path Following ◽  
Necessary And Sufficient Conditions ◽  
End Effector ◽  
Circular Contour ◽  
Necessary And Sufficient

Force Control involves moving the end-effector of a robot manipulator on the surface of an object while ensuring that no other part of the manipulator collides with the object. Suppose C is a given contour to be followed. If the end-effector can move between two points a and b on C while meeting the collision avoidance requirement, we can say that a path exists between a and b. We begin by considering a planar manipulator and a circular contour and derive the necessary and sufficient conditions for a path to exist between a pair of points. By extending these ideas, sufficient conditions are derived for a noncircular contour. The advantages of a (kinematically redundant) 3-link planar manipulator over a 2-link manipulator are pointed out. Finally, we consider spatial manipulators and derive the necessary and sufficient conditions for the case where the contour lies on the surface of a sphere.

Force Control of Robot Manipulator by Neural Network Model - Experiment and Evaluation of One-Degree-of-Freedom Manipulator -

1990 ◽  
Vol 2 (4) ◽  
pp. 273-281 ◽  
Author(s):  
Masatoshi Tokita ◽  
◽  
Toyokazu Mitsuoka ◽  
Toshio Fukuda ◽  
Takashi Kurihara ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Force Control ◽  
Pid Controller ◽  
Robot Manipulator ◽  
Force Sensor ◽  
Robotic Manipulator ◽  
The Neural Network ◽  
The Stability

In this paper, a force control of a robotic manipulator based on a neural network model is proposed with consideration of the dynamics of both the force sensor and objects. This proposed system consists of the standard PID controller, the gains of which are augmented and adjusted depending on objects through a process of learning. The authors proposed a similar method previously for the force control of the robotic manipulator with consideration of dynamics of objects, but without consideration of dynamics of the force sensor, showing only simulation results. This paper shows the similar structure of the controller via the neural network model applicable to the cases with consideration of both effects and demonstrates that the proposed method shows the better performance than the conventional PID type of controller, yielding to the wider range of applications, consequently. Therefore, this method can be applied to the force/compliance control problems. The effects of the number of neurons and hidden layers of the neural network model are also discussed through the simulation and experimental results as well as the stability of the control system.

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