Real-time dynamic control of an industrial manipulator using a neural network-based learning controller

1990 ◽  
Vol 6 (1) ◽  
pp. 1-9 ◽  
Author(s):  
W.T. Miller ◽  
R.P. Hewes ◽  
F.H. Glanz ◽  
L.G. Kraft
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Dynamic Control ◽  
Time Dynamic ◽  
Industrial Manipulator

Theory of Temperature Dynamic Control in Arch Dams and its Application

2012 ◽  
Vol 594-597 ◽  
pp. 738-741 ◽  
Author(s):  
Yin Duan ◽  
Xing Hong Liu ◽  
Xiao Lin Chang
Keyword(s):  
Real Time ◽  
Temperature Control ◽  
Control Method ◽  
Dynamic Control ◽  
Arch Dam ◽  
Real Time Control ◽  
New Techniques ◽  
Time Dynamic ◽  
Arch Dams ◽  
Time Control

Main factors of the temperature control and crack prevention in arch dams are summarized. The Space-time Dynamic Control method in pipe cooling process and the Temperature Real-time Control and Decision Database System are introduced to help for temperature real-time control and rapid analysis. Successful application of these new techniques in the construction of Dagangshan arch dam indicates that the proposed method are of significant effectiveness on the temperature control and crack prevention, and have good application prospect in practical project.

Real-Time Control Using Explicit Dynamic Solutions of a Two-Motion-Modes Mobile Robot

1994 ◽  
Vol 208 (3) ◽  
pp. 157-167 ◽  
Author(s):  
Y Y Cha ◽  
D G Gweon
Keyword(s):  
Mobile Robot ◽  
Real Time ◽  
Dynamic Control ◽  
Robot Dynamics ◽  
Real Time Control ◽  
Inverse Dynamic ◽  
Time Dynamic ◽  
Time Control ◽  
Motion Modes ◽  
Velocity Motion

In this study a two-motion-modes mobile robot is developed. The motion of the mobile robot is controlled by three d.c. servo-motors, two of which drive two wheels independently and one of which steers the wheels simultaneously. The two motion modes of the mobile robot, different velocity motion (DVM) and equal velocity motion (EVM), are analysed. Kinematic and dynamic analyses of the two motion modes are performed. For the implementation of real-time control considering mobile robot dynamics, the forward and inverse dynamic solutions are derived explicitly. Through a simulation, the path-tracking and control performance of the mobile robot considering dynamics is compared with the considering kinetics only, and the possibility of real-time dynamic control is proved.

NEURAL NETWORK BASED DYNAMIC CONTROLLERS FOR INDUSTRIAL ROBOTS

1995 ◽  
Vol 06 (03) ◽  
pp. 257-271
Author(s):  
SE-YOUNG OH ◽  
WEON-CHANG SHIN ◽  
HYO-GYU KIM
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Industrial Robot ◽  
Industrial Robots ◽  
System Stability ◽  
Robot Dynamics ◽  
Time Dynamic ◽  
Dynamic Controller ◽  
And Control

The industrial robot’s dynamic performance is frequently measured by positioning accuracy at high speeds and a good dynamic controller is essential that can accurately compute robot dynamics at a servo rate high enough to ensure system stability. A real-time dynamic controller for an industrial robot is developed here using neural networks. First, an efficient time-selectable hidden layer architecture has been developed based on system dynamics localized in time, which lends itself to real-time learning and control along with enhanced mapping accuracy. Second, the neural network architecture has also been specially tuned to accommodate servo dynamics. This not only facilitates the system design through reduced sensing requirements for the controller but also enhances the control performance over the control architecture neglecting servo dynamics. Experimental results demonstrate the controller’s excellent learning and control performances compared with a conventional controller and thus has good potential for practical use in industrial robots.

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