scholarly journals Hybrid control of robotic manipulator by neural network model. 3rd rtport. Control of 2 D.O.F. manipulators by neural network using fuzzy set theory.

1991 ◽  
Vol 57 (535) ◽  
pp. 874-881
Author(s):  
Toshio FUKUDA ◽  
Takanori SHIBATA ◽  
Masatoshi TOKITA ◽  
Toyokazu MITSUOKA
Keyword(s):  
Neural Network ◽  
Set Theory ◽  
Network Model ◽  
Neural Network Model ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Hybrid Control ◽  
Robotic Manipulator

scholarly journals Hybrid Control of Robotic Manipulator by Neural Network Model. 4th Report, Sensing and Hybrid Control with Collision Phenomena.

1991 ◽  
Vol 57 (539) ◽  
pp. 2305-2312 ◽  
Author(s):  
Takanori SHIBATA ◽  
Toshio FUKUDA ◽  
Fumito ARAI ◽  
Hiroshi WADA ◽  
Masatoshi TOKITA ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Hybrid Control ◽  
Robotic Manipulator

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.

Spectral Parameters of Straightness Deviation Evaluation

2016 ◽  
Vol 876 ◽  
pp. 74-79
Author(s):  
Alexander Vladimirovich Glubokov ◽  
Svetlana Vladimirovna Glubokova ◽  
Alexey Vileninovich Shulepov ◽  
Sergey Evgenievich Ped
Keyword(s):  
Neural Network ◽  
Spectral Analysis ◽  
Set Theory ◽  
Fuzzy Set ◽  
Fuzzy Set Theory ◽  
Automated System ◽  
Spectral Parameters ◽  
Deviation Measurement

Spectral analysis of different profiles obtained during straightness deviation measurement was performed. The several profiles are showed, for which the value of straightness deviation is the same, but its behavior differs greatly. Spectral parameters characterizing the type of straightness deviation are proposed. The automated system based on factors of fuzzy-set theory with implementation in the form of neural network is developed.

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