Adaptive Neural Impedance Control of a Robotic Manipulator With Input Saturation

2016 ◽  
Vol 46 (3) ◽  
pp. 334-344 ◽  
Author(s):  
Wei He ◽  
Yiting Dong ◽  
Changyin Sun
Keyword(s):  
Impedance Control ◽  
Input Saturation ◽  
Robotic Manipulator

Simultaneous Learning of Robot Impedance Parameters Using Neural Networks

2007 ◽  
Vol 19 (1) ◽  
pp. 106-113
Author(s):  
Mutsuhiro Terauchi ◽  
◽  
Yoshiyuki Tanaka ◽  
Seishiro Sakaguchi ◽  
Nan Bu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Simulations ◽  
Impedance Control ◽  
Robotic Manipulator ◽  
Effective Control ◽  
Control Methods ◽  
Learning Method ◽  
Conventional Methods ◽  
Impedance Parameters ◽  
Virtual Impedance

Impedance control is one of the most effective control methods for interaction between a robotic manipulator and its environment. Robot impedance control regulates the response of the manipulator to contact and virtual impedance control regulates the manipulator's response before contact. Although these impedance parameters may be regulated using neural networks, conventional methods do not consider regulating robot impedance and virtual impedance simultaneously. This paper proposes a simultaneous learning method to regulate the impedance parameters using neural networks. The validity of the proposed method is demonstrated in computer simulations of tasks by a multi-joint robotic manipulator.

scholarly journals A convex optimization based solution for the robotic manipulator control design problem subject to input saturation

2020 ◽  
Vol 53 (2) ◽  
pp. 5467-5472
Author(s):  
Eduardo S. Saraiva ◽  
Rafael S. Castro ◽  
Aurélio T. Salton ◽  
Guilherme A. Pimentel
Keyword(s):  
Convex Optimization ◽  
Design Problem ◽  
Input Saturation ◽  
Control Design ◽  
Robotic Manipulator ◽  
Manipulator Control ◽  
Robotic Manipulator Control
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