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Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process
by
Koustoumpardis, Panagiotis N.
, Aspragathos, Nikos A.
in
Computer simulation
/ Control systems
/ Control systems design
/ Controllers
/ End effectors
/ Fabrics
/ Fuzzy logic
/ Materials handling
/ Neural networks
/ Robot control
/ Robot learning
/ Robots
/ Robust control
/ Sewing
/ Studies
2003
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Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process
by
Koustoumpardis, Panagiotis N.
, Aspragathos, Nikos A.
in
Computer simulation
/ Control systems
/ Control systems design
/ Controllers
/ End effectors
/ Fabrics
/ Fuzzy logic
/ Materials handling
/ Neural networks
/ Robot control
/ Robot learning
/ Robots
/ Robust control
/ Sewing
/ Studies
2003
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While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process
by
Koustoumpardis, Panagiotis N.
, Aspragathos, Nikos A.
in
Computer simulation
/ Control systems
/ Control systems design
/ Controllers
/ End effectors
/ Fabrics
/ Fuzzy logic
/ Materials handling
/ Neural networks
/ Robot control
/ Robot learning
/ Robots
/ Robust control
/ Sewing
/ Studies
2003
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Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process
Journal Article
Fuzzy Logic Decision Mechanism Combined with a Neuro-Controller for Fabric Tension in Robotized Sewing Process
2003
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Overview
A new approach for flexible automated handling of fabrics in the sewing process is described, which focuses to control the cloth tension applied by a robot. The proposed hierarchical robot control system includes a Fuzzy decision mechanism combined with a Neuro-controller. The expert's actions during the sewing process are investigated and this human behavior is interpreted in order to design the controller. The Fuzzy Logic decision mechanism utilizes only qualitative knowledge concerning the properties of the fabrics, in order to determine the desired tensional force and the location of the robot hand on the fabric. A Neural Network controller regulates the fabric tension to achieve the desired value by determining the robot end effector velocity. The simulation results demonstrate the efficiency of the system as well as the robustness of the controller performance since the effects of the noise are negligible. The system capabilities are more evident when the controller uses its previously acquired “experience”.
Publisher
Springer Nature B.V
Subject
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