A Self-learning Nonlinear Variable Gain Proportional Derivative (PD) Controller in Robot Manipulators
This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller that exhibits self-constructing and self-learning capabilities. In this method, the conventional linear PD controller is augmented with a nonlinear variable PD gain control signal using a dynamic structural netw...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Universiti Putra Malaysia Press
2004
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| Online Access: | http://psasir.upm.edu.my/id/eprint/3621/1/A_Self-learning_Nonlinear_Variable_Gain_Proportional.pdf http://psasir.upm.edu.my/id/eprint/3621/ |
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| Summary: | This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller
that exhibits self-constructing and self-learning capabilities. In this method, the
conventional linear PD controller is augmented with a nonlinear variable PD gain control
signal using a dynamic structural network. The dynamic structural network known as
Growing Multi-Experts etwork grows in time by placing hidden nodes in regions of the
state space visited by the system during operation. This results in a network that is
"economic" in terms of network sileo The proposed approach enhances the adaptability
of conventional PD controller while preserving its' linear structure. Based on the
simulation study on variable load and friction compensation, the fast adaptation is shown
to be able to compensate the non-linearity and the uncertainty in the robotic system. |
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