Neural Network Control of a Two-Link Flexible Robotic Manipulator Using Assumed Mode Method

In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory trackin...

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Bibliographic Details
Published in:IEEE transactions on industrial informatics 2019-02, Vol.15 (2), p.755-765
Main Authors: Gao, Hejia, He, Wei, Zhou, Chen, Sun, Changyin
Format: Article
Language:English
Subjects:
Artificial neural networks
Assumed mode method (AMM)
Computational modeling
Computer simulation
Control stability
Control systems
Discretization
dynamic modeling
Dynamic models
Feedback control
Flexible manipulators
flexible robotic manipulator
flexible structure
Manipulator dynamics
Mathematical model
Network control
Neural networks
neural networks (NN)
Output feedback
Robot arms
State feedback
two-link
Vibration control
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Summary:In this paper, the n-dimensional discretized model of the two-link flexible manipulator is developed by the assumed mode method (AMM). Subsequently, based on the discretized dynamic model, both full-state feedback control and output feedback control are investigated to achieve the trajectory tracking and vibration suppression. In order to guarantee the stability strictly, uniform ultimate boundedness (UUB) of the closed-loop system is realized by the Lyapunov's stability. Furthermore, through appropriately choosing control parameters, the states of the system will converge to zero within a small neighborhood. Eventually, extensive simulations and experiments on the Quanser platform for a two-link robotic manipulator are carried out to demonstrate the feasibility of the proposed neural network controller.
ISSN:1551-3203
1941-0050
DOI:10.1109/TII.2018.2818120