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Neural-Learning-Based Control for a Constrained Robotic Manipulator With Flexible Joints.
- Source :
-
IEEE Transactions on Neural Networks & Learning Systems . Dec2018, Vol. 29 Issue 12, p5993-6003. 11p. - Publication Year :
- 2018
-
Abstract
- Nowadays, the control technology of the robotic manipulator with flexible joints (RMFJ) is not mature enough. The flexible-joint manipulator dynamic system possesses many uncertainties, which brings a great challenge to the controller design. This paper is motivated by this problem. In order to deal with this and enhance the system robustness, the full-state feedback neural network (NN) control is proposed. Moreover, output constraints of the RMFJ are achieved, which improve the security of the robot. Through the Lyapunov stability analysis, we identify that the proposed controller can guarantee not only the stability of flexible-joint manipulator system but also the boundedness of system state variables by choosing appropriate control gains. Then, we make some necessary simulation experiments to verify the rationality of our controllers. Finally, a series of control experiments are conducted on the Baxter. By comparing with the proportional–derivative control and the NN control with the rigid manipulator model, the feasibility and the effectiveness of NN control based on flexible-joint manipulator model are verified. [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL neural networks
*ARTIFICIAL intelligence
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 2162237X
- Volume :
- 29
- Issue :
- 12
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Neural Networks & Learning Systems
- Publication Type :
- Periodical
- Accession number :
- 133211370
- Full Text :
- https://doi.org/10.1109/TNNLS.2018.2803167