Neural Network Model-Based Control for Manipulator: An Autoencoder Perspective

Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the perfor...

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Veröffentlicht in:IEEE transaction on neural networks and learning systems Jg. 34; H. 6; S. 2854 - 2868
Hauptverfasser: Li, Zhan, Li, Shuai
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States IEEE 01.06.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Autoencoder
Computational modeling
Computer architecture
Control systems design
Controllers
Deep learning
End effectors
Kinematics
Machine learning
manipulator
Manipulator dynamics
Manipulators
neural network control
Neural networks
Optimization
Parameter robustness
Parameter uncertainty
Performance evaluation
Recurrent neural networks
Robot arms
Robustness
Theoretical analysis
ISSN:2162-237X, 2162-2388, 2162-2388
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Zusammenfassung:Recently, neural network model-based control has received wide interests in kinematics control of manipulators. To enhance learning ability of neural network models, the autoencoder method is used as a powerful tool to achieve deep learning and has gained success in recent years. However, the performance of existing autoencoder approaches for manipulator control may be still largely dependent on the quality of data, and for extreme cases with noisy data it may even fail. How to incorporate the model knowledge into the autoencoder controller design with an aim to increase the robustness and reliability remains a challenging problem. In this work, a sparse autoencoder controller for kinematic control of manipulators with weights obtained directly from the robot model rather than training data is proposed for the first time. By encoding and decoding the control target though a new dynamic recurrent neural network architecture, the control input can be solved through a new sparse optimization formulation. In this work, input saturation, which holds for almost all practical systems but usually is ignored for analysis simplicity, is also considered in the controller construction. Theoretical analysis and extensive simulations demonstrate that the proposed sparse autoencoder controller with input saturation can make the end-effector of the manipulator system track the desired path efficiently. Further performance comparison and evaluation against the additive noise and parameter uncertainty substantiate robustness of the proposed sparse autoencoder manipulator controller.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2021.3109953