Adaptive RBF neural network-based control of an underactuated control moment gyroscope

Radial basis function (RBF) neural networks have the advantages of excellent ability for the learning of the processes and certain immunity to disturbances when using in control systems. The robust trajectory tracking control of complex underactuated mechanical systems is a difficult problem that re...

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Bibliographic Details
Published in:Neural computing & applications 2021-06, Vol.33 (12), p.6805-6818
Main Authors: Montoya-Cháirez, Jorge, Rossomando, Fracisco G., Carelli, Ricardo, Santibáñez, Víctor, Moreno-Valenzuela, Javier
Format: Article
Language:English
Subjects:
Adaptive control
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Controllers
Data Mining and Knowledge Discovery
Disturbances
Gyroscopes
Image Processing and Computer Vision
Mechanical systems
Neural networks
Original Article
Probability and Statistics in Computer Science
Proportional integral derivative
Radial basis function
Robust control
System effectiveness
Tracking control
Tracking errors
Trajectory control
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Summary:Radial basis function (RBF) neural networks have the advantages of excellent ability for the learning of the processes and certain immunity to disturbances when using in control systems. The robust trajectory tracking control of complex underactuated mechanical systems is a difficult problem that requires effective approaches. In particular, adaptive RBF neural networks are a good candidate to deal with that type of problems. In this document, a new method to solve the problem of trajectory tracking of an underactuated control moment gyroscope is addressed. This work is focused on the approximation of the unknown function by using an adaptive neural network with RBF fully tuned. The stability of the proposed method is studied by showing that the trajectory tracking error converges to zero while the solutions of the internal dynamics are bounded for all time. Comparisons between the model-based controller, a cascade PID scheme, the adaptive regressor-based controller, and an adaptive neural network-based controller previously studied are performed by experiments with and without two kinds of disturbances in order to validate the proposed method.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-020-05456-8