TY - JOUR
T1 - Adaptive RBF neural network-based control of an underactuated control moment gyroscope
AU - Montoya-Cháirez, Jorge
AU - Rossomando, Fracisco G.
AU - Carelli, Ricardo
AU - Santibáñez, Víctor
AU - Moreno-Valenzuela, Javier
N1 - Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
KW - Control moment gyroscope
KW - Neural networks
KW - Radial basis functions
KW - Real-time experiments
KW - Trajectory tracking control
KW - Underactuated systems
UR - http://www.scopus.com/inward/record.url?scp=85096379624&partnerID=8YFLogxK
U2 - 10.1007/s00521-020-05456-8
DO - 10.1007/s00521-020-05456-8
M3 - Artículo
AN - SCOPUS:85096379624
SN - 0941-0643
VL - 33
SP - 6805
EP - 6818
JO - Neural Computing and Applications
JF - Neural Computing and Applications
IS - 12
ER -