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

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Abstract

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.

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Acknowledgements

This work was supported in part by the Consejo Nacional de Ciencia y Tecnología, CONACYT–Fondo Sectorial de Investigación para la Educación under Project A1-S-24762, and in part by Secretaría de Investigación y Posgrado-Instituto Politécnico Nacional, México. Proyecto Apoyado por el Fondo Sectorial de Investigación para la Educación. Work partially supported by CONACYT project 134534 and TecNM projects.

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Authors and Affiliations

  1. Instituto Politécnico Nacional-CITEDI, Av. Instituto Politécnico Nacional 1310, Col. Nueva Tijuana, 22435, Tijuana, Baja California, Mexico

    Jorge Montoya-Cháirez & Javier Moreno-Valenzuela

  2. Instituto de Automática-Conicet-Universidad Nacional de San Juan, Av. Libertador 1109, Oeste, 5400, Argentina

    Fracisco G. Rossomando & Ricardo Carelli

  3. Tecnológico Nacional de México/Instituto Tecnológico de La Laguna, Blvd. Revolución and Cuauhtémoc SN, 27000, Torreón, Mexico

    Víctor Santibáñez

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  1. Jorge Montoya-Cháirez
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  2. Fracisco G. Rossomando
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  3. Ricardo Carelli
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  4. Víctor Santibáñez
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  5. Javier Moreno-Valenzuela
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Correspondence to Jorge Montoya-Cháirez.

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Montoya-Cháirez, J., Rossomando, F.G., Carelli, R. et al. Adaptive RBF neural network-based control of an underactuated control moment gyroscope. Neural Comput & Applic 33, 6805–6818 (2021). https://doi.org/10.1007/s00521-020-05456-8

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