Dieses Dokument steht unter einer CreativeCommons Lizenz by/4.0
Verlagslink DOI: https://doi.org/10.1155/2018/9648126
Titel: A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent
Sprache: English
Autor/Autorin: Bessa, Wallace Moreira 
Brinkmann, Gerrit 
Dücker, Daniel-André 
Kreuzer, Edwin 
Solowjow, Eugen 
Erscheinungsdatum: 30-Dez-2018
Verlag: Hindawi Publishing Corporation
Quellenangabe: Wallace M. Bessa, Gerrit Brinkmann, Daniel A. Duecker, Edwin Kreuzer, and Eugen Solowjow, “A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent,” Mathematical Problems in Engineering, vol. 2018, Article ID 9648126, 16 pages, 2018. doi:10.1155/2018/9648126
Zeitschrift oder Schriftenreihe: Mathematical problems in engineering 
Zusammenfassung (englisch): Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.
URI: http://dx.doi.org/10.1155/2018/9648126
https://tubdok.tub.tuhh.de/handle/11420/1971
DOI: 10.15480/882.1968
ISSN: 1563-5147
Institut: Mechanik und Meerestechnik M-13 
Dokumenttyp: (wissenschaftlicher) Artikel
Sponsor / Fördernde Einrichtung: DFG [Kr752/33-1, Kr752/36-1] u.a.
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