Please use this identifier to cite or link to this item: https://doi.org/10.15480/882.1968
Publisher DOI: 10.1155/2018/9648126
Title: A biologically inspired framework for the intelligent control of mechatronic systems and its application to a micro diving agent
Language: English
Authors: Bessa, Wallace Moreira 
Brinkmann, Gerrit 
Dücker, Daniel-André 
Kreuzer, Edwin 
Solowjow, Eugen 
Issue Date: 30-Dec-2018
Publisher: Hindawi Publishing Corporation
Source: 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
Journal or Series Name: Mathematical problems in engineering 
Abstract (english): 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: https://tubdok.tub.tuhh.de/handle/11420/1971
DOI: 10.15480/882.1968
ISSN: 1563-5147
Institute: Mechanik und Meerestechnik M-13 
Type: (wissenschaftlicher) Artikel
Funded by: DFG [Kr752/33-1, Kr752/36-1] u.a.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
Appears in Collections:Publications with fulltext

Files in This Item:
File Description SizeFormat
MPE.2018.9648126.pdf2,87 MBAdobe PDFThumbnail
View/Open
Show full item record

Page view(s)

327
Last Week
1
Last month
11
checked on Sep 25, 2020

Download(s)

172
checked on Sep 25, 2020

Google ScholarTM

Check

Note about this record

Export

This item is licensed under a Creative Commons License Creative Commons