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Optimal Design of Neuro-Mechanical Oscillators
Linköping University, Department of Management and Engineering, Mechanics. Linköping University, The Institute of Technology.
2013 (English)In: Computers & structures, ISSN 0045-7949, E-ISSN 1879-2243, Vol. 119, 189-202 p.Article in journal (Refereed) Published
Abstract [en]

This paper concerns optimization of active mechanical systems capable of exhibiting persistent oscillatory behavior. In part inspired by biological systems possessing similar properties we refer to these systems as neuro-mechanical oscillators. The mathematical model consists of a set of nonlinear ordinary differential equations describing an actuated truss excited by a nonlinear recurrent neural network. An optimization problem is formulated with the goal of adjusting some of the parameters in the system such that when the neural network is subjected to a constant input, one of the nodes in the truss follows a prescribed trajectory in a periodic fashion. Two examples are presented to illustrate the concept, and the corresponding optimization problems are solved numerically.

Place, publisher, year, edition, pages
2013. Vol. 119, 189-202 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-67846DOI: 10.1016/j.compstruc.2012.11.018ISI: 000317171000017OAI: oai:DiVA.org:liu-67846DiVA: diva2:413610
Available from: 2011-04-29 Created: 2011-04-29 Last updated: 2017-12-11Bibliographically approved
In thesis
1. Optimal Design of Neuro-Mechanical Networks
Open this publication in new window or tab >>Optimal Design of Neuro-Mechanical Networks
2011 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns modeling and optimal design of Neuro-Mechanical Networks. A Neuro-Mechanical Network (NMN) can be described as an active mechanical structure, made up from a network of simple but multifunctional elements that interact with their nearest neighbors. The concept is of mechatronic character as it involves integration of actuators, sensors, signal processing, and control, into a mechanical structure.

The first part of the thesis consists of three chapters. The first of these chapters contains a brief introduction to the NMN-concept and the present work. In the second chapter, the particular type of NMNs considered here is described in more detail, and the third chapter constitute a brief survey of some works relevant to optimization of active structures, including enabling technologies and static and dynamic shape control.

The second part of the thesis consists of two papers, where the first paper describes optimal design of NMNs for static shape control, while the second paper is concerned with optimal design of structures that exhibit oscillatory motion.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 38 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1495
Keyword
Neuro-mechanical networks, active structures, neural networks, structural optimization
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-67847 (URN)LIU-TEK-LIC-2011:34 (Local ID)978-91-7393-141-0 (ISBN)LIU-TEK-LIC-2011:34 (Archive number)LIU-TEK-LIC-2011:34 (OAI)
Presentation
2011-05-20, ACAS, Hus A, Campus Valla, Linköpings universitet, Linköping, 13:15 (Swedish)
Opponent
Supervisors
Available from: 2011-04-29 Created: 2011-04-29 Last updated: 2017-05-15Bibliographically approved
2. Optimal Design of Neuro-Mechanical Networks
Open this publication in new window or tab >>Optimal Design of Neuro-Mechanical Networks
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Many biological and artificial systems are made up from similar, relatively simple elements that interact directly with their nearest neighbors. Despite the simplicity of the individual building blocks, systems of this type, network systems, often display complex behavior — an observation which has inspired disciplines such as artificial neural networks and modular robotics. Network systems have several attractive properties, including distributed functionality, which enables robustness, and the possibility to use the same elements in different configurations. The uniformity of the elements should also facilitate development of efficient methods for system design, or even self-reconfiguration. These properties make it interesting to investigate the idea of constructing mechatronic systems based on networks of simple elements.

This thesis concerns modeling and optimal design of a class of active mechanical network systems referred to as Neuro-Mechanical Networks (NMNs). To make matters concrete, a mathematical model that describes an actuated truss with an artificial recurrent neural network superimposed onto it is developed and used. A typical NMN is likely to consist of a substantial number of elements, making design of NMNs for various tasks a complex undertaking. For this reason, the use of numerical optimization methods in the design process is advocated. Application of such methods is exemplified in four appended papers that describe optimal design of NMNs which should take on static configurations or follow time-varying trajectories given certain input stimuli. The considered optimization problems are nonlinear, non-convex, and potentially large-scale, but numerical results indicate that useful designs can be obtained in practice.

The last paper in the thesis deals with a solution method for optimization problems with matrix inequality constraints. The method described was developed primarily for solving optimization problems stated in some of the other appended papers, but is also applicable to other problems in control theory and structural optimization.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2012. 42 p.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1444
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-76984 (URN)978-91-7519-900-9 (ISBN)
Public defence
2012-06-01, C3, C-huset, Campus Valla, Linköpings universitet, Linköping, 11:15 (English)
Opponent
Supervisors
Available from: 2012-04-27 Created: 2012-04-27 Last updated: 2017-05-15Bibliographically approved

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Thore, Carl-Johan

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