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Optimal Design of Neuro-Mechanical Oscillators with Stability Constraints
Linköping University, Department of Management and Engineering, Mechanics. Linköping University, The Institute of Technology.
2015 (English)In: ZAMM-ZEITSCHRIFT FUR ANGEWANDTE MATHEMATIK UND MECHANIK, ISSN 0044-2267, Vol. 95, no 6, 620-637 p.Article in journal (Refereed) Published
Abstract [en]

This paper concerns optimal design of so-called Neuro-Mechanical Oscillators (NMOs). An NMO is a new type of bio-inspired mechatronic system which consists of an actuated truss with a recurrent neural network (RNN) superimposed onto it. By choosing the entries of the weight matrix of the RNN, an NMO can be designed, using numerical optimization, to generate pre-specified time-varying motions when subject to certain time-varying input signals. However, to rule out possible dependence of the motion on the initial state of the system as well as convergence into limit cycles, some form of constraints must be imposed on the system's design parameters. To derive such constraints, we investigate under what conditions the influence of the initial state eventually vanishes and the motion becomes completely determined by the input signal. Three sufficient criteria are presented for RNNs, but the possibility of large mechanical deformations most likely rule out global system level results. For sufficiently small deformations, however, local results are obtained, and a numerical example provided in the paper indicates that these can be useful for designing practical systems.

Place, publisher, year, edition, pages
Wiley-VCH Verlagsgesellschaft, 2015. Vol. 95, no 6, 620-637 p.
Keyword [en]
Neuro-Mechanical Oscillators; recurrent neural networks; optimal design; convergent systems
National Category
Engineering and Technology
URN: urn:nbn:se:liu:diva-76981DOI: 10.1002/zamm.201300031ISI: 000355725900004OAI: diva2:524021
Available from: 2012-04-27 Created: 2012-04-27 Last updated: 2015-06-26Bibliographically approved
In thesis
1. 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.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1444
National Category
Engineering and Technology
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)
Available from: 2012-04-27 Created: 2012-04-27 Last updated: 2016-03-14Bibliographically approved

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