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Simulation and Selection Schemes in Machine Self-Awareness, a Position Control Case Study
Linköping University, Department of Mechanical Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology.
Linköping University, Department of Mechanical Engineering, Fluid and Mechanical Engineering Systems. Linköping University, The Institute of Technology.
2004 (English)In: Power Transmission and Motion Control. PTMC 2004 / [ed] C R Burrows, K A Edge, D N Johnston, London, Bury St Edmunds, UK: Professional Engineering Publishing , 2004, 307-318 p.Conference paper, Published paper (Other academic)
Abstract [en]

Simulation techniques for numerical prediction of dynamic behaviour of a sub-system may be combined with different types of selection schemes at a controller loop level. This constitutes a way of using physical simulation models as a prediction scheme for machine control. This is similar to model-based control techniques but introduces the flexibility of switching control objectives and limitations on the fly. Such a control strategy may be of interest in applications where the environment is not fully identified or may change in an unpredictable way. A sudden change in working conditions due to a failure in the system or component itself is an example. The machine then needs to have some degree of readiness for unforeseen conditions. This is a typical scenario in remote mobile robotics and machinery in hazardous environments. Some level of self-awareness in the controller of the machine is desirable.

This paper describes a case study of techniques suitable for such machine self awareness. Simulation techniques are used in this project to predict the dynamic behaviour of a rod-less pneumatic cylinder in real-time. This forms a controller for position control of the cylinder using selection and optimisation methods based on simulation results. Previous work has shown that the method works when using grid selection schemes of a reduced set of control signals. In this paper this is further extended using genetic algorithm selection schemes.

Possible applications for the proposed scheme are discussed and a simple selection scheme based upon genetic algorithms is proposed.

Place, publisher, year, edition, pages
London, Bury St Edmunds, UK: Professional Engineering Publishing , 2004. 307-318 p.
Keyword [en]
Machine Self-Awareness, simulation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-23249Local ID: 2666ISBN: 9781860584664 (print)OAI: oai:DiVA.org:liu-23249DiVA: diva2:243563
Conference
The Bath Workshop on Power Transmission & Motion Control, PTMC' 04, Bath, United Kingdom, 1st-3rd September, 2004
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2013-11-21
In thesis
1. Selection schemes and neural networks in adaptive real-time control: predictive simulation adaptive control
Open this publication in new window or tab >>Selection schemes and neural networks in adaptive real-time control: predictive simulation adaptive control
2005 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The vision of self-aware machines was the starting point of this work. The idea is a machine having knowledge about itself and its surrounding environment, being able to react to changes in the environment. To support this vision, a number of engineering disciplines need to be merged and the control strategy "predictive simulation adaptive control'', PSAC, developed.

The focus of the thesis is the synthesis of a number of algorithms and ideas from different engineering disciplines and fields. The main disciplines that the work is based on are simulation techniques, selection schemes and neural networks; all of these combined with the constraints imposed by the real-time demands of control systems.

Selection schemes, or optimisation algorithms, are introduced here and used directly for real-time control of the test system, a rod-less pneumatic cylinder. The selection process is primarily based on genetic algorithms and the outcome of numerous simulations of the system for different possible control signals.

Neural networks in general, and the version used here in particular, the Kohonen self-organising map, is widely used for classification and storage of information. Here it is used first to approximate friction in a rod-less pneumatic cylinder, and later on, possible ways to utilise this technique for condition monitoring are briefly discussed.

Real-time systems and programming are a necessity when designing modern control systems. From the real-time constraints, special demands are put on the implemented algorithms and ideas.

By bringing all this together, piece by piece, the vision comes a little bit closer. One step on the path, is the PSAC control concept proposed here. The control concept is successfully implemented and tested on a position servo consisting of a pneumatic rod-less cylinder controlled by on/off-valves.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2005. 62 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1187
Keyword
Adaptive Control, Real-Time, Self-Organizing Feature Maps, Pneumatic, Neural Networks, Evolutionary Algorithms
National Category
Engineering and Technology
Identifiers
urn:nbn:se:liu:diva-29324 (URN)14645 (Local ID)91-85457-11-6 (ISBN)14645 (Archive number)14645 (OAI)
Available from: 2009-10-09 Created: 2009-10-09 Last updated: 2013-11-21

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Zachrison, AndersSethson, Magnus

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