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Learning Feed-Forward Control with the Python Scikit-learn library

Schrijvers, E.A. (2017) Learning Feed-Forward Control with the Python Scikit-learn library.

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Abstract:The research of this thesis is about using a learning feed-forward controlled system in a platform independent way. To achieve this, the feed-forward part of the control system is implemented in Python while the general control system is within the 20-sim simulation environment. The implementation of LFFC in Python is relatively simple due to the existence of the Scikit-learn library. This library enables the use of a B-spline network (function approximator). Communication between both environments is achieved by setting up a network connection. To that end, data will be serialized and packed by the Protocol Buffer library from Google and ZeroMQ. The data can now be sent over the network in a proper and structured way. The 1-dimensional time-indexed LFFC is implemented twice. One is completely built-up in the environment of 20-sim and the other has its feed-forward part built-up in Python. A 1- dimensional state-indexed LFFC in Python is considered as well. All implementations are demonstrated by assuming an ideal linear motor model (moving mass) representing the plant of the control system. In the case of the two dimensional state-indexed LFFC a plantmodel is used that includes two different phenomena. One phenomena was considered in the 1-dimensional case as well, i.e. the inertia of the mass. The second phenomena is non-ideal and depends on the position of the linear motor, described as the cogging. This type of LFFC is implemented in two different ways, i.e. 2x a 1-dimensional BSN (parsimonious LFFC) and 1x a 2-dimensional BSN. Both imply the use of a different trainings method. The first approach trains one BSN at a time and in such a way that only one plant influence is dominant and will be learned. The second approach will try to learn two plant influence at the same time by using only one BSN. A demonstration is given of the first approach by assuming a plant model that incorporates inertia and position dependent cogging (non-ideal linear motor model). The second approach is not demonstrated and further research is required (even though this implementation is not preferred as it has to deal with the curse of dimensionality).
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:49 domestic science
Programme:Electrical Engineering MSc (60353)
Link to this item:http://purl.utwente.nl/essays/74245
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