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Faculty of Architecture and Regional Planning at Vienna University of Technology, Austria
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2 Papers

A New Training Algorithm for Neuro-Fuzzy Networks

Authors: Stefan Jakubek 1 and Nikolaus Keuth 2

Affiliations: 1 Vienna University of Technology, Institute for Mechanics and Mechatronics, Austria , 2 AVL List GmbH, Austria

Digital Object Identifier: 10.5220/0001180200230034

Proceedings of ANNIIP 2005/ICINCO, ISBN: 972-8865-36-8

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In this paper a new iterative construction algorithm for local model networks is presented. The algorithm is focussed on building models with sparsely distributed data as they occur in engine optimization processes. The validity function of each local model is fitted to the available data using statistical criteria along with regularisation and thus allowing an arbitrary orientation and extent in the input space. Local models are consecutively placed into those regions of the input space where the model error is still large thus guaranteeing maximal improvement through each new local model. The orientation and extent of each validity function is also adapted to the available training data such that the determination of the local regression parameters is a well posed problem. The regularisation of the model can be controlled in a distinct manner using only two user-defined parameters. Examples from an industrial problems illustrate the efficiency of the proposed algorithm.

MOBILE ROBOT PREDICTIVE TRAJECTORY TRACKING

Topic: Mobile Robots and Autonomous Systems

Authors: Martin Seyr and Stefan Jakubek

Affiliation: Vienna University of Technology, Institute of Mechanics and Mechatronics, Austria

Digital Object Identifier: 10.5220/0001184001120119

Proceedings of ICINCO 2005, ISBN: 972-8865-30-9

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For a two-wheeled differentially driven mobile robot a trajectory tracking concept is developed. A trajectory is a time-indexed path in the plane, i.e. in the three-dimensional configuration space consisting of position and orientation. Due to the nonholonomic nature of a rolling wheel, the system cannot be stabilized by a continuous time-invariant feedback or by feedback linearization. A novel approach taken in this paper to solve the nonholonomic control problem consists of nonlinear predictive control in conjunction with linear state space control with integration of the control error. Based on a Gauss-Newton algorithm, predicted future position errors are minimized by numerical computation of an optimal sequence of control inputs using prespecified shape functions.
Collapse Button Related Ontology Subjects/Areas/Topics (3)
Informatics in Control, Automation and Robotics

Mobile Robots and Autonomous Systems

Robotics and Automation

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