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PhD Position Data-driven Learning of Linear Parameter-varying Models

A new opening PhD position is available at Eindhoven University of Technology, Netherlands. The funds for this position are available for 4 years. There is no application deadline for this position.

This PhD research is within the scope of the APROCS (Automated Linear Parameter-Varying Modeling and Control Synthesis for Nonlinear Complex Systems) initiative funded by the European Research Council (ERC) and it aims to overcome current limitations of the Linear Parameter-Varying (LPV) framework. Specifically, this PhD project focuses on developing LPV modelling methods in terms of an automated toolset that facilitates system oriented LPV control design for Non-Linear (NL) and/or Time-Varying (TV) systems. In the APROCS project, currently, a radical paradigm-shift is developed in the LPV framework to focus control synthesis on the resulting controlled behavior with the targeted physical system, providing directly a NL/TV controller with stability and performance guarantees; i.e., the LPV concept is used as a solution approach for the underlying NL/TV controller design problem. However, this requires further development of existing data-driven modeling tools to achieve control oriented LPV modeling, i.e., developing automatically LPV models of physical systems that only capture that part of the underlying behavior which is necessary to achieve the control specifications set by the user. These methods are required to efficiently explore and identify uncertainties of known relations of the system. Hence novel parametrization of dynamical sub-modes for system identification under given priors is targeted enabling to introduce control oriented identification methods with plug & play (P&P) modelling capabilities. Furthermore, full exploration of the steps of the identification cycle from incremental experiment design to verification of model completion (validation) is required, exploring issues of informativity of data sets w.r.t. such P&P approaches.

This PhD project aims to surmount these challenges by establishing an innovative synergy between the Machine Learning (ML) and the LPV framework. The aim is to develop computationally efficient model learning approaches capable of supporting control synthesis. The emerging ML framework provides powerful data-driven approaches to facilitate non-parametric learning of complicated data-relations. The flexibility of the ML framework in defining learning objectives (aim-relevant estimation) and its ability to facilitate optimal recovery of structural relationships (model structure selection) provide novel perspectives in terms of developing dedicated methods to solve the limiting problems the current identification LPV theory.

This research will be conducted in close collaboration with other APROCS and industrially related projects, hence the results will be applied to modeling and control problems in complex mechatronic systems such as high-performance positioning applications, robots, suspension systems, etc.

- A challenging job for 4 years in a dynamic and ambitious university and a stimulating research environment;

- Support with your professional and personal development;

- A gross salary per month of 2325,- (first year) as a PhD up to 2972,- (final year) in accordance with the Collective Labor Agreement of the Dutch Universities

- Plus 8% holiday allowance + 8.3% end of the year allowance.

- An extensive package of fringe benefits, e.g., support in moving expenses, excellent technical infrastructure, on-campus child care, and excellent sports facilities.

Information

For more information about the advertised position, please contact: More information on employment conditions can be found here: http://www.tue.nl/en/university/working-at-tue/working-conditions/ .

Application

If interested, please use ‘apply now’-button at the top of this page. You should upload the following: - a cover letter explaining your motivation and suitability for the position;

- copies of diplomas with course grades (transcripts).


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