Apply Now! A start date in course of 2019 is to be agreed upon.
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For more information please contact Prof. dr. ir. Jan Swevers, tel.: +32 16 32 25 40, mail: firstname.lastname@example.org or dr. ir. Wilm Decré, tel.: +32 16 37 26 86, mail: email@example.com. Subject of your email should be: "PhD application Offline and online linear parameter-varying system identification"
You can apply for this job no later than April 30, 2019 via the online application tool
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The MECO research team of the Mechanical Engineering Department, KU Leuven (Belgium) is looking for a young, motivated and skilled PhD researcher with a strong background in systems, control, numerical optimization and programming.
In this research project you will develop fast and robust parameter identification methods for linear parameter-varying (LPV) systems.
LPV systems are linear systems with parameters that depend on one or several scheduling parameters that are typically variables whose variations are known or measurable. The LPV framework provides mathematically sound modelling and robust control design methods for a broad class of nonlinear systems in a wide variety of application areas, e.g. thermal, vibro-acoustic and mechatronic motion systems.
This research builds upon recently developed LPV identification techniques [1,2,3]. In a first stage you will work towards faster and numerically more robust implementations of these offline methods and their full integration into the Linear Control Toolbox developed by MECO . Next, you will develop online LPV system identification methods: recursive implementations and implementations following the framework of Moving Horizon Estimation. In the latter case, the model parameter estimation is formulated as an optimal control problem defined over a finite receding time horizon. Through a finite time horizon formulation, more robustness of the estimation with respect to measurement and system noise and disturbances is expected compared to recursive techniques, but at the cost of longer calculation times. To ease this trade-off, you will research fast solution strategies.
The focus of this research is on development of algorithms and software, and on numerical and experimental validation. For validation, several scenarios will be considered, e.g. a multi-systems learning setting where differences between systems and/or tasks are limited, and in an adaptive Model Predictive Control (MPC) setting, where model updates are used to improve controller performance and estimated model uncertainty is accounted for in the control formulation. The considered class of systems are mechatronic systems.
 Turk, D; Regularized Identification of Linear Parameter-Varying Systems: Methods and Mechatronic Applications. PhD Thesis, Department Mechanical Engineering, KU Leuven, Belgium, 2018.
 Turk, D; Singh, T; Swevers, J; 2018. Linear parameter-varying system identification of an industrial ball screw setup. Proceedings - 2018 IEEE 15th International Workshop on Advanced Motion Control, AMC 2018; 2018; pp. 90 - 95.
 Turk, D; Gillis, J; Pipeleers, G; Swevers, J; Identification of linear parameter-varying systems: A reweighted l(2,1)-norm regularization approach. Mechanical Systems and Signal Processing; 2018; Vol. 100; pp. 729 –742.
 Verbandt, Maarten; Jacobs, Laurens; Turk, Dora; Singh, Taranjitsingh; Swevers, Jan; Pipeleers, Goele; 2018. Linear Control Toolbox - supporting B-splines in LPV control. Mechatronics; 2018; Vol.52; pp. 78 – 89.
An ideal candidate has a MSc degree in engineering or applied mathematics, a strong background in systems and control, numerical optimization and programming (Matlab, C/C++), a strong interest for algorithm implementation and experimental validation on academic lab setups, and enthusiasm for scientific research. Proficiency in English is a requirement and applicants whose mother tongue is neither Dutch nor English must present an official language test report. Acceptable tests are TOEFL and Academic IELTS. Required minimum scores are:
A fully funded PhD position for four years at KU Leuven, Belgium.