Postdoc in LPV Model Reduction and Frequency Domain Analysis Eindhoven University of Technology School of Electrical Engineering Netherlands


Job Title: Postdoc in LPV Model Reduction and Frequency Domain Analysis
Faculty: Electrical Engineering 

Closing date:07/07/2019

Vacancy number: V36.3931

 

 

Description


Research group:


The Control Systems (CS) group has a long tradition in modeling, system identification and control of dynamical systems. With an international staff and close cooperation with industrial research partners, the group is active in four technology domains: industrial production processes, high-tech systems, power networks and automotive systems. The basis of systems and control research and competence in the group is in system identification, model approximation, (nonlinear) model predictive control, spatial-temporal systems and model-based control and optimization. The group provides education modules in the BSc and MSc programs of Electrical Engineering, Automotive Systems and the MSc program Systems and Control. The group also actively participates in the PhD school of the Dutch Institute of Systems and Control (DISC). Currently the group consists of about 35 people, among which 6 full-time academics.

 

Project description:


Linear Parameter-Varying (LPV) systems are flexible mathematical models capable of representing Nonlinear (NL)/Time-Varying (TV) dynamical behaviors of complex physical systems (e.g., wafer scanners, car engines, chemical reactors), often encountered in engineering, via a linear structure. The LPV framework provides computationally efficient and robust approaches to synthesize digital controllers that can ensure desired operation of such systems - making it attractive to

(i) high-tech mechatronic, (ii) automotive and (iii) chemical-process applications.

Such a framework is important to meet with the increasing operational demands of systems in these industrial sectors and to realize future technological targets. However, recent studies have shown that, to fully exploit the potential of the LPV framework, a number of limiting factors of the underlying theory ask a for serious innovation, as currently it is not understood how to

(1) automate exact and low-complexity LPV modeling of real-world applications and how to refine uncertain aspects of these models efficiently by the help of measured data, (2) incorporate control objectives directly into modeling and to develop model reduction approaches for control, and (3) how to see modeling & control synthesis as a unified, closed-loop system synthesis approach directly oriented for the underlying NL/TV system. Furthermore, due to the increasingly cyber-physical nature of applications, (4) control synthesis is needed in a plug & play fashion, where if sub-systems are modified or exchanged, then the control design and the model of the whole system are only incrementally updated. This project aims to surmount Challenges (1)-(4) by establishing an innovative revolution of the LPV framework supported by a software suite and extensive empirical studies on real-world industrial applications; with a potential of technological innovation in the high-impact industrial sectors (i)-(iii).

 

Position


The position 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 the fundamental limitations of the current LPV framework listed by Challenges (1)-(4).

This 3 years Postdoc position focuses on model reduction approaches to be developed both in the time and frequency domain that aim at complexity reduction of converted LPV first-principle models by balancing the trade-off between complexity and accuracy in terms of the control/user objectives. This concept of reduction is drastically different from the available literature and require fundamentally new concepts to take into account: (1) the true underlying behavior of the represented NL/TV system, (2) how closed-loop measures of control performance effect the trade-offs and (3) how to achieve joint state-order and scheduling complexity reduction which represent two complementary sources of complexity. A thorough (4) understanding of LPV models in the frequency domain is also intended to be developed. This challenging objective would allow the generalization of powerful design concepts of controller tuning and performance specifications together with stability results and model reduction methods in the LTI case to the LPV case. Furthermore, the long-term focus is on developing a novel theoretical LPV framework where modeling and control synthesis is seen as a unified process with a major emphasis on the achieved controlled (i.e., in case of feedback control, the closed-loop) NL/TV behavior, i.e., a Direct Controlled System Synthesis (DCSS), where achieved performance bottlenecks of control synthesis are used to re-fine and re-adjust the system model.

 

Tasks:


Study the literature of linear robust and parameter-varying model reduction, system theory and the incorporation of LPV stability and control performance specifications into the reduction/modeling. Study concepts of frequency domain analysis of nonlinear and multidimensional linear systems.
Development of systematic approaches which are capable to surmount Challenges (2) and (4) according to the above mentioned focus points (1)-(4).
Implementation and validation of the developed approaches on existing prototype applications and benchmark setups at TU/e.
Actively participating in the development of a software suite (modeling and control module) to provide a publically available toolbox that offers off-shelf solution for LPV modeling and control.
Actively contribute to and take initiative in the life of co-operating research teams and companies.
Dissemination of the results of your research in international and peer-reviewed journals and conferences.
Assume educational tasks like assisting the supervision of PhD students, Master students and internships.
Functie-eisen


We are looking for a candidate who meets the following requirements:


You are a talented and enthusiastic researcher.
You have obtained a PhD in systems and control and have strong background in mathematics, statistics, signal processing and electrical machines or mechanical engineering.
You have good programming skills and experience (knowledge in C++, DSpace, Mathematica and/or Matlab is an asset).
You have good communicative skills, and the attitude to partake successfully in the work of a research team.
You are creative and ambitious, hardworking and persistent.
You are a team player.
You have good command of the English language (knowledge of Dutch is not required).

 

Job Conditions


We offer an exciting job in the group Control Systems of the Eindhoven University of Technology:

A fulltime appointment for 1 year at Eindhoven University of Technology http://www.tue.nl/en/
Gross monthly salaries and conditions are in accordance with the Collective Labor Agreement of the Dutch Universities (CAO NU), scale 10 (between € 3255,00 and € 4274,00 depending on your experience).
More information on employment conditions can be found here: https://www.tue.nl/en/working-at-tue/why-tue/compensation-and-benefits/
Interesting cooperation with the high-tech and process industry in- and outside The Netherlands.
An attractive package of benefits, including end-of-year allowance, and excellent sport facilities.
Informatie en sollicitatie
Information

More information on the vacancy and project can be obtained from dr. ir. Roland Tóth, r.toth[at]tue.nl .

For information concerning employment conditions, you can contact:
Mrs. Tanja van Waterschoot, e-mail: t.a.m.v.waterschoot[at]tue.nl .

 

Applications

 

Interested candidates with a background in systems and control, (applied) physics, mathematics, electrical engineering, mechanical engineering, chemical engineering or a related field are encouraged to apply using the 'apply now'-button. You should upload the following:

A detailed curriculum vitae, a letter of motivation and portfolio with relevant work.
A cover letter explaining your motivation and suitability for the position.
A list of publications and key achievements in research project(s)).
Contact information of two references.
Copies of diplomas with course grades.
Please keep in mind; you can upload only 5 documents up to 2 MB each.

 


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