PhD Researcher in Fast Model Predictive Control for Robots KU Leuven, Department of Mechanical Engineering

FAST MODEL PREDICTIVE CONTROL FOR ROBOTS

 

(Ref. BAP-2017-506)

 

Employment: Full time

 

Duration: Certain duration

 

City: Leuven

 

Apply until: 31/10/2017

 

The KU Leuven, Department of Mechanical Engineering is searching for a young, motivated and skilled PhD researcher with a strong background in numerical optimization, systems and control, and robotics.

 

FAST MODEL PREDICTIVE CONTROL FOR ROBOTS

 

This research will be performed in the MECO research team (Motion Estimation Control and Optimization), of the department Mechanical Engineering, KU Leuven. 
 

MECO is involved in the KU Leuven Center of Excellence on Optimization in Engineering (OPTEC), is a strategic partner of Flanders Make, and has a close cooperation with leading mechatronic and machine-building companies in Flanders. The MECO research team focusses on modeling, estimation, identification, analysis and optimal control of motion and motion systems such as mechatronic systems, machine tools. It combines theoretical contributions (development of design methodologies) with experimental knowhow (implementation and experimental validation on lab-scale as well as industrial setups). The theoretical research benefits from the group’s expertise on numerical optimization, especially convex optimization.

Project

This project focuses on optimal contact-free motion control of serial robots operating in changing environments. Changing environments require real-time motion planning, which is very challenging due to complex robot kinematics and dynamics and continuously changing collision constraints. The overall project goal is to develop and experimentally validate an effective MPC approach for serial robots that realizes contact-free optimal robot motion planning and control in real-time. This research will be supported by an MPC toolchain development in order to integrate all software in an open and modular fashion as to create a workflow from problem specification to deployment. All developments will be validated experimentally on industrial robotic set-ups in the lab.

Profile

An ideal candidate has a master degree in engineering (mechanical, control ...) and a strong background in control and dynamic system modelling, robotics, numerical optimization, programming (Matlab, C/C++), a strong interest and experience in work on real-world experiments, and enthusiasm for the project. Proficiency in English is a requirement. Applicants whose mother tongue is neither Dutch nor English must present an official language test report. The acceptable tests are TOEFL, IELTS, and Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Required minimum scores are:
  • TOEFL: 600 (paper-based test), 100 (internet-based test)
  • IELTS: 7 (only Academic IELTS test accepted)
  • CAE/CPE: grade B or A.

 

Offer

A fully funded PhD position for four years at the KU Leuven. KU Leuven is among the top European universities and a hub for interdisciplinary research in the field of optimization. You will be embedded in the MECO research team of the department of Mechanical Engineering located at the campus in Leuven and the new mechatronics research group, established in 2015, and located at the campus in Bruges. Theoretical work and lab experiments, which will be the main part of the work, will be carried out at the campus in Leuven. Industrial applications will be implemented at the campus in Bruges.
 

Interested?

 

To apply, send email to jan.swevers@kuleuven.be. Subject of your email should be: “ROBOT MPC PhD application”. Deadline: October 31, 2017! 
Include:

- an academic CV,
- a Pdf of your diplomas and transcript of course work and grades,
- statement of research interests and career goals (max. 2 pages),
- sample of technical writing (publication or thesis),
- contact details of at least two referees,
- proof of English language proficiency test results.

 

You can apply for this job no later than October 31, 2017 via the 


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