PhD on AI-driven Co-design for Motion Control

Job Description

This PhD position at the Control Systems group of the Electrical Engineering department of TU/e is part of a research consortium aiming at the development of a radically new high-precision planar stage technology where the mechanical (ME) structure, the electromagnetic (EM) motor design, the metrology layout and the motion controllers (CT) design are simultaneously co-optimized. It is expected that taking these elements jointly into account during one combined AI-driven optimization process creates a significantly better end result than performing these optimizations individually and can lead to the discovery of unconventional ME, EM and CT designs and trade-offs that would have required decades of conventional design iterations to be discovered. The research is part of the AIDEAL project conducted together with academic partners (TU/e: Electromechanics and Power Electronics and TUDelft: Computational Design and Mechanics Group) and leading companies of the semiconductor industry (ASML, ASMPT, ITEC, MI-Partners, NTS-AM, Tecnotion, VAC Germany).

The objective of this PhD research is developing the high-precision motion control part of the co-design process for planar stage actuators. The research involves significant laboratory work in terms of implementation and experimental verification of the research results on a prototype stage developed at TU/e in collaboration with industrial partners.

The control performance of a planar motor, measured in nanometers of error during a scanning motion and is highly dependent on physical properties of the mover. Dynamic behavior in terms of resonant modes, position dependency, commutation strategy and of course feedback and feedforward controller design, are all critical elements. During optimization of the mechanical and electromagnetical structures, a fast assessment of control performance is required. However, the complexity of a full model and its associated complexity of the controller design is a limiting factor. Therefore, one research focus lies on constructing simplified (“surrogate”) models that are simple enough for an efficient performance assessment, but still capture the relevant mover properties. In the longer term, the availability of the control performance sensitivity to changes in the mechanical and electromagnetic design would make the co-optimization much more efficient.

Main research directions:

• Automated physics-driven surrogate modeling for co-design: establish learning-assisted automated approaches, capable of extracting the control specifications-relevant dynamics of planar motor designs and provide automatized trade-off selection between complexity of the model and the size of the uncertainty capturing the reduced dynamics. Key aspect is to learn a mapping that can re-cast the complex parametrization-based design variations to key low dimensional features on which the extracted surrogate dynamics depend on (reduced order modeling, co-parametrized model extractions, auto-encoders, reinforcement learning).
• Data-driven surrogate co-models: develop identification of surrogate representations from experimental data to allow their augmentation and adjustment to manufactured instances of the design based on physical measurements (physics-guided learning, deep recurrent neural networks, Gaussian processes).
• Automated design of optimal motion-controllers: development of automated design of advanced motion controllers based on the extracted surrogate models, capable to provide high-accuracy (sub-nm) point of interest (POI) motion of the mover of the planar motor by rejecting environmental disturbances and controlling the spatial deformation of the plate through the highly complex coil-magnet EM interactions. The focus is on the handling of a diverse set objective functions based on customer-specified motion behavior of the planar motor (e.g., guaranteed finite-time settling) under uncertain and nonlinear behavior of the system and on the computational efficiency of the optimization.

Job Requirements

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

• You are a talented and enthusiastic young researcher.
• You have experience with or a strong background in systems and control, mathematics, signal processing. Preferably you finished a master in Systems & Control, Mechanical Engineering, Electrical Engineering, or Artificial Intelligence for Engineering.
• You have good programming skills and experience (Python, C/C++, Matlab).
• You have good communicative skills, and the attitude to partake successfully in the work of a research team.
• You are creative and ambitious, hard-working and persistent.
• You have good command of the English language (knowledge of Dutch is not required).

Conditions of Employment

• A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you: 
• Full-time employment for four years, with an intermediate assessment after nine months. You will spend a minimum of 10% of your four-year employment on teaching tasks, with a maximum of 15% per year of your employment. 
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. € 2,901 max. € 3,707).  
• A year-end bonus of 8.3% and annual vacation pay of 8%. 
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process. 
• An excellent technical infrastructure, on-campus children's day care and sports facilities.  
• An allowance for commuting, working from home and internet costs. 
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates. 

Information

Do you recognize yourself in this profile and would you like to know more? Please contact the hiring manager Prof.dr.ir. Roland Toth (r.toth@tue.nl). https://www.tue.nl/en/research/researchers/roland-toth/

Visit our website for more information about the application process or the conditions of employment. You can also contact HR Services Flux (HRServices.Flux@tue.nl).

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Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position.
• Curriculum vitae, including a list of your publications and the contact information of three references.

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled.