The chair of Prof. Bajcinca focuses on research of modern methods and advanced applications of control and system theory, involving three main pillars: cyber-physical systems, complex dynamical systems and machine learning. Through networking with a large number of national and international research, academic and industrial partners, funding projects with exotic and highly interesting tasks regarding model-based and data-driven control have been acquired on a regular basis. The research work is supported with an excellent laboratory equipment and high-performance computation in the areas of autonomous systems, robotics and energy systems, which is continuously being further developed.

https://www.mv.uni-kl.de/mec/home.
 

When it is infeasible to derive a mathematical model for a physical process using first principles, we use measured data to model the process. This is called system identification and has been the subject of research for many decades. Once we have the model for the process, we can use it to design the control laws. This is called model-based control. Although model-based control has been quite successful, it has some shortcomings. As discussed, it is a two-step process: first, identify the model and then design the control laws. Both steps typically require optimization tasks. Thus, the overall solution to the control problem may be suboptimal. To circumvent this issue, recently data-driven control approaches have been proposed. In this paradigm, control laws are designed directly from measured data without explicitly identifying the model beforehand.
 

Data-driven control is a rapidly evolving paradigm, where control strategies are designed directly from measured data. One of the main approaches in this paradigm seems to be inspired by a key result known as the fundamental lemma of the behavioral system theory, in which a model is defined as a set of trajectories. According to this result, one can generate all input/output trajectories of a controllable linear time-invariant system if one can measure a persistently exciting input/output trajectory of the system. In this project, which is funded by DFG, we will explore data-driven methods to tackle several control problems:

• To derive data-driven necessary and sufficient criteria for structural properties such as controllability and observability of different classes of systems.
• To develop behavioral system theory for nonlinear/stochastic systems. In particular, to generalize Willems et al. fundamental lemma to certain classes of nonlinear deterministic/stochastic systems.
• To develop data-driven predictive control (DPC) algorithms as motivated by the approach of model predictive control (MPC), which is a powerful technique to design control laws for complex tasks. While MPC requires an explicit model description by dynamic equations for its implementation, in this work package, the aim is to design the predictive control laws based directly on measured data, thus avoiding the need for explicit identification of the underlying model. 
• The latter algorithms should be extended to adaptive DPC. Various disturbance models need to be synthesised to guarantee the necessary persistence of excitation. In this context, the adaptation laws for Hankel matrices, lying at the core of data driven modelling should be studied, too. 
• As an application, the developed theory/algorithms should be applied to devise autonomous powder compaction processing. Powder compaction performed on a rotary tablet press is a dry granulation method to transfer powder materials consisting of several components (drug, lubricant, and other excipients) into compacts (tablets). To this end, we are cooperating with one of our research academic partners at the University of Dortmund. 
   

• Above-average Master’s degree in electrical engineering, mathematics, or related field.
• Advanced control engineering and numerical optimization skills beyond the content of basic lectures.
• Familiarity with the behavioral system theory is advantageous.
• Highly motivated, eager to work within a team or independently
• Knowledge of at least one programming language: Matlab, Julia, or Python is expected.
• Organizational and collaborative skills with research partners from different disciplines.
• Proficiency in English and / or German is essential.
   

• Payment according to TV-L E13 with an initial one-year time limit
• The possibility to do a PhD and to teach is given in case of scientific aptitude
• TUK strongly encourages qualified female academics to apply
• Severely disabled persons will be given preference in the case of appropriate suitability (please enclose proof)
• Electronic application is preferred. Please attach only one coherent PDF.

You can expect an interesting, diversified and responsible task within a young, highly motivated and interdisciplinary team of a growing chair with great personal creativity freedom.

Prof. Dr.-Ing. Naim Bajcinca
Phone: +49 (0)631/205-3230
Mobile: +49 (0)172/614-8209
Fax:  +49 (0)631/205-4201
Email: mec-apps(at)mv.uni-kl.de

Powder compaction
Data-driven control
Fundamental Lemma
Autonomous Control
 

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31. October 2023

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