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Available master theses

Multi-modal Data-enabled predictive control (DeePC)

Starting date: ASAP

Contact: Pouria Tajvar (

Data-enabled predictive control is an optimization based method to control dynamical systems [1]. The central idea is to make the system (i.e. the robot in this project) produce desired behaviours by combining a set of previously recorded behaviours. This method has enjoyed great interest from the system identification and data-driven control community as it enables control of complex systems without an explicit modelling of the dynamics [2].

DeePC is primarily designed for linear systems; in this thesis, the student works on developing an algorithm that allows applying DeePC to non-linear systems by decomposing the system state into different working modes (hence multi-modal) as for example proposed in [3]. The multi-modal DeePC will be primarily used to control a ground robot. One of the challenges of such design, is to collect data from the real robot that are sufficient for reference following control synthesis. During the thesis the student will also explore data-collection strategies ranging from learning from demonstration to autonomous exploration.

Thesis goals

  • Reviewing data-driven control and control policy learning literature.
  • Designing and implementing a multi-modal data-enabled predictive controller.
  • Analyzing the data-collection policies and dynamics exploration for mobile robots.
  • Implementing the data-driven controller on the ground robot.

The interested student is expected to be familiar with linear algebra. Methods are to be implemented using python and integrated with ROS. Prioir familiarity with ROS is valuable but not neccessary.


[1] Coulson, Jeremy, John Lygeros, and Florian Dörfler. "Data-enabled predictive control: In the shallows of the DeePC." 2019 18th European Control Conference (ECC). IEEE, 2019.

[2] De Persis, Claudio, and Pietro Tesi. "Formulas for data-driven control: Stabilization, optimality, and robustness." IEEE Transactions on Automatic Control 65.3 (2019): 909-924.

[3] Tajvar, Pouria, et al. "Robust motion planning for non-holonomic robots with planar geometric constraints." The International Symposium on Robotics Research October 6-10, 2019, Hanoi, Vietnam. 2019.

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  • Pouria Tajvar,
    DOCTORAL STUDENT, Doctoral student


  • Available master theses