Automatic Control Laboratory
Complex physical processes are often modeled by large-scale dynamical systems that are difficult to simulate, predict and control. Model reduction is the art of approximating the behavior of such dynamical systems, while preserving their main features. A popular way to approach the model reduction problem is through methods based on moment matching (or rational interpolation), because of superior scalability and tractability properties as well as efficient data-driven implementations, such as Vector Fitting  and the Loewner framework . The project hinges on recent advances in the area and explores new least-squares algorithms based on moment matching.
References:  B. Gustavsen and A. Semlyen, "Rational approximation of frequency domain responses by vector fitting", IEEE Trans. Power Delivery, vol. 14, no. 3, pp. 1052-1061, July 1999.  A. Mayo and A. Antoulas. A framework for the solution of the generalized realization problem.
Linear Algebra Appl., 425(2):634–662, 2007. Special Issue in honor of Paul Fuhrmann.
See also: https://en.wikipedia.org/wiki/Model_order_reduction
- Systems Theory and Control, Systems Theory and Control
- Master Thesis, Semester Project
Model predictive control (MPC) has proven to be extremely successful in a number of real applications, as
it incorporates feedback to achieve stability and can also satisfy certain design constraints. This assumes the
existence of the model of the plant, which is not always the case. On top of that, there might be disturbances
perturbing the system or the task might change over time, e.g. when we track a moving target. In these cases
the controller needs to both learn the system through an exploration phase, e.g., using system identification
and other relevant learning techniques, and control it at the same time. Therefore, an interesting trade-
off between exploration (learning the system) and exploitation (controlling the system) arises. Finding the
right balance between exploration and exploitation is vital for deploying reliable and efficient control systems.
This problem can be approached by using both control theoretic and online learning/optimization toolboxes.
The non-asymptotic performance metric of regret will be used to evaluate the algorithms. In this thesis, the
student will concentrate on the control of linear systems and apply it to a practical example of the control
of a quadrotor for optimal reference trajectory tracking.
- Optimisation, Systems Theory and Control
- Master Thesis
Additive manufacturing, or 3D printing, produced three-dimensional parts in a layer-by-layer fashion via material deposition, melting, or other processes. The goal of this internship is to develop a modular control and simulation software framework integrating control-oriented modeling, state estimation, data handling, and optimization, integrating the separate components together.
- Engineering and Technology
Robust model predictive control (MPC) extends the guarantees provided by nominal MPC to systems subject to uncertainties, thereby ensuring safety. However, this comes at a large computational cost of propagating tubes along the prediction horizon. In this project, a novel way to ensure robustness is investigated using set invariance and constraint tightening techniques. The proposed idea could reduce the robust MPC computation times by orders of magnitude and increase feasible region compared to state-of-the-art techniques.
- Control Engineering, Optimisation, Systems Theory and Control, Systems Theory and Control
- Master Thesis