Publikationen

Recently, data-driven predictive control of linear systems has received wide-spread research attention. It hinges on the fundamental lemma by Willems et al. In a previous paper, we have shown how this framework can be applied to predictive control of linear time-invariant descriptor systems. In the present paper, we present a case study wherein we apply data-driven predictive control to a discrete-time descriptor model obtained by discretization of the power-swing equations for a nine-bus system. Our results show the efficacy of the proposed control scheme and they underpin the prospect of the data-driven framework for control of descriptor systems.

Laser photocoagulation is a technique applied in the treatment of retinal disease, which is often done manually or using simple control schemes. We pursue an optimization-based approach, namely Model Predictive Control (MPC), to enforce bounds on the peak temperature and, thus, to ensure safety during the medical treatment procedure - despite the spot-dependent absorption of the tissue. The desired laser repetition rate of 1 kHz is renders the requirements on the computation time of the MPC feedback a major challenge. We present a tailored MPC scheme using parametric model reduction, an extended Kalman filter for the parameter and state estimation, and suitably tuned stage costs and verify its applicability both in simulation and experiments with porcine eyes. Moreover, we give some insight on the implementation specifically tailored for fast numerical computations.

Classical turnpikes correspond to optimal steady states which are attractors of infinite-horizon optimal control problems. In this paper, motivated by mechanical systems with symmetries, we generalize this concept to manifold turnpikes. Specifically, the necessary optimality conditions projected onto a symmetry-induced manifold coincide with those of a reduced-order problem defined on the manifold under certain conditions. We also propose sufficient conditions for the existence of manifold turnpikes based on a tailored notion of dissipativity with respect to manifolds. Furthermore, we show how the classical Legendre transformation between Euler-Lagrange and Hamilton formalisms can be extended to the adjoint variables. Finally, we draw upon the Kepler problem to illustrate our findings.

In this paper, a research platform for concentric tube continuum robots is developed in order to enable advances in deploying curved cannulae for stereotactic neurosurgery. The system consists of a manually operated high-precision actuation apparatus and a photogrammetric system with measurement errors in the range of 100 micrometer. With this platform, previously planned curved paths can be analyzed ex-situ w.r.t., e.g., target precision, follow-the-leader-behavior, and hysteretic phenomena. Regarding research towards an in-vivo application in human brains, first tests with porcine brain cadavers inside an intraoperative CT are conducted in order to pave the way for histological as well as target reachability studies.

Dynamic safety-critical systems require model-based techniques and tools for their systems design. The paper presents a stochastic Petri net model of an industrial safetycritical cloud server architecture for train control. Its reliability has to be evaluated to assess tradeoffs in architecture and level of fault tolerance. Simulation methods are too slow for such rare-event problems, while numerical analysis techniques suffer from the state-space explosion problem. The paper extends a recently developed multi-trajectory simulation algorithm combining elements of simulation and numerical analysis such that it increases the accuracy of rare-event simulations within a given computation time budget. Simulation experiments have been carried out with a prototype tool.