Geförderte OA-Publikationen

Amorphous carbon materials with sophisticated morphologies, variable carbon layer structures, abundant defects, and tunable porosities are favorable as anodes for potassium-ion batteries (PIBs). Synthesizing amorphous carbon materials typically involves the pyrolysis of carbonaceous precursors. Nonetheless, there is still a lack of studies focused on achieving multifaceted structural optimizations of amorphous carbon through precursor formulation. Herein, nitrogen-doped amorphous carbon nanotubes (NACNTs) are derived from a novel composite precursor of cobalt-based metal-organic framework (CMOF) and graphitic carbon nitride (g-CN). The addition of g-CN in the precursor optimizes the structure of amorphous carbon such as morphology, interlayer spacing, nitrogen doping, and porosity. As a result, NACNTs demonstrate significantly improved electrochemical performance. The specific capacities of NACNTs after cycling at current densities of 100 mA/g and 1000 mA/g increased by 194 % and 230 %, reaching 346.6 mAh/g and 211.8 mAh/g, respectively. Furthermore, the NACNTs anode is matched with an organic cathode for full-cell evaluation. The full-cell attains a high specific capacity of 106 mAh/gcathode at a current density of 100 mA/g, retaining 90.5 % of the specific capacity of the cathode half-cell. This study provides a valuable reference for multifaceted structural optimization of amorphous carbon to improve potassium-ion storage capability.

Output reference tracking of unknown nonlinear systems is considered. The control objective is exact tracking in predefined finite time, while in the transient phase the tracking error evolves within a prescribed boundary. To achieve this, a novel high-gain feedback controller is developed that is similar to, but extends, existing high-gain feedback controllers. Feasibility and functioning of the proposed controller is proven rigorously. Examples for the particular control objective under consideration are, for instance, linking up two train sections, or docking of spaceships.

The upcoming 3GPP global mobile communication standard 6G strives to push the technological limits of radio frequency (RF) communication even further than its predecessors: Sum data rates beyond 100 Gbit/s, RF bandwidths above 1 GHz per link, and sub-millisecond latency necessitate very high performance development tools. We propose a new SDR firmware and software architecture designed explicitly to meet these challenging requirements. It relies on Ethernet and commercial off-the-shelf network and server components to maximize flexibility and to reduce costs. We analyze state-of-the-art solutions (USRP X440 and other RFSoC-based systems), derive architectural design goals, explain resulting design decision in detail, and exemplify our architecture’s implementation on the XCZU48DR RFSoC. Finally, we validate its performance via measurements and outline how the architecture surpasses the state of the art with respect to sustained RF recording, while maintaining high Ethernet bandwidth efficiency. Building a 6G integrated sensing and communication (ISAC) example, we demonstrate its real-time and rapid application development capabilities.

A supervised probabilistic dynamic-controlled latent-variable (SPDCLV) model is proposed for online prediction, as well as real-time optimisation of process quality indicators. Compared to existing probabilistic latent-variable models, the key advantage of the proposed method lies in explicitly modelling the dynamic causality from the manipulated inputs to the quality pattern. This is achieved using a well-designed, dynamic-controlled Bayesian network. Furthermore, the algorithms for expectation-maximisation, forward filtering, and backward smoothing are designed for learning the SPDCLV model. For engineering applications, a framework for pattern-based quality prediction and optimisation is proposed, under which the pattern-filtering and pattern-based soft sensor are explored for online quality prediction. Furthermore, quality optimisation can be realised by directly controlling the pattern to the desired condition. Finally, case studies on both an industrial primary milling circuit and a numerical example illustrate the benefits of the SPDCLV method in that it can fully model the process dynamics, effectively predict and optimise the quality indicators, and monitor the process.

To guarantee the safe and dependable operation of a magnetic levitation train, the distance between the magnet and the reaction rail needs to be kept within a given range. In this work, we design model predictive controllers which, in addition to complying with these constraints, provide a favorable behavior with regard to performance criteria such as travel comfort and control effort. For this purpose, we present a model of the system and the disturbances affecting it. Several results regarding the mathematical properties of this model are proven to gain insight for controller design. Finally we compare three different controllers w.r.t. performance criteria such as robustness, travel comfort, control effort, and computation time in an extensive numerical simulation study: a linear feedback controller, a model predictive control (MPC) scheme with quadratic stage costs, and the recently-proposed funnel MPC scheme. We show that the MPC closed loop complies with the constraints while also exhibiting excellent performance. Furthermore, we implement the MPC algorithms within the GRAMPC framework. This allows us to reduce the computational effort to a point at which real-time application becomes feasible.