@inproceedings{30236,
  abstract     = {{Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive
results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field.

To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of
wireless mobile networks.}},
  author       = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}},
  booktitle    = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}},
  keywords     = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}},
  location     = {{Budapest}},
  publisher    = {{IEEE}},
  title        = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}},
  year         = {{2022}},
}

@article{30861,
  abstract     = {{<jats:title>Abstract</jats:title><jats:p>We consider the problem of maximization of metabolite production in bacterial cells formulated as a dynamical optimal control problem (DOCP). According to Pontryagin’s maximum principle, optimal solutions are concatenations of singular and bang arcs and exhibit the chattering or <jats:italic>Fuller</jats:italic> phenomenon, which is problematic for applications. To avoid chattering, we introduce a reduced model which is still biologically relevant and retains the important structural features of the original problem. Using a combination of analytical and numerical methods, we show that the singular arc is dominant in the studied DOCPs and exhibits the <jats:italic>turnpike</jats:italic> property. This property is further used in order to design simple and realistic suboptimal control strategies.</jats:p>}},
  author       = {{Caillau, Jean-Baptiste and Djema, Walid and Gouzé, Jean-Luc and Maslovskaya, Sofya and Pomet, Jean-Baptiste}},
  issn         = {{0022-3239}},
  journal      = {{Journal of Optimization Theory and Applications}},
  keywords     = {{Applied Mathematics, Management Science and Operations Research, Control and Optimization}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Turnpike Property in Optimal Microbial Metabolite Production}}},
  doi          = {{10.1007/s10957-022-02023-0}},
  year         = {{2022}},
}

@article{34414,
  abstract     = {{Given a steadily increasing demand on multi-material lightweight designs, fast and cost-efficient production technologies, such as the mechanical joining process clinching, are becoming more and more relevant for series production. Since the application of such joining techniques often base on the ability to reach similar or even better joint loading capacities compared to established joining processes (e.g., spot welding), few contributions investigated the systematic improvement of clinch joint characteristics. In this regard, the use of data-driven methods in combination with optimization algorithms showed already high potentials for the analysis of individual joints and the definition of optimal tool configurations. However, the often missing consideration of uncertainties, such as varying material properties, and the related calculation of their impact on clinch joint properties can lead to poor estimation results and thus to a decreased reliability of the entire joint connection. This can cause major challenges, especially for the design and dimensioning of safety-relevant components, such as in car bodies. Motivated by this, the presented contribution introduces a novel method for the robust estimation of clinch joint characteristics including uncertainties of varying and versatile process chains in mechanical joining. Therefore, the utilization of Gaussian process regression models is demonstrated and evaluated regarding the ability to achieve sufficient prediction qualities.}},
  author       = {{Zirngibl, Christoph and Schleich, Benjamin and Wartzack, Sandro}},
  issn         = {{0268-3768}},
  journal      = {{The International Journal of Advanced Manufacturing Technology}},
  keywords     = {{Industrial and Manufacturing Engineering, Computer Science Applications, Mechanical Engineering, Software, Control and Systems Engineering}},
  publisher    = {{Springer Science and Business Media LLC}},
  title        = {{{Robust estimation of clinch joint characteristics based on data-driven methods}}},
  doi          = {{10.1007/s00170-022-10441-7}},
  year         = {{2022}},
}

@article{34046,
  author       = {{Hoffmann, Christin and Thommes, Kirsten}},
  issn         = {{2168-2291}},
  journal      = {{IEEE Transactions on Human-Machine Systems}},
  keywords     = {{Artificial Intelligence, Computer Networks and Communications, Computer Science Applications, Human-Computer Interaction, Signal Processing, Control and Systems Engineering, Human Factors and Ergonomics}},
  pages        = {{1--11}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Seizing the Opportunity for Automation—How Traffic Density Determines Truck Drivers' Use of Cruise Control}}},
  doi          = {{10.1109/thms.2022.3212335}},
  year         = {{2022}},
}

@article{35586,
  author       = {{Protte, Marius and Fahr, Rene and Quevedo, Daniel E.}},
  issn         = {{1066-033X}},
  journal      = {{IEEE Control Systems}},
  keywords     = {{Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering, Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering}},
  number       = {{6}},
  pages        = {{57--76}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Behavioral Economics for Human-in-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy}}},
  doi          = {{10.1109/mcs.2020.3019723}},
  volume       = {{40}},
  year         = {{2022}},
}

@article{35206,
  author       = {{Bonnard, Bernard and Rouot, Jérémy and Wembe Moafo, Boris Edgar}},
  issn         = {{2156-8472}},
  journal      = {{Mathematical Control and Related Fields}},
  keywords     = {{Applied Mathematics, Control and Optimization, General Medicine}},
  pages        = {{0--0}},
  publisher    = {{American Institute of Mathematical Sciences (AIMS)}},
  title        = {{{Accessibility properties of abnormal geodesics in optimal control illustrated by two case studies}}},
  doi          = {{10.3934/mcrf.2022052}},
  year         = {{2022}},
}

@article{33869,
  author       = {{Bonnard, B. and Cots, O. and Gergaud, J. and Wembe Moafo, Boris Edgar}},
  issn         = {{0167-6911}},
  journal      = {{Systems &amp; Control Letters}},
  keywords     = {{Electrical and Electronic Engineering, Mechanical Engineering, General Computer Science, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Abnormal geodesics in 2D-Zermelo navigation problems in the case of revolution and the fan shape of the small time balls}}},
  doi          = {{10.1016/j.sysconle.2022.105140}},
  volume       = {{161}},
  year         = {{2022}},
}

@article{33982,
  author       = {{Koppert, Steven and Henke, Christian and Trächtler, Ansgar and Möhringer, Stefan}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{Control and Systems Engineering}},
  number       = {{2}},
  pages        = {{554--560}},
  publisher    = {{Elsevier BV}},
  title        = {{{Tool Wear Monitoring of a Tree Log Bandsaw using a Deep Convolutional Neural Network on challenging data}}},
  doi          = {{10.1016/j.ifacol.2022.04.252}},
  volume       = {{55}},
  year         = {{2022}},
}

@article{47961,
  abstract     = {{<jats:p>Due to failures or even the absence of an electricity grid, microgrid systems are becoming popular solutions for electrifying African rural communities. However, they are heavily stressed and complex to control due to their intermittency and demand growth. Demand side management (DSM) serves as an option to increase the level of flexibility on the demand side by scheduling users’ consumption patterns profiles in response to supply. This paper proposes a demand-side management strategy based on load shifting and peak clipping. The proposed approach was modelled in a MATLAB/Simulink R2021a environment and was optimized using the artificial neural network (ANN) algorithm. Simulations were carried out to test the model’s efficacy in a stand-alone PV-battery microgrid in East Africa. The proposed algorithm reduces the peak demand, smoothing the load profile to the desired level, and improves the system’s peak to average ratio (PAR). The presence of deferrable loads has been considered to bring more flexible demand-side management. Results promise decreases in peak demand and peak to average ratio of about 31.2% and 7.5% through peak clipping. In addition, load shifting promises more flexibility to customers.</jats:p>}},
  author       = {{Philipo, Godiana Hagile and Kakande, Josephine Nakato and Krauter, Stefan}},
  issn         = {{1996-1073}},
  journal      = {{Energies}},
  keywords     = {{Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous), Building and Construction}},
  number       = {{14}},
  publisher    = {{MDPI AG}},
  title        = {{{Neural Network-Based Demand-Side Management in a Stand-Alone Solar PV-Battery Microgrid Using Load-Shifting and Peak-Clipping}}},
  doi          = {{10.3390/en15145215}},
  volume       = {{15}},
  year         = {{2022}},
}

@inproceedings{29803,
  abstract     = {{Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and
power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in
the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture
this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the
bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian
optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of
bonding: the constraint is used to check one quality feature of the process and the use of batches leads to
more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding
process that provides very high quality bonds without using a physical model. We also show that the quality
of the Bayesian optimization based control outperforms random search as well as manual search by a user.
Using a simple prior knowledge model derived from data further improves the quality of the connection.
The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control
parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary,
Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward
control without full modeling of the underlying physical processes.}},
  author       = {{Hesse, Michael and Hunstig, Matthias and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM)}},
  isbn         = {{978-989-758-549-4}},
  keywords     = {{Bayesian optimization, Wire bonding, Feed-forward control, model-free design}},
  location     = {{Online}},
  pages        = {{383--394}},
  title        = {{{Batch Constrained Bayesian Optimization for UltrasonicWire Bonding Feed-forward Control Design}}},
  year         = {{2022}},
}

@inbook{33849,
  abstract     = {{Modern traffic control systems are key to cope with current and future traffic challenges. In this paper information obtained from a microscopic traffic estimation using various data sources is used to feed a new developed traffic control approach. The presented method can control a traffic area with multiple traffic light systems (TLS) reacting to individual road users and pedestrians. In contrast to widespread green time extension techniques, this control selects the best phase sequence by analyzing the current traffic state reconstructed in SUMO and its predicted progress. To achieve this, the key aspect of the control strategy is to use Model Predictive Control (MPC). In order to maintain realism for real world applications, among other things, the traffic phase transitions are modelled in detail and integrated within the prediction. For the efficiency, the approach incorporates a fuzzy logic preselection of all phases reducing the computational effort. The evaluation itself is able to be easily adjusted to focus on various objectives like low occupancies, reducing waiting times and emissions, few number of phase transitions etc. determining the best switching times for the selected phases. Exemplary traffic simulations demonstrate the functionality of the MPC-based control and, in addition, some aspects under development like the real-world communication network are also discussed.}},
  author       = {{Malena, Kevin and Link, Christopher and Bußemas, Leon and Gausemeier, Sandra and Trächtler, Ansgar}},
  booktitle    = {{Communications in Computer and Information Science}},
  editor       = {{Klein, Cornel and Jarke, Mathias and Helfert, Markus and Berns, Karsten and Gusikhin, Oleg}},
  isbn         = {{9783031170973}},
  issn         = {{1865-0929}},
  keywords     = {{Traffic control, Traffic estimation, Real-time, MPC, Fuzzy, Isolated intersection, Networked intersection, Sensor fusion}},
  pages        = {{232–254}},
  publisher    = {{Springer International Publishing}},
  title        = {{{Traffic Estimation and MPC-Based Traffic Light System Control in Realistic Real-Time Traffic Environments}}},
  doi          = {{10.1007/978-3-031-17098-0_12}},
  volume       = {{1612}},
  year         = {{2022}},
}

@inproceedings{26389,
  abstract     = {{Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic  examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.}},
  author       = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}},
  booktitle    = {{2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC 2022)}},
  isbn         = {{978-1-6654-5946-4}},
  keywords     = {{Koopman Operator, Nonlinear Control, Extended Dynamic Mode Decomposition, Hybrid Modelling}},
  location     = {{Cairo, Egypt}},
  pages        = {{1--9}},
  title        = {{{Data-Driven Models for Control Engineering Applications Using the Koopman Operator}}},
  doi          = {{10.1109/AIRC56195.2022.9836980}},
  year         = {{2022}},
}

@article{50071,
  author       = {{Junker, Annika and Timmermann, Julia and Trächtler, Ansgar}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{Control and Systems Engineering}},
  number       = {{12}},
  pages        = {{389--394}},
  publisher    = {{Elsevier BV}},
  title        = {{{Learning Data-Driven PCHD Models for Control Engineering Applications*}}},
  doi          = {{10.1016/j.ifacol.2022.07.343}},
  volume       = {{55}},
  year         = {{2022}},
}

@inproceedings{25278,
  abstract     = {{Using Service Function Chaining (SFC) in wireless networks became popular in many domains like networking and multimedia. It relies on allocating network resources to incoming SFCs requests, via a Virtual Network Embedding (VNE) algorithm, so that it optimizes the performance of the SFC. When the load of incoming requests -- competing for the limited network resources -- increases, it becomes challenging to decide which requests should be admitted and which one should be rejected. In this work, we propose a deep Reinforcement learning (RL) solution that can learn the admission policy for different dependencies, such as the service lifetime and the priority of incoming requests. We compare the deep RL solution to a first-come-first-serve baseline that admits a request whenever there are available resources. We show that deep RL outperforms the baseline and provides higher acceptance rate with low rejections even when there are enough resources.}},
  author       = {{Afifi, Haitham and Sauer, Fabian Jakob and Karl, Holger}},
  booktitle    = {{2021 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS) (ANTS'21)}},
  keywords     = {{reinforcement learning, admission control, wireless sensor networks}},
  title        = {{{Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding}}},
  year         = {{2021}},
}

@article{29543,
  author       = {{Djema, Walid and Giraldi, Laetitia and Maslovskaya, Sofya and Bernard, Olivier}},
  issn         = {{0005-1098}},
  journal      = {{Automatica}},
  keywords     = {{Electrical and Electronic Engineering, Control and Systems Engineering}},
  publisher    = {{Elsevier BV}},
  title        = {{{Turnpike features in optimal selection of species represented by quota models}}},
  doi          = {{10.1016/j.automatica.2021.109804}},
  volume       = {{132}},
  year         = {{2021}},
}

@inproceedings{29938,
  abstract     = {{Modular solid-state transformers (SSTs) are a promising technology in converting power from a 10kV three-phase medium voltage to a lower DC-voltage in the range of 100…400V to provide pure DC power to applications such as electrolyzers for hydrogen generation, data centers with a DC power distribution and DC micro grids. Modular SSTs which can be interpreted as modular multilevel converters with an isolated DC-DC output stage per module, are designed with redundant modules to increase reliability. Usually, each of the three arms operates independently, and therefore, only a fixed number of faulty modules can be compensated in each arm, even if all modules are operational in the remaining two arms. With the proposed zero-sequence voltage injection, up to 100% more faulty modules can be compensated in an arm by employing the same hardware. In addition, module power imbalances are nearly eliminated by utilizing a fundamental frequency zero-sequence voltage. A dominant 3rd harmonic zero-sequence voltage injection in combination with the 5th, 7th and several higher order harmonics with adaptive (small) amplitudes minimize the required arm voltages at steady-state. For nominal operation or symmetrical faults, the proposed technique is equivalent to the well known Min-Max voltage injection, which already reduces the peak arm voltage by 13.4% compared to a constant star point potential. A statistical analysis proves, that the expected number of tolerable faulty modules of the 1MW SST increases by 12% without the need for additional hardware.}},
  author       = {{Unruh, Roland and Lange, Jarren and Schafmeister, Frank and Böcker, Joachim}},
  booktitle    = {{23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe)}},
  isbn         = {{978-9-0758-1537-5}},
  keywords     = {{Solid-State Transformer, Zero sequence voltage, Fault handling strategy, Power balance control technique, Three-phase system}},
  location     = {{Ghent, Belgium}},
  publisher    = {{IEEE}},
  title        = {{{Adaptive Zero-Sequence Voltage Injection for Modular Solid-State Transformer to Compensate for Asymmetrical Fault Conditions}}},
  doi          = {{https://doi.org/10.23919/EPE21ECCEEurope50061.2021.9570542}},
  year         = {{2021}},
}

@article{33658,
  abstract     = {{<jats:p>We demonstrate how to fully ascribe Raman peaks simulated using ab initio molecular dynamics to specific vibrations in the structure at finite temperatures by means of Wannier functions. Here, we adopt our newly introduced method for the simulation of the Raman spectra in which the total polarizability of the system is expressed as a sum over Wannier polarizabilities. The assignment is then based on the calculation of partial Raman activities arising from self- and/or cross-correlations between different types of Wannier functions in the system. Different types of Wannier functions can be distinguished based on their spatial spread. To demonstrate the predictive power of this approach, we applied it to the case of a cyclohexane molecule in the gas phase and were able to fully assign the simulated Raman peaks.</jats:p>}},
  author       = {{Partovi-Azar, Pouya and Kühne, Thomas}},
  issn         = {{2072-666X}},
  journal      = {{Micromachines}},
  keywords     = {{Electrical and Electronic Engineering, Mechanical Engineering, Control and Systems Engineering}},
  number       = {{10}},
  publisher    = {{MDPI AG}},
  title        = {{{Full Assignment of Ab-Initio Raman Spectra at Finite Temperatures Using Wannier Polarizabilities: Application to Cyclohexane Molecule in Gas Phase}}},
  doi          = {{10.3390/mi12101212}},
  volume       = {{12}},
  year         = {{2021}},
}

@article{35575,
  author       = {{Schulze Darup, Moritz and Alexandru, Andreea B. and Quevedo, Daniel E. and Pappas, George J.}},
  issn         = {{1066-033X}},
  journal      = {{IEEE Control Systems}},
  keywords     = {{Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering, Electrical and Electronic Engineering, Modeling and Simulation, Control and Systems Engineering}},
  number       = {{3}},
  pages        = {{58--78}},
  publisher    = {{Institute of Electrical and Electronics Engineers (IEEE)}},
  title        = {{{Encrypted Control for Networked Systems: An Illustrative Introduction and Current Challenges}}},
  doi          = {{10.1109/mcs.2021.3062956}},
  volume       = {{41}},
  year         = {{2021}},
}

@article{35576,
  author       = {{Schulze Darup, Moritz and Klädtke, Manuel and Mönnigmann, Martin}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{Control and Systems Engineering}},
  number       = {{6}},
  pages        = {{290--295}},
  publisher    = {{Elsevier BV}},
  title        = {{{Exact solution to a special class of nonlinear MPC problems}}},
  doi          = {{10.1016/j.ifacol.2021.08.559}},
  volume       = {{54}},
  year         = {{2021}},
}

@article{35578,
  author       = {{Faulwasser, Timm and Lucia, Sergio and Schulze Darup, Moritz and Mönnigmann, Martin}},
  issn         = {{2405-8963}},
  journal      = {{IFAC-PapersOnLine}},
  keywords     = {{Control and Systems Engineering}},
  number       = {{6}},
  pages        = {{238--243}},
  publisher    = {{Elsevier BV}},
  title        = {{{Teaching MPC: Which Way to the Promised Land?}}},
  doi          = {{10.1016/j.ifacol.2021.08.551}},
  volume       = {{54}},
  year         = {{2021}},
}