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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Julius Lorenz Anselm Klose Hannes Lange Tobias Kock Leon Urbas Chair of Process Control Systems TUD Dresden University of Technology Dresden Germany Process to Order Lab TUD Dresden University of Technology Dresden Germany Process Systems Engineering Group TUD Dresden University of Technology Dresden Germany

The scale up of hydrogen production to large scale production systems demands a structured approach to control these systems. With suitable control principles, optimization for operation can be achieved whilst maintaining flexibility. In this paper, existing control strategies are analyzed and compared for large scale electrolysis systems. It is argued, how modularization can enable the optimal distribution of set-points and enables the systems for capacity extension. An initial implementation in MATLAB is presented, showing the feasibility of the proposed power distribution concept.

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Flexible process control for Scalable Electrolysis systems

Flexible Process Control for Scalable Electrolysis Systems

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Electrical, Computer and Energy Technologies (ICECET), International Conference on

Large-scale water electrolysis systems are built from a multitude of stack units. These stack units have to be controlled during operation. The question arises how process control of the system can be designed in that way, that scalable, extendable but also optimal operation of the stack units becomes possible. To achieve this goal, existing process control strategies from literature are analyzed. The requirements resulting from the inherent numbering up of stack units are described and an approach for an adaptable optimized process control strategy considering the system properties is proposed. The proposed process control strategy is implemented in a modeled electrolysis system and evaluated regarding the stated criteria. The results could show, that the inherent modularization of electrolysis systems can be used in advantage for a scalable operation.

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Indistinguishability analysis and observer design for size-structured cell populations

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IFAC-PapersOnLine 2023年第2期56卷 581-586页

作者： P. Jerono J.J. Winkin A. Vande Wouwer A. Schaum Digital Process Engineering Group (DPE) Karlsruhe Institute of Technology Germany Department of Mathematics and Institute for Complex Systems (naXys) University of Namur Belgium Systems Estimation Control and Optimization (SECO) University of Mons Belgium Chair of Automation and Control (ACON) Kiel University Germany

The observability property of population balance equations with biomass measurements is addressed within the framework of indistinguishable trajectories. The population balance equation is described by a partial integro-differential equation which is coupled with an ordinary differential equation for the nutrient dynamics. For the semi-discretized model equations, the dynamics of indistinguishable trajectories are derived and evaluated for the special case of equal partitioning at cell division revealing that the observability property is guaranteed as long as the nutrient concentration is not depleted. Based on these results, an observer is designed and tested in simulation to estimate the cell population distribution using biomass measurements in a batch bioreactor for the case of a bi-structured population.

关键词： Observability indistinguishable trajectories cell population dynamics

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A Numerical Study on the Parallelization of Dual Decomposition-based Distributed Mixed-Integer Programming

A Numerical Study on the Parallelization of Dual Decompositi...

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European control Conference (ECC)

作者： Mario Klostermeier Vassilios Yfantis Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems University of Kaiserslautern-Landau Kaiserslautern Germany German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

The shift from centralized to decentralized systems is increasing the complexity of many problems in control and optimization. However, it also presents the opportunity to exploit parallelized computational schemes. This paper shows how the solution process of mixed-integer problems, which often arise in areas like production scheduling or logistics, can be supported by employing parallel computations. To this end, dual variables are introduced that enable the decomposition of these complex problems into subproblems that can then be solved in parallel. The presented dual decomposition-based approach provides a lower bound for the optimal solution of the original problem, which can support the overall solution process. The focus of this paper is on the parallelizability of the computation of this lower bound. The bounds from three different dual decomposition- based distributed optimization algorithms are compared to the lower bounds provided by several commercial solvers within their branch-&-cut framework.

关键词： processor scheduling Europe Production Programming Complexity theory Optimization Logistics

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Model Predictive control Based Reference Generation for Optimal Proportional Integral Derivative control

Model Predictive Control Based Reference Generation for Opti...

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Mediterranean Conference on control and Automation (MED)

作者： Fatos Gashi Khalil Abuibaid Martin Ruskowski Achim Wagner German Research Center for Artificial Intelligence (DFKI) Kaiserslautern Germany Department of Mechanical and Process Engineering Chair of Machine Tools and Control Systems RPTU Kaiserslautern-Landau Kaiserslautern Germany

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

We introduce an alternative approach towards optimal proportional integral derivative (PID) control, consisting of model predictive control (MPC) based reference generation. To this end, we have integrated the reference as part of optimization variables of the resulting problem, where a deliberate sequence of errors is induced to obtain an optimal PID control action. In addition, the desired behavior of the PID controller is achieved without the need for internal modification of the PID gains. To better highlight the ability of coping with poor PID tuning, several test cases consisting of progressively degraded PID gains are presented. Validation of the proposed strategy is displayed by comprehensive simulations using two different plants.

关键词： PI control Automation PD control Tuning Optimization Predictive control

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Stochastic Online Optimization for Cyber-Physical and Robotic systems

arXiv

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arXiv 2024年

作者： Ma, Hao Zeilinger, Melanie Muehlebach, Michael Learning and Dynamical Systems Group Max Planck Institute for Intelligent Systems Max-Planck-Ring 4 Tübingen Germany Department of Mechanical and Process Engineering Institute for Dynamic Systems and Control Leonhardstrasse 21 Zürich8092 Switzerland

We propose a novel gradient-based online optimization framework for solving stochastic programming problems that frequently arise in the context of cyber-physical and robotic systems. Our problem formulation accommodates constraints that model the evolution of a cyber-physical system, which has, in general, a continuous state and action space, is nonlinear, and where the state is only partially observed. We also incorporate an approximate model of the dynamics as prior knowledge into the learning process and show that even rough estimates of the dynamics can significantly improve the convergence of our algorithms. Our online optimization framework encompasses both gradient descent and quasi-Newton methods, and we provide a unified convergence analysis of our algorithms in a non-convex setting. We also characterize the impact of modeling errors in the system dynamics on the convergence rate of the algorithms. Finally, we evaluate our algorithms in simulations of a flexible beam, a four-legged walking robot, and in real-world experiments with a ping-pong playing robot. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Towards Digital Twins for Power-to-X processes: Comparing Surrogate Models for a Catalytic CO2 Methanation Reactor

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IEEE Transactions on Automation Science and engineering 2025年

作者： Peterson, Luisa Forootani, Ali Medina, Edgar Ivan Sanchez Gosea, Ion Victor Sundmacher, Kai Benner, Peter Group Magdeburg Germany Group Magdeburg Germany Otto von Guericke University Magdeburg Chair for Process Systems Engineering Magdeburg Germany Otto von Guericke University Magdeburg Germany

Power-to-X processes convert excess renewable energy into storable fuels such as methane, addressing critical energy storage and transportation challenges. However, implementing these processes presents operational difficulties, particularly in managing fluctuating energy inputs. Digital Twins, or virtual replicas of physical systems, offer a promising solution by enabling model-based strategies for optimization and control. However, traditional mechanistic models are computationally intensive, limiting their application in real-time scenarios. This highlights the need for surrogate models that are computationally efficient and capable of accurately capturing the essential dynamics of complex systems. In this study, we evaluate and compare advanced techniques for modeling spatio-temporal processes, including a graph attention network (GAT), operator inference (OpInf) with stability guarantees, and sparse identification with greedy sampling (GN-SINDy). These methods are evaluated for their ability to model the dynamic operation and control of a catalytic CO2 methanation reactor. By evaluating their accuracy and computational efficiency, we aim to identify surrogate models tailored to spatio-temporal systems, advance the development of Digital Twins, and improve renewable energy conversion. © 2004-2012 IEEE.

关键词： Gas fuel storage

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The need for neuromorphic computing in industrial robotics

The need for neuromorphic computing in industrial robotics

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Artificial Intelligence and Power engineering (AIPE), International Conference on

作者： Tapanta Bhanja Jonathan Nußbaum Khalil Abuibaid Tatjana Legler Achim Wagner Martin Ruskowski Department of Mechanical and Process Engineering Chair for Machine Tools and Control Systems University of Kaiserslautern-Landau Kaiserslautern Germany Innovative Factory Systems German Research Centre for Artificial Intelligence Kaiserslautern Germany

With Moore’s law coming to its limits, the rate of increase in compute power available for processing applications is similarly coming to a halt. This implies that the compute intensive tasks, such as robotics, artificial intelligence, and high-performance space computing need innovative ways to cater their ever-increasing compute needs. One innovative way to solve computational bottlenecks is to bring compute and memory together, as opposed to the Von Neumann computational model, with greater degree of parallelism in an event-based, asynchronous computation paradigm. Neuromorphic computing is one such paradigm that draws its inspiration from the brain. Energy and computational efficiency, asynchronous and event-based processing being its salient features, neuromorphic computing is an area worth exploring for compute intensive tasks. In this paper, the authors explore the possibilities and benefits of neuromorphic computing in robotics, and establish possible research directions that could benefit the robotics domain.

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Explorative Application of Discrete Bayesian Networks as Surrogate Models for Energy systems Analysis

SSRN

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SSRN 2024年

作者： Kelk, Rainer Podofillini, Luca Dang, Vinh N. Panos, Evangelos Centers for Nuclear Engineering and Science and for Energy and Environmental Sciences Laboratory for Energy Systems Analysis Paul Scherrer Institute PSI Villigen Switzerland Chair of Energy Systems Analysis Institute of Energy and Process Engineering ETH Zurich Zurich8092 Switzerland Energy Economics Group

This work investigates the application of data-based discrete Bayesian Belief Networks (BBNs) as surrogate energy system models for result analysis and interactive analyses, such as what-if analyses. A method to optimize the BBN model is devised, based on performance metrics related to the accuracy of the BBN predictions, relative to the energy systems model, a simplified version of the Swiss TIMES (STEM) model, referred to as STEM-lite. The validation of the BBN model on a set of predetermined scenarios yielded an average relative error below 2%, suggesting that BBNs can perform adequately as energy system model surrogates. In a final step, the application of the surrogate BBN model is demonstrated to highlight its benefits, which include enabling interactive analysis (supported by the visualization of key variables, their relationships and interactions), fast and intuitive uncertainty propagation, and support for goal-driven analysis (backward reasoning from outcomes to the inputs that produce these outcomes). The surrogate BBN presented here was developed to elaborate the methods for constructing, validating, and using BBN models for energy systems analysis and to demonstrate the benefits of such a model;at this stage, this model is not intended for energy systems and economics policy discussions. For practical applications, future work is needed to reduce the number of data records to construct the surrogate BBN, to reintroduce the option to treat the time dependence of the input variables, and to allow for larger BBN models (involving more variables) that reflect the increasing complexity of energy systems. © 2024, The Authors. All rights reserved.

关键词： Hierarchical systems

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Evolutionary Algorithm-based Optimal Parametrization of Multi-objective Mixed-integer Linear Programming Scheduling Models

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IFAC-PapersOnLine 2023年第2期56卷 5382-5387页

作者： Vassilios Yfantis Alexander Babskiy Bastian Dörig Thorsten Winterer Achim Wagner Martin Ruskowski Chair of Machine Tools and Control Systems Department of Mechanical and Process Engineering RPTU Kaiserslautern-Landau Kaiserslautern D-67663 Germany flexis AG Stuttgart D-70565 Germany Innovative Factory Systems German Research Center for Artificial Intelligence (DFKI) Kaiserslautern D-67663 Germany

Modern manufacturing systems strive to optimize many different key performance indicators. Additionally, mathematical programming-based scheduling models enable the explicit considerations of constraints. However, due to the ever-increasing demand for customized products it is not guaranteed that all constraints can be met at the same time. An approach to circumvent this problem of infeasibility is to replace the constraints by soft-constraints, i.e., to penalize their violation in the objective function. The presence of multiple competing objectives and soft-constraints gives rise to multi-objective optimization problems, where a decision maker has to weigh and balance the different objectives and soft-constraints according to his/her preferences and priorities. Ideally the values of all objectives and soft-constraints should lie within the same order of magnitude, making it easy to weigh them against each other. However, for heterogeneous objectives and soft-constraints with different scales and units this might be challenging. This paper presents an evolutionary algorithm for the optimal parametrization of multi-objective mixed-integer linear programming-based scheduling models. The goal of the evolutionary algorithm is to compute weights for the different terms of the objective which lead to a balanced influence on the overall objective at the optimum. Furthermore, the algorithm is extended by introducing an a priori weighing of the objectives in the fitness function of the evolutionary algorithm. The method is demonstrated on a scheduling problem in which a given set of orders has to be allocated to machines within a manufacturing environment.

关键词： Evolutionary Algorithm Multi-objective optimization Scheduling

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