| 51. |
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| 52. |
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| 53. |
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| 54. |
- Cronrath, Constantin Christian Justin, 1990, et al.
(författare)
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Energy reduction in paint shops through energy-sensitive on-off control
- 2016
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. - 9781509024094 ; , s. 1282-1288
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Konferensbidrag (refereegranskat)abstract
- Energy efficiency is the key for a sustainable manufacturing. However, energy efficiency measures often struggle with barriers, which inhibit their implementation. Environmental discrete event simulation is an approach increasingly discussed in research to overcome such barriers. Paint shops, which are responsible for 50-70% of an assembly plant's energy utilization in vehicular manufacturing, are seldom considered in this research, though. Therefore, the specific requirements on an energy consumption model for painting systems were investigated and implemented in simulation software. In addition, an energy-sensitive algorithm is proposed to reveal energy efficiency potentials based on an on-off control strategy. The result is a comprehensive simulation concept, that contributes to the removal of energy efficiency barriers by enabling a detailed evaluation of improvement measures and by indicating worthwhile saving potentials. A case study for a typical paint shop shows that the total energy saving potential is 7% in total, corresponding to more than 300 MWh of annual savings. © 2016 IEEE.
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| 55. |
- Cronrath, Constantin, 1990, et al.
(författare)
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Enhancing digital twins through reinforcement learning
- 2019
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2019-August, s. 293-298
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Konferensbidrag (refereegranskat)abstract
- Digital Twins are core enablers of smart and autonomous manufacturing systems. Although they strive to represent their physical counterpart as accurately as possible, slight model or data errors will remain. We present an algorithm to compensate for those residual errors through Reinforcement Learning (RL) and data fed back from the manufacturing system. When learning, the Digital Twin acts as teacher and safety policy to ensure minimal performance. We test the algorithm in a sheet metal assembly context, in which locators of the fixture are optimally adjusted for individual assemblies. Our results show a fast adaption and improved performance of the autonomous system.
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| 56. |
- Cronrath, Constantin, 1990, et al.
(författare)
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Formal Properties of the Digital Twin-Implications for Learning, Optimization, and Control
- 2020
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2020-August, s. 679-684
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Konferensbidrag (refereegranskat)abstract
- Digital twins are regarded as enablers of smart and autonomous manufacturing systems. The digital twin concept essentially refers to a ultra-realistic digital model of a products or system, coupled by a bidirectional automated data exchange, used for simulation, optimization, and control. Although the concept has gained significant attention, its conceptual basis is still weak. We review common definitions and descriptions of digital twins and refine the concept in system theoretic terms. With this sharpened perspective on digital twins, we sketch out three basic problems that need to be solved to turn a digital model into a digital twin. To that end, we call attention to challenges that need to be researched into.
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| 57. |
- Cronrath, Constantin, 1990, et al.
(författare)
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How Useful is Learning in Mitigating Mismatch Between Digital Twins and Physical Systems?
- 2024
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Ingår i: IEEE Transactions on Automation Science and Engineering. - 1558-3783 .- 1545-5955. ; 21:1, s. 758-770
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Tidskriftsartikel (refereegranskat)abstract
- In the control of complex systems, we observe two diametrical trends: model-based control derived from digital twins, and model-free control through AI. There are also attempts to bridge the gap between the two by incorporating learning-based AI algorithms into digital twins to mitigate mismatches between the digital twin model and the physical system. One of the most straightforward approaches to this is direct input adaptation. In this paper, we ask whether it is useful to employ a generic learning algorithm in such a setting, and our conclusion is "not very". We denote an algorithm to be more useful than another algorithm based on three aspects: 1) it requires fewer data samples to reach a desired minimal performance, 2) it achieves better performance for a reasonable number of data samples, and 3) it accumulates less regret. In our evaluation, we randomly sample problems from an industrially relevant geometry assurance context and measure the aforementioned performance indicators of 16 different algorithms. Our conclusion is that blackbox optimization algorithms, designed to leverage specific properties of the problem, generally perform better than generic learning algorithms, once again finding that "there is no free lunch".
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| 58. |
- Cronrath, Constantin, 1990, et al.
(författare)
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Relevant Safety Falsification by Automata Constrained Reinforcement Learning
- 2022
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Ingår i: IEEE International Conference on Automation Science and Engineering. - 2161-8070 .- 2161-8089. ; 2022-August, s. 2273-2280
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Konferensbidrag (refereegranskat)abstract
- Complex safety-critical cyber-physical systems, such as autonomous cars or collaborative robots, are becoming increasingly common. Simulation-based falsification is a testing method for uncovering safety hazards of such systems already in the design phase. Conventionally, the falsification method takes the form of a static optimization. Recently, dynamic optimization methods such as reinforcement learning have gained interest for their ability to uncover harder-to-find safety hazards. However, these methods may converge to risk-maximising, but irrelevant behaviors. This paper proposes a principled formulation and solution of the falsification problem by automata constrained reinforcement learning, in which rewards for relevant behavior are tuned via Lagrangian relaxation. The challenges and proposed methods are demonstrated in a use-case example from the domain of industrial human-robot collaboration, where falsification is used to identify hazardous human worker behaviors that result in human-robot collisions. Compared to random sampling and conventional approximate Q-learning, we show that the proposed method generates equally hazardous, but at the same time more relevant testing conditions that expose safety flaws.
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| 59. |
- Danielsson, Kristin, 1976, et al.
(författare)
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Automatic Scheduling and Verification of the Control Function of Flexible Assembly Cells in an Information Reuse Environment
- 2005
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Ingår i: Proc. of the IEEE Symposium on Assembly and Task Planning. - 0780390806 ; , s. 80-85
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Konferensbidrag (refereegranskat)abstract
- Formal synthesis and verification of control programs in manufacturing applications are currently undeveloped areas. Today, new control programs are most often debugged on the shop floor, and when formal methods are used, the result is often a control function that is impossible to interpret by the operators. We present a method for synthesis of the control function of a PLC program. By combining supervisory control theory with a hierarchical program structure, in which the control function is separated from the rest of the PLC program, the generated control function combines the benefits of a traditional supervisor, e.g. non-blocking, optimality and flexibility, with simplicity and clearness. The traditional coding is replaced by information reuse and configuration of program components, instantiated from a software library. © 2005 IEEE.
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