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| Format: | Preprint |
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2021
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| Online Access: | https://arxiv.org/abs/2109.09055 |
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| _version_ | 1866929342388371456 |
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| author | Malikopoulos, Andreas |
| author_facet | Malikopoulos, Andreas |
| contents | Cyber-physical systems (CPS), in most instances, represent systems of systems with an informationally decentralized structure such as emerging mobility systems, networked control systems, sustainable manufacturing, smart power grids, power systems, mobility markets, social media platforms, cooperation of robots, and internet of things. To optimize the operation of such systems, we typically assume an ideal model. Such model-based control approaches cannot effectively facilitate optimal solutions with performance guarantees due to the discrepancy between the model and the actual CPS. On the other hand, in most CPS there is a large volume of data with a dynamic nature which is added to the system gradually in real time and not altogether in advance. Thus, traditional supervised learning approaches cannot always facilitate robust solutions using data derived offline. By contrast, applying reinforcement learning approaches directly to the actual CPS might impose significant implications on the safety and robust operation of the system. The overarching goal of the Information and Decision Science (IDS) Lab is to investigate how to circumvent these challenges by developing data-driven system approaches at the intersection of learning and control. The emphasis is on how to improve energy efficiency and reduce greenhouse gas emissions in applications related to emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, sociotechnical systems, and smart cities, and thus contribute to the health of the planet. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2109_09055 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Combining Learning and Control for Data-driven Approaches of Cyber-Physical Systems Malikopoulos, Andreas Optimization and Control Cyber-physical systems (CPS), in most instances, represent systems of systems with an informationally decentralized structure such as emerging mobility systems, networked control systems, sustainable manufacturing, smart power grids, power systems, mobility markets, social media platforms, cooperation of robots, and internet of things. To optimize the operation of such systems, we typically assume an ideal model. Such model-based control approaches cannot effectively facilitate optimal solutions with performance guarantees due to the discrepancy between the model and the actual CPS. On the other hand, in most CPS there is a large volume of data with a dynamic nature which is added to the system gradually in real time and not altogether in advance. Thus, traditional supervised learning approaches cannot always facilitate robust solutions using data derived offline. By contrast, applying reinforcement learning approaches directly to the actual CPS might impose significant implications on the safety and robust operation of the system. The overarching goal of the Information and Decision Science (IDS) Lab is to investigate how to circumvent these challenges by developing data-driven system approaches at the intersection of learning and control. The emphasis is on how to improve energy efficiency and reduce greenhouse gas emissions in applications related to emerging mobility systems, e.g., connected and automated vehicles (CAVs), shared mobility, sociotechnical systems, and smart cities, and thus contribute to the health of the planet. |
| title | Combining Learning and Control for Data-driven Approaches of Cyber-Physical Systems |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2109.09055 |