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Papadopoulos, AlessandroORCID iD iconorcid.org/0000-0002-1364-8127
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Publications (10 of 36) Show all publications
Ioli, D., Falsone, A., Papadopoulos, A. & Prandini, M. (2019). A compositional modeling framework for the optimal energy management of a district network. Journal of Process Control, 74, 160-176
Open this publication in new window or tab >>A compositional modeling framework for the optimal energy management of a district network
2019 (English)In: Journal of Process Control, ISSN 0959-1524, E-ISSN 1873-2771, Vol. 74, p. 160-176Article in journal (Refereed) Published
Abstract [en]

This paper proposes a compositional modeling framework for the optimal energy management of a district network. The focus is on cooling of buildings, which can possibly share resources to the purpose of reducing maintenance costs and using devices at their maximal efficiency. Components of the network are described in terms of energy fluxes and combined via energy balance equations. Disturbances are accounted for as well, through their contribution in terms of energy. Different district configurations can be built, and the dimension and complexity of the resulting model will depend both on the number and type of components and on the adopted disturbance description. Control inputs are available to efficiently operate and coordinate the district components, thus enabling energy management strategies to minimize the electrical energy costs or track some consumption profile agreed with the main grid operator.

Place, publisher, year, edition, pages
Elsevier Ltd, 2019
Keywords
Building thermal regulation, Compositional systems, Energy management, Smart grid modeling, Electric power transmission networks, Compositional modeling, Cooling of buildings, District networks, Electrical energy costs, Energy balance equations, Energy management strategies, Smart grid, Thermal regulation, Smart power grids
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-43055 (URN)10.1016/j.jprocont.2017.10.005 (DOI)000465050900015 ()2-s2.0-85033227619 (Scopus ID)
Available from: 2019-04-10 Created: 2019-04-10 Last updated: 2019-05-09Bibliographically approved
Johansson, B., Leander, B., Causevic, A., Papadopoulos, A. & Nolte, T. (2019). Classification of PROFINET I/O Configurations utilizing Neural Networks. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA: . Paper presented at 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019 (pp. 1321-1324). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Classification of PROFINET I/O Configurations utilizing Neural Networks
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2019 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1321-1324Conference paper, Published paper (Refereed)
Abstract [en]

In process automation installations, the I/O system connect the field devices to the process controller over a fieldbus, a reliable, real-time capable communication link with signal values cyclical being exchanged with a 10-100 millisecond rate. If a deviation from intended behaviour occurs, analyzing the potentially vast data recordings from the field can be a time consuming and cumbersome task for an engineer. For the engineer to be able to get a full understanding of the problem, knowledge of the used I/O configuration is required. In the problem report, the configuration description is sometimes missing. In such cases it is difficult to use the recorded data for analysis of the problem.In this paper we present our ongoing work towards using neural network models as assistance in the interpretation of an industrial fieldbus communication recording. To show the potential of such an approach we present an example using an industrial setup where fieldbus data is collected and classified. In this context we present an evaluation of the suitability of different neural net configurations and sizes for the problem at hand.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Field devices, Fieldbus, In-process, Neural network model, Process controllers, PROFInet, Real time, Signal value, Factory automation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-47118 (URN)10.1109/ETFA.2019.8869024 (DOI)2-s2.0-85074197516 (Scopus ID)9781728103037 (ISBN)
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019
Available from: 2020-02-20 Created: 2020-02-20 Last updated: 2020-02-20Bibliographically approved
Struhar, V., Ashjaei, S. M., Behnam, M., Craciunas, S. & Papadopoulos, A. (2019). DART: Dynamic Bandwidth Distribution Framework for Virtualized Software Defined Networks. In: IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19: . Paper presented at IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 14 Oct 2019, Lisbon, Portugal.
Open this publication in new window or tab >>DART: Dynamic Bandwidth Distribution Framework for Virtualized Software Defined Networks
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2019 (English)In: IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 2019Conference paper, Published paper (Refereed)
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-45054 (URN)
Conference
IEEE 45th Annual Conference of the Industrial Electronics Society IECON'19, 14 Oct 2019, Lisbon, Portugal
Projects
Future factories in the CloudFORA - Fog Computing for Robotics and Industrial Automation
Available from: 2019-08-22 Created: 2019-08-22 Last updated: 2019-08-22Bibliographically approved
Leva, A., Papadopoulos, A., Seva, S. & Cimino, C. (2019). Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection. Industrial & Engineering Chemistry Research, 58(51), 23211-23224
Open this publication in new window or tab >>Explicit Model-Based Real PID Tuning for Efficient Load Disturbance Rejection
2019 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 58, no 51, p. 23211-23224Article in journal (Refereed) Published
Abstract [en]

In the process control, many PID loops are primarily devoted to rejecting load disturbances, and some of them are crucial for the quality of the overall plant operation. In such a scenario, automatic tuning is highly desired. However, load disturbance rejection calls for strong feedback up to quite high frequencies with respect to the dominant plant dynamics, on which most tuning rules are centered. As such it is difficult for a rule to yield good and, above all, uniform results in the face of all the various process structures it can be confronted with. In this paper, we propose an explicit model-based PID tuning rule specifically targeted at the problem just evidenced. The rule minimizes the magnitude of the nominal disturbance-to-output frequency response, at the same time preventing that magnitude to exhibit a peak or a plateau around its maximum. This characteristic, together with tuning the PID derivative filter, leads to sharp disturbance rejection without incurring in an excessive control sensitivity to high-frequency measurement noise and mitigates the problems caused by heterogeneous process dynamics. The proposed approach is assessed by comparing the rule with selected counterparts, on a literature benchmark with different process structures. A laboratory experiment is finally presented to show that our rule can withstand real-world operating conditions.

Place, publisher, year, edition, pages
AMER CHEMICAL SOC, 2019
National Category
Energy Systems
Identifiers
urn:nbn:se:mdh:diva-46804 (URN)10.1021/acs.iecr.9b04198 (DOI)000505632500052 ()2-s2.0-85076988681 (Scopus ID)
Available from: 2020-01-23 Created: 2020-01-23 Last updated: 2020-02-20Bibliographically approved
Miloradović, B., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). Extended colored traveling salesperson for modeling multi-agent mission planning problems. In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems: . Paper presented at 8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019 (pp. 237-244). SciTePress
Open this publication in new window or tab >>Extended colored traveling salesperson for modeling multi-agent mission planning problems
2019 (English)In: ICORES 2019 - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems, SciTePress , 2019, p. 237-244Conference paper, Published paper (Refereed)
Abstract [en]

In recent years, multi-agent systems have been widely used in different missions, ranging from underwater to airborne. A mission typically involves a large number of agents and tasks, making it very hard for the human operator to create a good plan. A search for an optimal plan may take too long, and it is hard to make a time estimate of when the planner will finish. A Genetic algorithm based planner is proposed in order to overcome this issue. The contribution of this paper is threefold. First, an Integer Linear Programming (ILP) formulation of a novel Extensive Colored Traveling Salesperson Problem (ECTSP) is given. Second, a new objective function suitable for multi-agent mission planning problems is proposed. Finally, a reparation algorithm to allow usage of common variation operators for ECTSP has been developed. 

Place, publisher, year, edition, pages
SciTePress, 2019
Keywords
Colored traveling salesperson (CTSP), Genetic algorithms, Multi-agent mission planning, Integer programming, Operations research, Software agents, Human operator, Integer Linear Programming, Mission planning, Mission planning problem, Objective functions, Traveling salesperson problem, Variation operator, Multi agent systems
National Category
Computer Sciences
Identifiers
urn:nbn:se:mdh:diva-43305 (URN)2-s2.0-85064712559 (Scopus ID)9789897583520 (ISBN)
Conference
8th International Conference on Operations Research and Enterprise Systems, ICORES 2019, 19 February 2019 through 21 February 2019
Available from: 2019-05-09 Created: 2019-05-09 Last updated: 2019-10-01Bibliographically approved
Salman, C. A., Struhar, V., Papadopoulos, A., Behnam, M. & Nolte, T. (2019). Fogification of industrial robotic systems: Research challenges. In: IoT-Fog 2019 - Proceedings of the 2019 Workshop on Fog Computing and the IoT: . Paper presented at 2019 Workshop on Fog Computing and the IoT, IoT-Fog 2019, 15 April 2019 (pp. 41-45). Association for Computing Machinery, Inc
Open this publication in new window or tab >>Fogification of industrial robotic systems: Research challenges
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2019 (English)In: IoT-Fog 2019 - Proceedings of the 2019 Workshop on Fog Computing and the IoT, Association for Computing Machinery, Inc , 2019, p. 41-45Conference paper, Published paper (Refereed)
Abstract [en]

To meet the demands of future automation systems, the architecture of traditional control systems such as the industrial robotic systems needs to evolve and new architectural paradigms need to be investigated. While cloud-based platforms provide services such as computational resources on demand, they do not address the requirements of real-time performance expected by control applications. Fog computing is a promising new architectural paradigm that complements the cloud-based platform by addressing its limitations. In this paper, we analyse the existing robot system architecture and propose a fog-based solution for industrial robotic systems that addresses the needs of future automation systems. We also propose the use of Time-Sensitive Networking (TSN) services for real-time communication and OPC-UA for information modelling within this architecture. Additionally, we discuss the main research challenges associated with the proposed architecture.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc, 2019
Keywords
Automation, Computer architecture, Fog, Industrial research, Internet of things, Robotics, Cloud based platforms, Computational resources, Control applications, Industrial robotic systems, Information modelling, Proposed architectures, Real time performance, Real-time communication, Fog computing
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:mdh:diva-43888 (URN)10.1145/3313150.3313225 (DOI)000473542200009 ()2-s2.0-85066045184 (Scopus ID)9781450366984 (ISBN)
Conference
2019 Workshop on Fog Computing and the IoT, IoT-Fog 2019, 15 April 2019
Available from: 2019-06-11 Created: 2019-06-11 Last updated: 2019-10-11Bibliographically approved
Wang, W., Mosse, D. & Papadopoulos, A. (2019). Packet priority assignment for wireless control systems of multiple physical systems. In: Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019: . Paper presented at 22nd IEEE International Symposium on Real-Time Distributed Computing, ISORC 2019, 7 May 2019 through 9 May 2019 (pp. 143-150). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Packet priority assignment for wireless control systems of multiple physical systems
2019 (English)In: Proceedings - 2019 IEEE 22nd International Symposium on Real-Time Distributed Computing, ISORC 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 143-150Conference paper, Published paper (Refereed)
Abstract [en]

Wireless control systems (WCSs) have gained much attention lately, due to their easy deployment and flexibility compared to wired control systems. However, this comes at the cost of possibly increased network delay and packet losses, that can significantly impact the control system performance, and possibly its stability. Such problems become even more relevant if the network is shared among different control systems, and thus becomes a scarce resource, like in Industrial Internet of Things applications. In this paper, we describe how to assign packet priorities dynamically when there are many physical systems sharing a given network, aiming at minimizing the performance degradation of the WCS. Towards that, we present a network model including both delay and packet losses, both of which are very important for the control system performance. Our solution is evaluated over two different use cases to show the generality of the approach: the WCS for a set of inverted pendula, and the WCS for small modular reactors in a nuclear power plant. The results show that the proposed approach allows for a more stable performance even in presence of highly nonlinear systems, sensitive to time-varying delays, as well as in presence of high network interference.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Distributed computer systems, Nuclear fuels, Nuclear power plants, Packet loss, Control system performance, Delay and packet loss, Network interferences, Performance degradation, Priority assignment, Small modular reactors, Stable performance, Time varying- delays, Control systems
National Category
Communication Systems Telecommunications Control Engineering Embedded Systems
Identifiers
urn:nbn:se:mdh:diva-45015 (URN)10.1109/ISORC.2019.00036 (DOI)000490865800025 ()2-s2.0-85070349885 (Scopus ID)9781728101507 (ISBN)
Conference
22nd IEEE International Symposium on Real-Time Distributed Computing, ISORC 2019, 7 May 2019 through 9 May 2019
Available from: 2019-08-15 Created: 2019-08-15 Last updated: 2019-10-31Bibliographically approved
Thörn, J., Vidimlic, N., Friebe, A., Papadopoulos, A. & Nolte, T. (2019). Probabilistic Timing Analysis of a Periodic Task on a Microcontroller. In: The 24th IEEE Conference on Emerging Technologies and Factory Automation ETFA2019: . Paper presented at The 24th IEEE Conference on Emerging Technologies and Factory Automation ETFA2019, 10 Sep 2019, Zaragoza, Spain (pp. 1419-1422).
Open this publication in new window or tab >>Probabilistic Timing Analysis of a Periodic Task on a Microcontroller
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2019 (English)In: The 24th IEEE Conference on Emerging Technologies and Factory Automation ETFA2019, 2019, p. 1419-1422Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we present our ongoing work towards a realistic probabilistic timing analysis of embedded software systems subject to timing requirements. In order to provide such an analysis that captures necessary and important behavioural features of the software system under analysis, including the underlying platform, we have implemented a real-time system running on a Rasberry Pi microcontroller on which we have performed a series of experiments and measurements. The results so far suggest a new model for analysis that captures more detailed behaviour and consequently provides a more accurate and correct probabilistic analysis.

Keywords
probabilistic timing analysis, response time, Markov Chain
National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46268 (URN)10.1109/ETFA.2019.8869210 (DOI)2-s2.0-85074202970 (Scopus ID)978-1-7281-0303-7 (ISBN)
Conference
The 24th IEEE Conference on Emerging Technologies and Factory Automation ETFA2019, 10 Sep 2019, Zaragoza, Spain
Projects
PARIS - Practical Probabilistic Timing Analysis of Real-Time Systems
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2019-12-17Bibliographically approved
Miloradović, B., Frasheri, M., Curuklu, B., Ekström, M. & Papadopoulos, A. (2019). TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model. In: PRIMA 2019: Principles and Practice of Multi-Agent Systems. Paper presented at The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy (pp. 478-486).
Open this publication in new window or tab >>TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model
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2019 (English)In: PRIMA 2019: Principles and Practice of Multi-Agent Systems, 2019, p. 478-486Conference paper, Published paper (Refereed)
Abstract [en]

Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.

National Category
Engineering and Technology Computer Systems
Identifiers
urn:nbn:se:mdh:diva-46316 (URN)10.1007/978-3-030-33792-6_32 (DOI)2-s2.0-85076411190 (Scopus ID)978-3-030-33791-9 (ISBN)
Conference
The 22nd International Conference on Principles and Practice of Multi-Agent Systems PRIMA'19, 28 Oct 2019, Turin, Italy
Projects
DPAC - Dependable Platforms for Autonomous systems and ControlUnicorn -Sustainable, peaceful and efficient robotic refuse handlingAggregate Farming in the Cloud
Available from: 2019-12-12 Created: 2019-12-12 Last updated: 2020-01-02Bibliographically approved
Relefors, J., Momeni, M., Pettersson, L., Hellström, E., Thunell, A., Papadopoulos, A. & Nolte, T. (2019). Towards Automated Installation of Reinforcement Using Industrial Robots. In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA: . Paper presented at 24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019 (pp. 1595-1598). Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Towards Automated Installation of Reinforcement Using Industrial Robots
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2019 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation, ETFA, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 1595-1598Conference paper, Published paper (Refereed)
Abstract [en]

The construction industry is today among the least automated industries with a long tradition of utilizing manual labour. Despite the potential benefits of automation, only a few examples of using robots to automate (parts of) construction have been presented over the past years. In this paper we present our ongoing work towards automated installation of reinforcement, a traditionally very heavy and labour intensive work. We use industrial robots and we discuss the potential benefits and challenges of such robotic automation in construction. Our overall goal is to achieve a fully automated robotic solution for flexible serial production of custom made non-identical reinforcement cages. In the paper we highlight and analyse the main challenges that must be addressed in order to reach a functioning and efficient solution.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2019
Keywords
Construction industry, Factory automation, Reinforcement, Robotics, Automated industry, Fully automated, Labour-intensive, Non-identical, Potential benefits, Robotic automation, Robotic solutions, Serial production, Industrial robots
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
urn:nbn:se:mdh:diva-46535 (URN)10.1109/ETFA.2019.8869343 (DOI)2-s2.0-85074194750 (Scopus ID)9781728103037 (ISBN)
Conference
24th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2019, 10 September 2019 through 13 September 2019
Available from: 2019-12-17 Created: 2019-12-17 Last updated: 2019-12-17Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-1364-8127

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