|
|
Y. Driouich, M. Parente, and E. Tronci. "Model Checking Cyber-Physical Energy Systems." In Proceedings of 2017 International Renewable and Sustainable Energy Conference, IRSEC 2017. Institute of Electrical and Electronics Engineers Inc., 2018. DOI: 10.1109/IRSEC.2017.8477334.
|
|
|
|
T. Mancini, F. Mari, I. Melatti, I. Salvo, E. Tronci, J. Gruber, B. Hayes, M. Prodanovic, and L. Elmegaard. "Parallel Statistical Model Checking for Safety Verification in Smart Grids." In 2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 1–6., 2018. DOI: 10.1109/SmartGridComm.2018.8587416.
|
|
|
|
Y. Driouich, M. Parente, and E. Tronci. "A methodology for a complete simulation of Cyber-Physical Energy Systems." In EESMS 2018 – Environmental, Energy, and Structural Monitoring Systems, Proceedings, 1–5., 2018. DOI: 10.1109/EESMS.2018.8405826.
|
|
|
|
Y. Driouich, M. Parente, and E. Tronci. "Modeling cyber-physical systems for automatic verification." In 14th International Conference on Synthesis, Modeling, Analysis and Simulation Methods and Applications to Circuit Design (SMACD 2017), 1–4., 2017. DOI: 10.1109/SMACD.2017.7981621.
Keywords: cyber-physical systems;formal verification;maximum power point trackers;power engineering computing;Modelica;automatic verification;complex power electronics systems;cyber-physical systems modeling;distributed maximum power point tracking system;open standard modeling language;Computational modeling;Control systems;Integrated circuit modeling;Mathematical model;Maximum power point trackers;Object oriented modeling;Radiation effects;Automatic Formal Verification;Cyber-Physical Systems;DMPPT;Modeling;Photovoltaic systems;Simulation;System Analysis and Design
|
|
|
|
T. Mancini, F. Mari, I. Melatti, I. Salvo, and E. Tronci. "An Efficient Algorithm for Network Vulnerability Analysis Under Malicious Attacks." In Foundations of Intelligent Systems – 24th International Symposium, ISMIS 2018, Limassol, Cyprus, October 29-31, 2018, Proceedings, 302–312., 2018. Notes: Best Paper. DOI: 10.1007/978-3-030-01851-1_29.
|
|
|
|
Vadim Alimguzhin, Federico Mari, Igor Melatti, Ivano Salvo, and Enrico Tronci. A Map-Reduce Parallel Approach to Automatic Synthesis of Control Software. Vol. abs/1210.2276. CoRR, Technical Report, 2012. http://arxiv.org/abs/1210.2276 (accessed July 7, 2024).
Abstract: Many Control Systems are indeed Software Based Control Systems, i.e. control systems whose controller consists of control software running on a microcontroller device. This motivates investigation on Formal Model Based Design approaches for automatic synthesis of control software.
Available algorithms and tools (e.g., QKS) may require weeks or even months of computation to synthesize control software for large-size systems. This motivates search for parallel algorithms for control software synthesis.
In this paper, we present a map-reduce style parallel algorithm for control software synthesis when the controlled system (plant) is modeled as discrete time linear hybrid system. Furthermore we present an MPI-based implementation PQKS of our algorithm. To the best of our knowledge, this is the first parallel approach for control software synthesis.
We experimentally show effectiveness of PQKS on two classical control synthesis problems: the inverted pendulum and the multi-input buck DC/DC converter. Experiments show that PQKS efficiency is above 65%. As an example, PQKS requires about 16 hours to complete the synthesis of control software for the pendulum on a cluster with 60 processors, instead of the 25 days needed by the sequential algorithm in QKS.
|
|
