IFAC-PapersOnLine最新文献

We present a new algorithm to verify inference observability in supervisory control of decentralized discrete-event systems. The algorithm's success relies on the following idea. An inferencing solution exists only when a language contains no inferencing cycles. When there are no cycles, the algorithm computes the smallest upper bound for an inferencing solution.

A new approach for the online state estimation of discrete event systems (DESs) modeled by λ-free labeled Petri nets (λf-LPNs) is presented, wherein all events are observable. Instead of exhaustively enumerating all marking vectors consistent with any given event occurrence, we do a representation-based state estimation, where we compute a compact representation structure that characterizes these markings. Our representation structure is based on a representative Petri net (RPN), whose single initial marking represents all markings of the system λf-LPN consistent with the sequence. These representations can be directly used to solve other DES-related problems, such as fault diagnosis. Our approach can compute representations of any λf-LPNs. In addition, for a class of unbounded λf-LPNs, our approach can compute representations that do not grow indefinitely as more events occur.

As environmental concerns grow, Electric Vehicles (EVs) are becoming essential for sustainable urban Traffic. Some countries plan to phase out conventional vehicles to cut carbon emissions. Yet, challenges include a lack of charging infrastructure and potential strain on power networks. EVs could benefit by integrating Vehicle-to-Grid connections. To leverage e-flexibility, modeling EV power requirements and mobility patterns is crucial. The paper presents eMob-Twin, a digital twin, combining urban EV mobility with an energy model, aiding simulations with fine granularity including charging stations, its potential connections with the Grid and electricity markets. We present details of the structure and operation of eMob-Twin and some examples of their utility in connection with user cases such as impact of the EVs penetration rate, and optimal locations of charge stations.

In this paper a perception system of a mobile robot is proposed and implemented combining a LiDAR sensor with a colour camera. Localisation of unknown objects in an a priori known environment and their classification is required in order for the robot to safely and reliably navigate the working area. To this end, a pipeline is proposed to process and fuse the LiDAR 3D point-cloud data with a monocular colour camera image classifications yielding precisely localised 3D detections with class designations. The proposed pipeline is designed to run on limited computational power embedded platforms in the ROS 2 environment, and has been tested on a robotic testbench.

This study employs a Mixed-Integer Linear Programming model to optimize energy transactions in an energy community. This research promotes energy sharing through grid and peer-to-peer transactions by incorporating energy storage systems. The article also integrates fairness metrics to evaluate economic equity. The case study examines households with varying equipment acquisition power, highlighting the impact of pricing structures on economic fairness. Analyzing optimal results, the study focused on a community of ten households, six of which are prosumers with PV and energy storage of different capacities. Results show that combining peer-to-peer transactions and high-capacity PV and storage can lead up to a 183% improvement in energy bills. On the other hand, members participating in energy sharing without PV and storage within the community only achieve ~13% improvement in their energy bill, clearly showing a disparity in cost savings linked to equipment capacity acquisition.