Cristian Mahulea
University of Zaragoza
124 Papers
421 Citations
Cristian Mahulea is an academic researcher from University of Zaragoza. The author has contributed to research in topics: Petri net & Stochastic Petri net. The author has an hindex of 18, co-authored 111 publications. Previous affiliations of Cristian Mahulea include University of Cagliari.
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Papers
Fault Diagnosis of Discrete-Event Systems Using Continuous Petri Nets
Cristian Mahulea,Carla Seatzu,Maria Paola Cabasino,Manuel Silva +3 more
- 01 Jul 2012
TL;DR: This paper defines three diagnosis states, namely N, U, and F, corresponding respectively to no fault, uncertain, and fault state, and proves that, given an observation, the resulting diagnosis state can be computed solving linear programming problems rather than integer programming problems as in the discrete case.
Basic Server Semantics and Performance Monotonicity of Continuous Petri Nets
TL;DR: This paper will compare the results obtained with both relaxations for the broad class of mono-T-semiflow reducible nets, and prove that, under some frequently true conditions, infinite server semantics offers a throughput which is closer to the throughput of the discrete system in steady state.
Optimal Model Predictive Control of Timed Continuous Petri Nets
TL;DR: The optimal control problem of timed continuous Petri nets under infinite servers semantics is addressed through model predictive control (MPC), and an upper bound on the sample period is given in order to preserve important information of the timed continuous net.
Path planning for robotic teams based on LTL specifications and Petri net models
Marius Kloetzer,Cristian Mahulea +1 more
TL;DR: An automatic strategy for planning a team of identical robots evolving in a known environment by iterating the selection of an accepted run that satisfies the specification and the search for RMPN sequences of reachable markings that can produce desired observations.
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LTL-Based Planning in Environments With Probabilistic Observations
Marius Kloetzer,Cristian Mahulea +1 more
TL;DR: This research proposes a centralized method for planning and monitoring the motion of one or a few mobile robots in an environment where regions of interest appear and disappear based on exponential probability density functions.
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