In this paper we provide an overview of recent theoretical approaches and technologies that respond to the fundamental challenges of modern factory automation. We classify these major methods and technologies into several groups and, for seven of them - namely: vertical integration of factory automation systems; distributed and decentralised control, smart sensors and actuators in factories; networked control systems and wireless sensors and actuators; autonomy and self-organisation of factories; advanced sensing for factory automation; semantic models of factories; engineering methods of factory automation systems - we report recent research contributions and formulate open technical problems in the domain of modern factory automation.

In this paper, we propose a novel approach to perform codiagnosability analysis of labeled bounded Petri nets. A set of sites observe the system evolution, each one with its own observation mask. Sites do not exchange information with each other but communicate with a coordinator. The coordinator is able to detect a fault if and only if at least one site is able to do that. In a previous work by some of us, it has been proven that a necessary and sufficient condition for codiagnosability under such a framework is the absence of sequences that are “ambiguous” with respect to all sites and whose length may grow indefinitely after the occurrence of some fault. The novelties of the approach consist in using the notion of basis markings to avoid exhaustive enumeration of the set of reachable markings, and in the construction of an automaton, called Verifier, that allows one to detect the presence of ambiguous sequences. Finally, we introduce the notion of K-codiagnosability: a system is K-codiagnosable if and only if faults can be detected in the above framework within at most K observations after their occurrence. An algorithm is provided to compute the smallest value of K such that the system is K-codiagnosable.

In this paper we deal with the problem of enforcing diagnosability to labeled Petri nets appropriately adding new sensors. We show that, solving an integer linear programming problem, it is possible to select a solution that is optimal with respect to a given objective function (e.g., the cost of sensors). The solution is based on two notions, already introduced by the authors in previous works, namely basis marking and Unfolded Verifier. This allows to solve the considered problem in a more efficient way with respect to other approaches in the literature. Finally, we propose an algorithm to compute the smallest value of K such that the Petri net system is K-diagnosable under the new labeling function, which implies that faults can be detected in at most K observations after their occurrence.

The main contribution of this correspondence paper consists in a linear algebraic characterization of the admissible marking set relative to a Petri net with uncontrollable transitions, subject to a linear constraint. In more detail, given a linear constraint that limits the number of tokens in one place, an algorithm is proposed to compute an approximation of the admissible marking set in terms of a disjunction of transformed linear constraints. The optimality of the solution is guaranteed provided that certain conditions are satisfied during the intermediate steps of the iterative approach. In all the other cases, the set of markings described by the transformed constraints could be surely contained in the admissible marking set.

Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network.