Due to the steady growth of decentralised distributed generation, the operational management of small, local electricity networks (microgrids) is becoming an increasing challenge to meet: How to provide an operational control for microgrids with a high share of renewable energy sources (RES) that is robust to perturbations? In this paper we address an optimal control problem (OCP) that maintains all of the stated properties in the presence of an uncertain load and RES infeed in islanded operation. Assuming that the uncertainty is within a bounded region along a given load and RES trajectory prediction, the problem is posed as a worst-case hybrid OCP, where the RES output can be curtailed. We propose a minimax (MM) model predictive control (MPC) scheme that adjusts according to the present uncertainty and can be formulated as a mixed-integer linear program (MILP) and solved numerically online.

In this paper we present logics about stable and unstable versions of several well-known relations from mereology: part-of, overlap and underlap. An intuitive semantics is given for the stable and unstable relations, describing them as dynamic counterparts of the base mereological relations. Stable relations are described as ones that always hold, while unstable relations hold sometimes. A set of first-order sentences is provided to serve as axioms for the stable and unstable relations, and representation theory is developed in similar fashion to Stone’s representation theory for distributive lattices. First-order predicate logic and modal logic are presented with semantics based on structures with stable and unstable mereological relations. Completeness theorems for these logics are proved, as well as decidability in the case of the modal logic, hereditary undecidability in the case of the first-order logic, and NP-completeness for the satisfiability problem of the quantifier-free fragment of the first-order logic.

The topic of this paper is distributed state estimation for time-invariant systems with finite input and output spaces. We assume that the system under investigation can be realised by a hybrid I/S/O-machine, where some of the discrete states may also represent failure modes. Our approach is based on previous work, e.g., Moor and Raisch (1999); Moor et al. (2002), where l-complete approximations were proposed as discrete event abstractions for hybrid dynamical systems. In particular, it has been shown that l-complete approximations can be used to provide set-valued estimates for the unknown system state. Estimates are conservative in the sense that the true state can be guaranteed to be contained in the set-valued estimate. In this contribution, we show that for a class of hybrid systems the same estimate can be obtained via a distributed, or decentralised, approach involving several less complex approximations, which are run in parallel. For a larger class of systems, it can be shown that this approach provides an outer approximation of the estimate provided by a monolithic l-complete estimator. The proposed procedure implies significant computational savings during estimator synthesis, with an only modest increase in on-line effort. The latter is a result of "assembling" the global estimate from the available local estimates. The resulting computational trade-off is explicitly discussed.

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The quality of requirements is fundamental in engineering projects. Requirements are usually expressed partly or totally in a natural language (NL) format and come from different documents.Their qualities are difficult to analyze manually, especially when hundreds of thousands of them have to be considered. The assistance of software tools is becoming a necessity. In this article, the goal was to develop a set of metrics supported by NL processing (NLP) methods supporting different types of analysis of requirements and especially the dependencies between requirements.An NLP approach is used to extract requirements from text; to analyze their quality, links, similarities, and contradictions; and to cluster them automatically. The analysis framework includes different combinations of methods such as cosine similarity, singular value decomposition, and Kmeans clustering. One objective is to assess the possible combinations and their impacts on detections to establish optimal metrics. Three case studies exemplify and support the validation of the work. Graphs are used to represent the automatically clustered requirements, as well as similarities and contradictions. A new contradiction analysis process that includes a rules-based approach is proposed. Finally, the combined results are presented as graphs, which unveil the semantic relationships between requirements. Subsection 4.8 compares the results provided by the tool and the results obtained from experts. The proposed methodology and network presentation not only support the understanding of the semantics of the requirements but also help requirements engineers to review the interconnections and consistency of requirements systems and manage traceability. The approach is valuable during the early phases of projects when requirements are evolving dynamically and rapidly. K E Y W O R D S contradictions analysis, network representation, requirements management, similarity Systems Engineering. 2018;21:555-575. c 2018 Wiley Periodicals, Inc. 555 wileyonlinelibrary.com/journal/sys AUTHORS' BIOGRAPHIES FAISAL MOKAMMEL is a doctoral student at Aalto University and work at Selko Oy developing a commercial version of the requirement extractor and analyser tool developed during his doctoral thesis. ERIC COATANÉA is tenured professor at Tampere University of Technology and was the initiator of the requirement extractor and analyser project. JOONAS COATANÉA is developer at Selko Oy. MOKAMMEL ET AL. 575 VLADISLAV NENCHEV is software developer and formal logic expert at Selko Oy. ERIC BLANCO is professor at INP Grenoble. MATTI PIETOLA is tenured professor at Aalto University. How to cite this article: Mokammel F, Coatanéa E, Coatanéa J, Nenchev V, Blanco E, Pietola M. Automatic requirements extraction, analysis, and graph representation using an approach derived from computational linguistics. Systems Engineering. 2018;21:555-575. https://doi.

In this paper, we address the problem of an autonomous robotic vehicle collecting a finite but unknown number of objects with non-negligible masses and unknown locations in a restricted area and moving them to a particular spot in minimum time. An adaptive certainty-equivalent navigation and control policy is introduced based on a pick-up and an exploration/drop-off mode. While the input signal in pickup mode is easily obtained in real time, complete exploration and drop-off corresponds to a hybrid optimal control problem (OCP) with exponential complexity in the finitely discretized space. We propose a trajectory planning algorithm by restricting the motion of the robot to a finite weighted graph. Further, we describe a discrete-time approximation of the hybrid OCP and compare both approaches with respect to computational complexity and accuracy.

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