1,721,013 research outputs found
A distributed Kalman filter with event-triggered communication and guaranteed stability
No full textThe paper addresses Kalman filtering over a peer-to-peer sensor network with a careful eye towards data transmission scheduling for reduced communication bandwidth and, consequently, enhanced energy efficiency and prolonged network lifetime. A novel consensus Kalman filter algorithm with event-triggered communication is developed by enforcing each node to transmit its local information to the neighbors only when this is considered as particularly significant for estimation purposes, in the sense that it notably deviates from the information that can be predicted from the last transmitted one. Further, it is proved how the filter guarantees stability (mean-square boundedness of the estimation error in each node) under network connectivity and system collective observability. Finally, numerical simulations are provided to demonstrate practical effectiveness of the distributed filter for trading off estimation performance versus transmission rate
Fast Algorithms For Generalized Predictive Control
No full textFast algorithms for generalized predictive control (GPC) are derived by adopting an approach whereby dynamic programming and a polynomial formulation are jointly exploited. They consist of a set of coupled linear polynomial recursions by which the dynamic output feedback GPC law is recursively computed with only O(Nn) computations for an n-th order plant and N-steps prediction horizon
Distributed Kalman filtering with data-driven communication
No full textThe paper deals with distributed Kalman filtering over a peer-to-peer sensor network with focus on a data transmission scheduling strategy aiming at reduced communication bandwidth and, consequently, at enhanced energy efficiency and prolonged network lifetime. A novel distributed Kalman filter algorithm with data-driven communication is devised relying on the idea that each node transmit its local information to the neighbors only when this is deemed to be particularly relevant for estimation purposes, i.e. whenever it significantly deviates from the information predicted from the last transmitted one. An interesting information-theoretic interpretation of the proposed strategy is presented and numerical simulations are provided to demonstrate its practical effectivenes
Indirect and implicit adaptive predictive control of a benchmark plant
No full text.Two different adaptive predictive controllers are used for controlling the benchmark plant. One is of an indirect type, the other of an implicit type. In both cases, the only a priori knowledge used is that the nominal process order equals three
Block recursive parallelotopic bounding in set membership identification
No full textIn this paper, a procedure for the recursive approximation of the feasible parameter set of a linear model with a set membership uncertainty description is provided. Approximating regions of parallelotopic shape are considered. The new contribution of this paper consists in devising a general procedure allowing for block processing of q > 1 measurements at each recursion step. Based on this, several approximation strategies for polytopes are presented. Simulation experiments are performed, showing the effectiveness of the algorithm as compared to the original algorithm processing one measurement at each step
Recursive state bounding by parallelotopes
No full textIn this paper, the problem of recursively estimating the state uncertainty set of a discrete-time linear dynamical system is addressed. A novel approach based on minimum-volume bounding parallelotopes is introduced and an algorithm of polynomial complexity is derived. Simulation results and performance comparisons with ellipsoidal recursive state-bounding algorithms are also given. Copyright (C) 1996 Elsevier Science Ltd
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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