This paper presents the development of a new method for parameter estimation in linear state space model. The proposed method is based on a Rao-Blackwellised particle filter. The simulation results with a railway vehicle dynamic model are provided which demonstrate the effectiveness of the proposed method in comparison with the conventional EKF-based method.
In this paper, a nonlinear current-limiting droop controller is proposed to achieve accurate power sharing among parallel operated DC-DC boost converters in a DC micro-grid application. In particular, the recently developed robust droop controller is adopted and implemented as a dynamic virtual resistance in series with the inductance of each DC-DC boost converter. Opposed to the traditional approaches that use small-signal modeling, the proposed control design takes into account the accurate nonlinear dynamic model of each converter and it is analytically proven that accurate power sharing can be accomplished with an inherent current limitation for each converter independently using input-to-state stability theory. When the load requests more power that exceeds the capacity of the converters, the current-limiting capability of the proposed control method protects the devices by limiting the inductor current of each converter below a given maximum value. Extensive simulation results of two paralleled DC-DC boost converters are presented to verify the power sharing and current-limiting properties of the proposed controller under several changes of the load.
Summary Two significant drawbacks of current self‐healing materials are that they are: (1) Passive and as such do not guarantee a match between the healing and damage rate; (2) Not monitored during and after healing, so that the performance of the healed material is not known without retrospective offline testing. As a consequence their application is currently limited in some sectors, such as the aerospace sector where high performance needs to be guaranteed within strict guidelines. This article proposes the first active self‐healing material that integrates with control and fault diagnosis to provide a system with a desired healing response. A fault diagnosis algorithm using supervised regression is used to estimate the measure of damage. Then based on this estimate, adaptive feedback control is used to ensure a match between the healing response and the damage rate, while taking into account the nonlinear system dynamics and uncertainty. The system is demonstrated in simulation using a self‐healing material based on piezoelectricity and electrolysis. This shows the ability of the integrated subsystems to tackle these two significant drawbacks of most current self‐healing systems and will benefit applications with strict performance requirements, or systems operating under harsh conditions or that are remotely accessed.
Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.
Abstract. Electromagnetic phenomena observed in association with increases in seismic activity have been studied for several decades. These phenomena are generated during the precursory phases of an earthquake as well as during the main event. Their occurrence during the precursory phases may be used in short-term prediction of a large earthquake. In this paper, we examine ultra-low frequency (ULF) electric field data from the DEMETER satellite during the period leading up to the Sichuan earthquake. It is shown that there is an increase in ULF wave activity observed as DEMETER passes in the vicinity of the earthquake epicentre. This increase is most obvious at lower frequencies. Examination of the ULF spectra shows the possible occurrence of geomagnetic pearl pulsations, resulting from the passage of atmospheric gravity waves generated in the vicinity of the earthquake epicentre.
This paper presents an adaptive code-aided technique for the suppression of narrowband interference (NBI) in direct-sequence code-division multiple access (DS-CDMA) systems. This technique uses a multiuser detector based on the interacting multiple model (BOA) algorithm. This detector is based on the concept that the effective model of the received signal at a time instance can be approximated by one of the models in the IMM algorithm. Simulations are used to compare the performance of the proposed technique with that of the recursive least squares (RLS) version of the minimum mean-square error (MMSE) for multiuser detection.
'%3e%3cpath fill='%23012169' d='M0 0v30h60V0z'/%3e%3cpath stroke='%23fff' stroke-width='6' d='m0 0 60 30m0-30L0 30'/%3e%3cpath stroke='%23C8102E' stroke-width='4' d='m0 0 60 30m0-30L0 30' clip-path='url(%23b)'/%3e%3cpath stroke='%23fff' stroke-width='10' d='M30 0v30M0 15h60'/%3e%3cpath stroke='%23C8102E' stroke-width='6' d='M30 0v30M0 15h60'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)