26,610 research outputs found
A comparative study on global wavelet and polynomial models for nonlinear regime-switching systems
A comparative study of wavelet and polynomial models for non-linear Regime-Switching (RS) systems is carried out. RS systems, considered in this study, are a class of severely non-linear systems, which exhibit abrupt changes or dramatic breaks in behaviour, due to RS caused by associated events. Both wavelet and polynomial models are used to describe discontinuous dynamical systems, where it is assumed that no a priori information about the inherent model structure and the relative regime switches of the underlying dynamics is known, but only observed input-output data are available. An Orthogonal Least Squares (OLS) algorithm interfered with by an Error Reduction Ratio (ERR) index and regularised by an Approximate Minimum Description Length (AMDL) criterion, is used to construct parsimonious wavelet and polynomial models. The performance of the resultant wavelet models is compared with that of the relative polynomial models, by inspecting the predictive capability of the associated representations. It is shown from numerical results that wavelet models are superior to polynomial models, in respect of generalisation properties, for describing severely non-linear RS systems
Data-driven modelling of biological multi-scale processes
Biological processes involve a variety of spatial and temporal scales. A
holistic understanding of many biological processes therefore requires
multi-scale models which capture the relevant properties on all these scales.
In this manuscript we review mathematical modelling approaches used to describe
the individual spatial scales and how they are integrated into holistic models.
We discuss the relation between spatial and temporal scales and the implication
of that on multi-scale modelling. Based upon this overview over
state-of-the-art modelling approaches, we formulate key challenges in
mathematical and computational modelling of biological multi-scale and
multi-physics processes. In particular, we considered the availability of
analysis tools for multi-scale models and model-based multi-scale data
integration. We provide a compact review of methods for model-based data
integration and model-based hypothesis testing. Furthermore, novel approaches
and recent trends are discussed, including computation time reduction using
reduced order and surrogate models, which contribute to the solution of
inference problems. We conclude the manuscript by providing a few ideas for the
development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and
Multiscale Dynamics (American Scientific Publishers
Temperature Regulation in Multicore Processors Using Adjustable-Gain Integral Controllers
This paper considers the problem of temperature regulation in multicore
processors by dynamic voltage-frequency scaling. We propose a feedback law that
is based on an integral controller with adjustable gain, designed for fast
tracking convergence in the face of model uncertainties, time-varying plants,
and tight computing-timing constraints. Moreover, unlike prior works we
consider a nonlinear, time-varying plant model that trades off precision for
simple and efficient on-line computations. Cycle-level, full system simulator
implementation and evaluation illustrates fast and accurate tracking of given
temperature reference values, and compares favorably with fixed-gain
controllers.Comment: 8 pages, 6 figures, IEEE Conference on Control Applications 2015,
Accepted Versio
Plug-and-play and coordinated control for bus-connected AC islanded microgrids
This paper presents a distributed control architecture for voltage and
frequency stabilization in AC islanded microgrids. In the primary control
layer, each generation unit is equipped with a local controller acting on the
corresponding voltage-source converter. Following the plug-and-play design
approach previously proposed by some of the authors, whenever the
addition/removal of a distributed generation unit is required, feasibility of
the operation is automatically checked by designing local controllers through
convex optimization. The update of the voltage-control layer, when units plug
-in/-out, is therefore automatized and stability of the microgrid is always
preserved. Moreover, local control design is based only on the knowledge of
parameters of power lines and it does not require to store a global microgrid
model. In this work, we focus on bus-connected microgrid topologies and enhance
the primary plug-and-play layer with local virtual impedance loops and
secondary coordinated controllers ensuring bus voltage tracking and reactive
power sharing. In particular, the secondary control architecture is
distributed, hence mirroring the modularity of the primary control layer. We
validate primary and secondary controllers by performing experiments with
balanced, unbalanced and nonlinear loads, on a setup composed of three
bus-connected distributed generation units. Most importantly, the stability of
the microgrid after the addition/removal of distributed generation units is
assessed. Overall, the experimental results show the feasibility of the
proposed modular control design framework, where generation units can be
added/removed on the fly, thus enabling the deployment of virtual power plants
that can be resized over time
Active sensor fault tolerant output feedback tracking control for wind turbine systems via T-S model
This paper presents a new approach to active sensor fault tolerant tracking control (FTTC) for offshore wind turbine (OWT) described via Takagi–Sugeno (T–S) multiple models. The FTTC strategy is designed in such way that aims to maintain nominal wind turbine controller without any change in both fault and fault-free cases. This is achieved by inserting T–S proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators to be capable to estimate different generators and rotor speed sensors fault for compensation purposes. Due to the dependency of the FTTC strategy on the fault estimation the designed observer has the capability to estimate a wide range of time varying fault signals. Moreover, the robustness of the observer against the difference between the anemometer wind speed measurement and the immeasurable effective wind speed signal has been taken into account. The corrected measurements fed to a T–S fuzzy dynamic output feedback controller (TSDOFC) designed to track the desired trajectory. The stability proof with H∞ performance and D-stability constraints is formulated as a Linear Matrix Inequality (LMI) problem. The strategy is illustrated using a non-linear benchmark system model of a wind turbine offered within a competition led by the companies Mathworks and KK-Electronic
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