296,838 research outputs found
Sparse Identification of Nonlinear Duffing Oscillator From Measurement Data
In this paper we aim to apply an adaptation of the recently developed
technique of sparse identification of nonlinear dynamical systems on a Duffing
experimental setup with cubic feedback of the output. The Duffing oscillator
described by nonlinear differential equation which demonstrates chaotic
behavior and bifurcations, has received considerable attention in recent years
as it arises in many real-world engineering applications. Therefore its
identification is of interest for numerous practical problems. To adopt the
existing identification method to this application, the optimization process
which identifies the most important terms of the model has been modified. In
addition, the impact of changing the amount of regularization parameter on the
mean square error of the fit has been studied. Selection of the true model is
done via balancing complexity and accuracy using Pareto front analysis. This
study provides considerable insight into the employment of sparse
identification method on the real-world setups and the results show that the
developed algorithm is capable of finding the true nonlinear model of the
considered application including a nonlinear friction term.Comment: 6 pages, 8 figures, conference pape
Autonomous frequency domain identification: Theory and experiment
The analysis, design, and on-orbit tuning of robust controllers require more information about the plant than simply a nominal estimate of the plant transfer function. Information is also required concerning the uncertainty in the nominal estimate, or more generally, the identification of a model set within which the true plant is known to lie. The identification methodology that was developed and experimentally demonstrated makes use of a simple but useful characterization of the model uncertainty based on the output error. This is a characterization of the additive uncertainty in the plant model, which has found considerable use in many robust control analysis and synthesis techniques. The identification process is initiated by a stochastic input u which is applied to the plant p giving rise to the output. Spectral estimation (h = P sub uy/P sub uu) is used as an estimate of p and the model order is estimated using the produce moment matrix (PMM) method. A parametric model unit direction vector p is then determined by curve fitting the spectral estimate to a rational transfer function. The additive uncertainty delta sub m = p - unit direction vector p is then estimated by the cross spectral estimate delta = P sub ue/P sub uu where e = y - unit direction vectory y is the output error, and unit direction vector y = unit direction vector pu is the computed output of the parametric model subjected to the actual input u. The experimental results demonstrate the curve fitting algorithm produces the reduced-order plant model which minimizes the additive uncertainty. The nominal transfer function estimate unit direction vector p and the estimate delta of the additive uncertainty delta sub m are subsequently available to be used for optimization of robust controller performance and stability
Real-time system identification of an unmanned quadcopter system using fully tuned radial basis function neural networks
In this paper, we present the performance analysis of a fully tuned neural network
trained with the extended minimal resource allocating network (EMRAN) algorithm for real-time
identification of a quadcopter. Radial basis function network (RBF) based on system
identification can be utilised as an alternative technique for quadcopter modelling. To prevent the
neurons and network parameters selection dilemma during trial and error approach, RBF with
EMRAN training algorithm is proposed. This automatic tuning algorithm will implement the
network growing and pruning method to add or eliminate neurons in the RBF. The EMRAN’s
performance is compared with the minimal resource allocating network (MRAN) training for
1000 input-output pair untrained attitude data. The findings show that the EMRAN method
generates a faster mean training time of roughly 4.16 ms for neuron size of up to 88 units
compared to MRAN at 5.89 ms with a slight reduction in prediction accuracy
Lightcurve Classification in Massive Variability Surveys II: Transients towards the Large Magellanic Cloud
Automatic classification of variability is now possible with tools like
neural networks. Here, we present two neural networks for the identification of
microlensing events -- the first discriminates against variable stars and the
second against supernovae. The inputs to the networks include parameters
describing the shape and the size of the lightcurve, together with colour of
the event. The network computes the posterior probability of microlensing,
together with an estimate of the likely error. An algorithm is devised for
direct calculation of the microlensing rate from the output of the neural
networks. We present a new analysis of the microlensing candidates towards the
Large Magellanic Cloud (LMC). The neural networks confirm the microlensing
nature of only 7 of the possible 17 events identified by the MACHO experiment.
This suggests that earlier estimates of the microlensing optical depth towards
the LMC may have been overestimated. A smaller number of events is consistent
with the assumption that all the microlensing events are caused by the known
stellar populations in the outer Galaxy/LMC.Comment: 11 pages, MNRAS, in pres
Parameter identification for nonlinear aerodynamic systems
Parameter identification for nonlinear aerodynamic systems is examined. It is presumed that the underlying model can be arranged into an input/output (I/O) differential operator equation of a generic form. The algorithm estimation is especially efficient since the equation error can be integrated exactly given any I/O pair to obtain an algebraic function of the parameters. The algorithm for parameter identification was extended to the order determination problem for linear differential system. The degeneracy in a least squares estimate caused by feedback was addressed. A method of frequency analysis for determining the transfer function G(j omega) from transient I/O data was formulated using complex valued Fourier based modulating functions in contrast with the trigonometric modulating functions for the parameter estimation problem. A simulation result of applying the algorithm is given under noise-free conditions for a system with a low pass transfer function
System identification and adaptive current balancing ON/OFF control of DC-DC switch mode power converter
PhD ThesisReliability becomes more and more important in industrial application of Switch Mode Power
Converters (SMPCs). A poorly performing power supply in a power system can influence its operation
and potentially compromise the entire system performance in terms of efficiency. To maintain a high
reliability, high performance SMPC effective control is necessary for regulating the output of the SMPC
system. However, an uncertainty is a key factor in SMPC operation. For example, parameter variations
can be caused by environmental effects such as temperature, pressure and humidity. Usually, fixed
controllers cannot respond optimally and generate an effective signal to compensate the output error
caused by time varying parameter changes. Therefore, the stability is potentially compromised in this
case. To resolve this problem, increasing interest has been shown in employing online system
identification techniques to estimate the parameter values in real time. Moreover, the control scheme
applied after system identification is often called “adaptive control” due to the control signal selfadapting to the parameter variation by receiving the information from the system identification process.
In system identification, the Recursive Least Square (RLS) algorithm has been widely used because it
is well understood and easy to implement. However, despite the popularity of RLS, the high
computational cost and slow convergence speed are the main restrictions for use in SMPC applications.
For this reason, this research presents an alternative algorithm to RLS; Fast Affline Projection (FAP).
Detailed mathematical analysis proves the superior computational efficiency of this algorithm.
Moreover, simulation and experiment result verify this unique adaptive algorithm has improved
performance in terms of computational cost and convergence speed compared with the conventional
RLS methods. Finally, a novel adaptive control scheme is designed for optimal control of a DC-DC
buck converter during transient periods. By applying the proposed adaptive algorithm, the control signal
can be successfully employed to change the ON/OFF state of the power transistor in the DC-DC buck
converter to improve the dynamic behaviour. Simulation and experiment result show the proposed
adaptive control scheme significantly improves the transient response of the buck converter, particularly
during an abrupt load change conditio
A unified wavelet-based modelling framework for non-linear system identification: the WANARX model structure
A new unified modelling framework based on the superposition of additive submodels, functional components, and
wavelet decompositions is proposed for non-linear system identification. A non-linear model, which is often represented
using a multivariate non-linear function, is initially decomposed into a number of functional components via the wellknown
analysis of variance (ANOVA) expression, which can be viewed as a special form of the NARX (non-linear
autoregressive with exogenous inputs) model for representing dynamic input–output systems. By expanding each functional
component using wavelet decompositions including the regular lattice frame decomposition, wavelet series and
multiresolution wavelet decompositions, the multivariate non-linear model can then be converted into a linear-in-theparameters
problem, which can be solved using least-squares type methods. An efficient model structure determination
approach based upon a forward orthogonal least squares (OLS) algorithm, which involves a stepwise orthogonalization
of the regressors and a forward selection of the relevant model terms based on the error reduction ratio (ERR), is
employed to solve the linear-in-the-parameters problem in the present study. The new modelling structure is referred to
as a wavelet-based ANOVA decomposition of the NARX model or simply WANARX model, and can be applied to
represent high-order and high dimensional non-linear systems
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