1,926 research outputs found
Nonparametric identification of a class of nonlinear close-coupled dynamic systems
A nonparametric identification technique for the identification of close coupled dynamic systems with arbitrary memoryless nonlinearities is presented. The method utilizes noisy recorded data (acceleration, velocity and displacement) to identify the restoring forces in the system. The masses in the system are assumed to be known (or fairly well estimated from the design drawings). The restoring forces are expanded in a series of orthogonal polnomials and the coefficients of these polynomial expansions are obtained by using least square fit method. A particularly simple and computationally efficient method is proposed for dealing with separable restoring forces. The identified results are found to be relatively insensitive to measurement noise. An analysis of the effects of measurement noise on the quality of the estimates is given. The computations are shown to be relatively quick (when compared say to the Wiener identification method) and the core storage required relatively small, making the method suitable for onboard identification of large space structures
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Robust H2/H∞-state estimation for discrete-time systems with error variance constraints
Copyright [1997] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This paper studies the problem of an H∞-norm and variance-constrained state estimator design for uncertain linear discrete-time systems. The system under consideration is subjected to
time-invariant norm-bounded parameter uncertainties in both the state and measurement matrices. The problem addressed is the design of
a gain-scheduled linear state estimator such that, for all admissible measurable uncertainties, the variance of the estimation error of each state is not more than the individual prespecified value, and the transfer function from disturbances to error state outputs satisfies the prespecified H∞-norm upper bound constraint, simultaneously. The conditions for the existence of desired estimators are obtained in terms of matrix inequalities, and the explicit expression of these estimators is also derived. A numerical example is provided to demonstrate various aspects of theoretical results
When self-consistency makes a difference
Compound semiconductor power RF and microwave device modeling requires, in many cases, the use of selfconsistent electrothermal equivalent circuits. The slow thermal dynamics and the thermal nonlinearity should be accurately included in the model; otherwise, some response features subtly related to the detailed frequency behavior of the slow thermal dynamics would be inaccurately reproduced or completely distorted. In this contribution we show two examples, concerning current collapse in HBTs and modeling of IMPs in GaN HEMTs. Accurate thermal modeling is proved to be be made compatible with circuit-oriented CAD tools through a proper choice of system-level approximations; in the discussion we exploit a Wiener approach, but of course the strategy should be tailored to the specific problem under consideratio
Finding Structural Information of RF Power Amplifiers using an Orthogonal Non-Parametric Kernel Smoothing Estimator
A non-parametric technique for modeling the behavior of power amplifiers is
presented. The proposed technique relies on the principles of density
estimation using the kernel method and is suited for use in power amplifier
modeling. The proposed methodology transforms the input domain into an
orthogonal memory domain. In this domain, non-parametric static functions are
discovered using the kernel estimator. These orthogonal, non-parametric
functions can be fitted with any desired mathematical structure, thus
facilitating its implementation. Furthermore, due to the orthogonality, the
non-parametric functions can be analyzed and discarded individually, which
simplifies pruning basis functions and provides a tradeoff between complexity
and performance. The results show that the methodology can be employed to model
power amplifiers, therein yielding error performance similar to
state-of-the-art parametric models. Furthermore, a parameter-efficient model
structure with 6 coefficients was derived for a Doherty power amplifier,
therein significantly reducing the deployment's computational complexity.
Finally, the methodology can also be well exploited in digital linearization
techniques.Comment: Matlab sample code (15 MB):
https://dl.dropboxusercontent.com/u/106958743/SampleMatlabKernel.zi
Measurement-driven Quality Assessment of Nonlinear Systems by Exponential Replacement
We discuss the problem how to determine the quality of a nonlinear system
with respect to a measurement task. Due to amplification, filtering,
quantization and internal noise sources physical measurement equipment in
general exhibits a nonlinear and random input-to-output behaviour. This usually
makes it impossible to accurately describe the underlying statistical system
model. When the individual operations are all known and deterministic, one can
resort to approximations of the input-to-output function. The problem becomes
challenging when the processing chain is not exactly known or contains
nonlinear random effects. Then one has to approximate the output distribution
in an empirical way. Here we show that by measuring the first two sample
moments of an arbitrary set of output transformations in a calibrated setup,
the output distribution of the actual system can be approximated by an
equivalent exponential family distribution. This method has the property that
the resulting approximation of the statistical system model is guaranteed to be
pessimistic in an estimation theoretic sense. We show this by proving that an
equivalent exponential family distribution in general exhibits a lower Fisher
information measure than the original system model. With various examples and a
model matching step we demonstrate how this estimation theoretic aspect can be
exploited in practice in order to obtain a conservative measurement-driven
quality assessment method for nonlinear measurement systems.Comment: IEEE International Instrumentation and Measurement Technology
Conference (I2MTC), Taipei, Taiwan, 201
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