259,971 research outputs found
Guidelines for Weighting Factors Adjustment in Finite State Model Predictive Control of Power Converters and Drives
INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY () (.2009.VICTORIA, AUSTRALIA)Model Predictive Control with a finite control set has
emerged as a promising control tool for power converters and
drives. One of the major advantages is the possibility to control
several system variables with a single control law, by including
them with appropriate weighting factors. However, at the present
state of the art, these coefficients are determined empirically.
There is no analytical or numerical method proposed yet to obtain
an optimal solution. In addition, the empirical method is not
always straightforward, and no procedures have been reported.
This paper presents a first approach to a set of guidelines
that reduce the uncertainty of this process. First a classification
of different types of cost functions and weighting factors is
presented. Then the different steps of the empirical process are
explained. Finally, results for several power converters and drives
applications are analyzed, which show the effectiveness of the
proposed guidelines to reach appropriate weighting factors and
control performance
μ-Dependent model reduction for uncertain discrete-time switched linear systems with average dwell time
In this article, the model reduction problem for a class of discrete-time polytopic uncertain switched linear systems with average dwell time switching is investigated. The stability criterion for general discrete-time switched systems is first explored, and a μ-dependent approach is then introduced for the considered systems to the model reduction solution. A reduced-order model is constructed and its corresponding existence conditions are derived via LMI formulation. The admissible switching signals and the desired reduced model matrices are accordingly obtained from such conditions such that the resulting model error system is robustly exponentially stable and has an exponential H∞ performance. A numerical example is presented to demonstrate the potential and effectiveness of the developed theoretical results
Non-Malleable Codes for Small-Depth Circuits
We construct efficient, unconditional non-malleable codes that are secure
against tampering functions computed by small-depth circuits. For
constant-depth circuits of polynomial size (i.e. tampering
functions), our codes have codeword length for a -bit
message. This is an exponential improvement of the previous best construction
due to Chattopadhyay and Li (STOC 2017), which had codeword length
. Our construction remains efficient for circuit depths as
large as (indeed, our codeword length remains
, and extending our result beyond this would require
separating from .
We obtain our codes via a new efficient non-malleable reduction from
small-depth tampering to split-state tampering. A novel aspect of our work is
the incorporation of techniques from unconditional derandomization into the
framework of non-malleable reductions. In particular, a key ingredient in our
analysis is a recent pseudorandom switching lemma of Trevisan and Xue (CCC
2013), a derandomization of the influential switching lemma from circuit
complexity; the randomness-efficiency of this switching lemma translates into
the rate-efficiency of our codes via our non-malleable reduction.Comment: 26 pages, 4 figure
Model Reduction by Moment Matching for Linear Switched Systems
Two moment-matching methods for model reduction of linear switched systems
(LSSs) are presented. The methods are similar to the Krylov subspace methods
used for moment matching for linear systems. The more general one of the two
methods, is based on the so called "nice selection" of some vectors in the
reachability or observability space of the LSS. The underlying theory is
closely related to the (partial) realization theory of LSSs. In this paper, the
connection of the methods to the realization theory of LSSs is provided, and
algorithms are developed for the purpose of model reduction. Conditions for
applicability of the methods for model reduction are stated and finally the
results are illustrated on numerical examples.Comment: Sent for publication in IEEE TAC, on October 201
Online Learning with Switching Costs and Other Adaptive Adversaries
We study the power of different types of adaptive (nonoblivious) adversaries
in the setting of prediction with expert advice, under both full-information
and bandit feedback. We measure the player's performance using a new notion of
regret, also known as policy regret, which better captures the adversary's
adaptiveness to the player's behavior. In a setting where losses are allowed to
drift, we characterize ---in a nearly complete manner--- the power of adaptive
adversaries with bounded memories and switching costs. In particular, we show
that with switching costs, the attainable rate with bandit feedback is
. Interestingly, this rate is significantly worse
than the rate attainable with switching costs in the
full-information case. Via a novel reduction from experts to bandits, we also
show that a bounded memory adversary can force
regret even in the full information case, proving that switching costs are
easier to control than bounded memory adversaries. Our lower bounds rely on a
new stochastic adversary strategy that generates loss processes with strong
dependencies
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
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