110,304 research outputs found
Sparse Iterative Learning Control with Application to a Wafer Stage: Achieving Performance, Resource Efficiency, and Task Flexibility
Trial-varying disturbances are a key concern in Iterative Learning Control
(ILC) and may lead to inefficient and expensive implementations and severe
performance deterioration. The aim of this paper is to develop a general
framework for optimization-based ILC that allows for enforcing additional
structure, including sparsity. The proposed method enforces sparsity in a
generalized setting through convex relaxations using norms. The
proposed ILC framework is applied to the optimization of sampling sequences for
resource efficient implementation, trial-varying disturbance attenuation, and
basis function selection. The framework has a large potential in control
applications such as mechatronics, as is confirmed through an application on a
wafer stage.Comment: 12 pages, 14 figure
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Robust H2/H∞-state estimation for systems with error variance constraints: the continuous-time case
Copyright [1999] 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.The paper is concerned with the state estimator design problem for perturbed linear continuous-time systems with H∞ norm and variance constraints. The perturbation is assumed to be time-invariant and norm-bounded and enters into both the state and measurement matrices. The problem we address is to design a linear state estimator such that, for all admissible measurable perturbations, 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. Existence conditions of the desired estimators are derived in terms of Riccati-type matrix inequalities, and the analytical expression of these estimators is also presented. A numerical example is provided to show the directness and effectiveness of the proposed design approac
Topics in Automotive Rollover Prevention: Robust and Adaptive Switching Strategies for Estimation and Control
The main focus in this thesis is the analysis of alternative approaches for estimation and control of automotive vehicles based on sound theoretical principles. Of particular importance is the problem rollover prevention, which is an important problem plaguing vehicles
with a high center of gravity (CG). Vehicle rollover is, statistically, the most dangerous accident type, and it is difficult to prevent it due to the time varying nature of the problem. Therefore, a major objective of the thesis is to develop the necessary theoretical and practical
tools for the estimation and control of rollover based on robust and adaptive techniques that are stable with respect to parameter variations.
Given this background, we first consider an implementation of the multiple model switching and tuning (MMST) algorithm for estimating the unknown parameters of automotive vehicles
relevant to the roll and the lateral dynamics including the position of CG. This results in high performance estimation of the CG as well as other time varying parameters, which can be used in tuning of the active safety controllers in real time. We then look into automotive rollover prevention control based on a robust stable control design methodology. As part of this we introduce a dynamic version of the load transfer ratio (LTR) as a rollover detection
criterion and then design robust controllers that take into account uncertainty in the CG position. As the next step we refine the controllers by integrating them with the multiple
model switched CG position estimation algorithm. This results in adaptive controllers with higher performance than the robust counterparts.
In the second half of the thesis we analyze extensions of certain theoretical results with important implications for switched systems. First we obtain a non-Lyapunov stability result for a certain class of linear discrete time switched systems. Based on this result, we suggest switched controller synthesis procedures for two roll dynamics enhancement control applications. One control design approach is related to modifying the dynamical response
characteristics of the automotive vehicle while guaranteeing the switching stability under parametric variations. The other control synthesis method aims to obtain transient free reference tracking of vehicle roll dynamics subject to parametric switching.
In a later discussion, we consider a particular decentralized control design procedure based on vector
Lyapunov functions for simultaneous, and structurally robust model reference tracking of both the lateral and the roll dynamics of automotive vehicles. We show that this controller design approach guarantees the closed loop stability subject to certain types of structural
uncertainty.
Finally, assuming a purely theoretical pitch, and motivated by the problems considered during the course of the thesis, we give new stability results on common Lyapunov solution
(CLS) existence for two classes of switching linear systems; one is concerned with switching pair of systems in companion form and with interval uncertainty, and the other is concerned with switching pair of companion matrices with general inertia. For both problems we give easily verifiable spectral conditions that are sufficient for the CLS existence. For proving the second result we also obtain a certain generalization of the classical Kalman-Yacubovic-Popov lemma for matrices with general inertia
Extended depth-of-field imaging and ranging in a snapshot
Traditional approaches to imaging require that an increase in depth of field is associated with a reduction in
numerical aperture, and hence with a reduction in resolution and optical throughput. In their seminal
work, Dowski and Cathey reported how the asymmetric point-spread function generated by a cubic-phase
aberration encodes the detected image such that digital recovery can yield images with an extended depth of
field without sacrificing resolution [Appl. Opt. 34, 1859 (1995)]. Unfortunately recovered images are
generally visibly degraded by artifacts arising from subtle variations in point-spread functions with defocus.
We report a technique that involves determination of the spatially variant translation of image components
that accompanies defocus to enable determination of spatially variant defocus. This in turn enables recovery
of artifact-free, extended depth-of-field images together with a two-dimensional defocus and range map
of the imaged scene. We demonstrate the technique for high-quality macroscopic and microscopic imaging
of scenes presenting an extended defocus of up to two waves, and for generation of defocus maps with an
uncertainty of 0.036 waves
Semi-Supervised Sound Source Localization Based on Manifold Regularization
Conventional speaker localization algorithms, based merely on the received
microphone signals, are often sensitive to adverse conditions, such as: high
reverberation or low signal to noise ratio (SNR). In some scenarios, e.g. in
meeting rooms or cars, it can be assumed that the source position is confined
to a predefined area, and the acoustic parameters of the environment are
approximately fixed. Such scenarios give rise to the assumption that the
acoustic samples from the region of interest have a distinct geometrical
structure. In this paper, we show that the high dimensional acoustic samples
indeed lie on a low dimensional manifold and can be embedded into a low
dimensional space. Motivated by this result, we propose a semi-supervised
source localization algorithm which recovers the inverse mapping between the
acoustic samples and their corresponding locations. The idea is to use an
optimization framework based on manifold regularization, that involves
smoothness constraints of possible solutions with respect to the manifold. The
proposed algorithm, termed Manifold Regularization for Localization (MRL), is
implemented in an adaptive manner. The initialization is conducted with only
few labelled samples attached with their respective source locations, and then
the system is gradually adapted as new unlabelled samples (with unknown source
locations) are received. Experimental results show superior localization
performance when compared with a recently presented algorithm based on a
manifold learning approach and with the generalized cross-correlation (GCC)
algorithm as a baseline
A survey on fractional order control techniques for unmanned aerial and ground vehicles
In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade
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