4,655 research outputs found
Application of Lanczos vectors to control design of flexible structures
This report covers research conducted during the first year of the two-year grant. The research, entitled 'Application of Lanczos Vectors to Control Design of Flexible Structures' concerns various ways to obtain reduced-order mathematical models for use in dynamic response analyses and in control design studies. This report summarizes research described in several reports and papers that were written under this contract. Extended abstracts are presented for technical papers covering the following topics: controller reduction by preserving impulse response energy; substructuring decomposition and controller synthesis; model reduction methods for structural control design; and recent literature on structural modeling, identification, and analysis
Data-driven adaptive model-based predictive control with application in wastewater systems
This study is concerned with the development of a new data-driven adaptive model-based predictive controller (MBPC) with input constraints. The proposed methods employ subspace identification technique and a singular value decomposition (SVD)-based optimisation strategy to formulate the control algorithm and incorporate the input constraints. Both direct adaptive model-based predictive controller (DAMBPC) and indirect adaptive model-based predictive controller (IAMBPC) are considered. In DAMBPC, the direct identification of controller parameters is desired to reduce the design effort and computational load while the IAMBPC involves a two-stage process of model identification and controller design. The former method only requires a single QR decomposition for obtaining the controller parameters and uses a receding horizon approach to process input/output data for the identification. A suboptimal SVD-based optimisation technique is proposed to incorporate the input constraints. The proposed techniques are implemented and tested on a fourth order non-linear model of a wastewater system. Simulation results are presented to compare the direct and indirect adaptive methods and to demonstrate the performance of the proposed algorithms
Blockwise Subspace Identification for Active Noise Control
In this paper, a subspace identification solution is provided for active noise control (ANC) problems. The solution is related to so-called block updating methods, where instead of updating the (feedforward) controller on a sample by sample base, it is updated each time based on a block of N samples. The use of the subspace identification based ANC methods enables non-iterative derivation and updating of MIMO compact state space models for the controller. The robustness property of subspace identification methods forms the basis of an accurate model updating mechanism, using small size data batches. The design of a feedforward controller via the proposed approach is illustrated for an acoustic duct benchmark problem, supplied by TNO Institute of Applied Physics (TNO-TPD), the Netherlands. We also show how to cope with intrinsic feedback. A comparison study with various ANC schemes, such as block filtered-U, demonstrates the increased robustness of a subspace derived controlle
Moduli space actions on the Hochschild Co-Chains of a Frobenius algebra I: Cell Operads
This is the first of two papers in which we prove that a cell model of the
moduli space of curves with marked points and tangent vectors at the marked
points acts on the Hochschild co--chains of a Frobenius algebra. We also prove
that a there is dg--PROP action of a version of Sullivan Chord diagrams which
acts on the normalized Hochschild co-chains of a Frobenius algebra. These
actions lift to operadic correlation functions on the co--cycles. In
particular, the PROP action gives an action on the homology of a loop space of
a compact simply--connected manifold.
In this first part, we set up the topological operads/PROPs and their cell
models. The main theorems of this part are that there is a cell model operad
for the moduli space of genus curves with punctures and a tangent
vector at each of these punctures and that there exists a CW complex whose
chains are isomorphic to a certain type of Sullivan Chord diagrams and that
they form a PROP. Furthermore there exist weak versions of these structures on
the topological level which all lie inside an all encompassing cyclic
(rational) operad.Comment: 50 pages, 7 figures. Newer version has minor changes. Some material
shifted. Typos and small things correcte
Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator
Abstract--The paper describes a real-time adaptive
battery model for use in an all-electric Personal Rapid
Transit vehicle. Whilst traditionally, circuit-based models
for lead-acid batteries centre on the well-known Randlesâ
model, here the Randlesâ model is mapped to an equivalent
circuit, demonstrating improved modelling capabilities and
more accurate estimates of circuit parameters when used in
Subspace parameter estimation techniques. Combined with
Kalman Estimator algorithms, these techniques are
demonstrated to correctly identify and converge on voltages
associated with the battery State-of-Charge, overcoming
problems such as SoC drift (incurred by coulomb-counting
methods due to over-charging or ambient temperature
fluctuations).
Online monitoring of the degradation of these estimated
parameters allows battery ageing (State-of-Health) to be
assessed and, in safety-critical systems, cell failure may be
predicted in time to avoid inconvenience to passenger
networks.
Due to the adaptive nature of the proposed methodology,
this system can be implemented over a wide range of
operating environments, applications and battery
topologies
Microscopic/stochastic timesteppers and coarse control: a kinetic Monte Carlo example
Coarse timesteppers provide a bridge between microscopic / stochastic system
descriptions and macroscopic tasks such as coarse stability/bifurcation
computations. Exploiting this computational enabling technology, we present a
framework for designing observers and controllers based on microscopic
simulations, that can be used for their coarse control. The proposed
methodology provides a bridge between traditional numerical analysis and
control theory on the one hand and microscopic simulation on the other
Reducing "Structure From Motion": a General Framework for Dynamic Vision - Part 1: Modeling
The literature on recursive estimation of structure and motion from monocular image sequences comprises a large number of different models and estimation techniques. We propose a framework that allows us to derive and compare all models by following the idea of dynamical system reduction.
The "natural" dynamic model, derived by the rigidity constraint and the perspective projection, is first reduced by explicitly decoupling structure (depth) from motion. Then implicit decoupling techniques are explored, which consist of imposing that some function of the unknown parameters is held constant. By appropriately choosing such a function, not only can we account for all models seen so far in the literature, but we can also derive novel ones
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