5,846 research outputs found
Modeling and Estimation of Biological Plants
Estimating the state of a dynamic system is an essential task for achieving important objectives such as process monitoring, identification, and control. Unlike linear systems, no systematic method exists for the design of observers for nonlinear systems. Although many researchers have devoted their attention to these issues for more than 30 years, there are still many open questions. We envisage that estimation plays a crucial role in biology because of the possibility of creating new avenues for biological studies and for the development of diagnostic, management, and treatment tools. To this end, this thesis aims to address two types of nonlinear estimation techniques, namely, the high-gain observer and the moving-horizon estimator with application to three different biological plants.
After recalling basic definitions of stability and observability of dynamical systems and giving a bird's-eye survey of the available state estimation techniques, we are interested in the high-gain observers. These observers may be used when the system dynamics can be expressed in specific a coordinate under the so-called observability canonical form with the possibility to assign the rate of convergence arbitrarily by acting on a single parameter called the high-gain parameter. Despite the evident benefits of this class of observers, their use in real applications is questionable due to some drawbacks: numerical problems, the peaking phenomenon, and high sensitivity to measurement noise. The first part of the thesis aims to enrich the theory of high-gain observers with novel techniques to overcome or attenuate these challenging performance issues that arise when implementing such observers. The validity and applicability of our proposed techniques have been shown firstly on a simple one-gene regulatory network, and secondly on an SI epidemic model.
The second part of the thesis studies the problem of state estimation using the moving horizon approach. The main advantage of MHE is that information
about the system can be explicitly considered in the form of constraints
and hence improve the estimates. In this work, we focus on estimation for nonlinear plants that can be rewritten in the form of quasi-linear parameter-varying systems with bounded unknown parameters. Moving-horizon estimators are proposed to estimate the state of such systems according to two different formulations, i.e., "optimistic" and "pessimistic". In the former case, we perform estimation by minimizing the least-squares moving-horizon cost with respect to both state variables and parameters simultaneously. In the latter, we minimize such a cost with respect to the state variables after picking up the maximum of the parameters. Under suitable assumptions, the stability of the estimation error given by the exponential boundedness is proved in both scenarios.
Finally, the validity of our obtained results has been demonstrated through three different examples from biological and biomedical fields, namely, an example of one gene regulatory network, a two-stage SI epidemic model, and Amnioserosa cell's mechanical behavior during Dorsal closure
Contracting Nonlinear Observers: Convex Optimization and Learning from Data
A new approach to design of nonlinear observers (state estimators) is
proposed. The main idea is to (i) construct a convex set of dynamical systems
which are contracting observers for a particular system, and (ii) optimize over
this set for one which minimizes a bound on state-estimation error on a
simulated noisy data set. We construct convex sets of continuous-time and
discrete-time observers, as well as contracting sampled-data observers for
continuous-time systems. Convex bounds for learning are constructed using
Lagrangian relaxation. The utility of the proposed methods are verified using
numerical simulation.Comment: conference submissio
On algebraic time-derivative estimation and deadbeat state reconstruction
This note places into perspective the so-called algebraic time-derivative
estimation method recently introduced by Fliess and co-authors with standard
results from linear state-space theory for control systems. In particular, it
is shown that the algebraic method can in a sense be seen as a special case of
deadbeat state estimation based on the reconstructibility Gramian of the
considered system.Comment: Maple-supplements available at
https://www.tu-ilmenau.de/regelungstechnik/mitarbeiter/johann-reger
The Lazarus project: A pragmatic approach to binary black hole evolutions
We present a detailed description of techniques developed to combine 3D
numerical simulations and, subsequently, a single black hole close-limit
approximation. This method has made it possible to compute the first complete
waveforms covering the post-orbital dynamics of a binary black hole system with
the numerical simulation covering the essential non-linear interaction before
the close limit becomes applicable for the late time dynamics. To determine
when close-limit perturbation theory is applicable we apply a combination of
invariant a priori estimates and a posteriori consistency checks of the
robustness of our results against exchange of linear and non-linear treatments
near the interface. Once the numerically modeled binary system reaches a regime
that can be treated as perturbations of the Kerr spacetime, we must
approximately relate the numerical coordinates to the perturbative background
coordinates. We also perform a rotation of a numerically defined tetrad to
asymptotically reproduce the tetrad required in the perturbative treatment. We
can then produce numerical Cauchy data for the close-limit evolution in the
form of the Weyl scalar and its time derivative
with both objects being first order coordinate and tetrad invariant. The
Teukolsky equation in Boyer-Lindquist coordinates is adopted to further
continue the evolution. To illustrate the application of these techniques we
evolve a single Kerr hole and compute the spurious radiation as a measure of
the error of the whole procedure. We also briefly discuss the extension of the
project to make use of improved full numerical evolutions and outline the
approach to a full understanding of astrophysical black hole binary systems
which we can now pursue.Comment: New typos found in the version appeared in PRD. (Mostly found and
collected by Bernard Kelly
Model predictive control techniques for hybrid systems
This paper describes the main issues encountered when applying model predictive control to hybrid processes. Hybrid model predictive control (HMPC) is a research field non-fully developed with many open challenges. The paper describes some of the techniques proposed by the research community to overcome the main problems encountered. Issues related to the stability and the solution of the optimization problem are also discussed. The paper ends by describing the results of a benchmark exercise in which several HMPC schemes were applied to a solar air conditioning plant.Ministerio de Eduación y Ciencia DPI2007-66718-C04-01Ministerio de Eduación y Ciencia DPI2008-0581
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