35 research outputs found
Numerical Methods for Random Parameter Optimal Control and the Optimal Control of Stochastic Differential Equations
This thesis considers the investigation and development of numerical methods for optimal control problems that are
influenced by stochastic phenomena of various type. The first part treats tasks characterized by random parameters,
while in the subsequent second part time-dependent stochastic processes are the basis of the dynamics describing the analyzed systems. In each case the investigations aim to transform the original problem into one that can be tackled by existing (direct) methods of deterministic optimal control - here we prefer Bock's direct multiple shooting approach.
In the context of this transformation, in the first part approaches from stochastic programming as well as robust and probabilistic optimization are used. Regarding a specific application from mathematical economics, which considers pricing conspicuous consumption products in periods of recession, new numerical procedures are developed and analyzed with due regard to those techniques - in particular, a scenario tree approach, approximations of robust worst-case settings, and financial tools as the Value at Risk and Conditional Value at Risk. Furthermore, necessary reformulations of the resulting optimal control problems, in particular for Value at Risk and Conditional Value at Risk, as well as the discussion and interpretation of results determined depending on an uncertain recession duration, an uncertain recession strength, and control delays are in focus. The gained economic insight can be seen as an important step in the direction of a better understanding of real-world pricing strategies.
In the second part of the thesis, based on the Wiener chaos expansion of a stochastic process and on Malliavin calculus, a system of coupled ordinary differential equations is developed that completely characterizes the stochastic differential equation describing the dynamics of the process. As in general this system includes infinitely many equations, a rigorous error estimation depending on the order of the chaos decomposition is proven in order to guarantee the numerical applicability. To transfer the generic procedure of the chaos expansion to stochastic optimal control problems, a method to preserve the feedback character of the occurring control process is shown. This allows the derivation of a novel direct method to solve finite-horizon stochastic optimal control problems. The appropriability and accuracy of this methodology are demonstrated by treating several problem instances numerically. Finally, the economic application of the first part is revisited under the viewpoint of dealing with a time-dependent recession strength, i.e., a stochastic process. In particular, those applications illustrate that the existing methods of deterministic optimal control can be extended to problems including stochastic differential equations
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Enhancements of online Bayesian filtering algorithms for efficient monitoring and improved uncertainty quantification in complex nonlinear dynamical systems
Recent years have seen a concurrent development of new sensor technologies and high-fidelity modeling capabilities. At the junction of these two topics lies an interesting opportunity for real-time system monitoring and damage assessment of structures. During monitoring, measurements from a structure are used to learn the parameters and equations characterizing a physics-based model of the system; thus enabling damage identification. Since monitored quantities are physical, these methods offer precious insight into the damage state of the structure (localization, type of damage and its extent). Furthermore, one obtains a model of the structure in its current condition, an essential element in predicting the future behavior of the structure and enabling adequate decision-making procedures.
This dissertation focuses more specifically on solving some of the challenges associated with the use of online Bayesian learning algorithms, also called sequential filtering algorithms, for damage detection and characterization in nonlinear structural systems. A major challenge regarding online Bayesian filtering algorithms lies in achieving good accuracy for large dimensional systems and complex nonlinear non-Gaussian systems, where non-Gaussianity can arise for instance in systems which are not globally identifiable. In the first part of this dissertation, we show that one can derive algorithmic enhancements of filtering techniques, mainly based on innovative ways to reduce the dimensionality of the problem at hand, and thus obtain a good trade-off between accuracy and computational complexity of the learning algorithms. For instance, for particle filtering techniques (sampling-based algorithms) subjected to the so-called curse of dimensionality, the concept of Rao-Blackwellisation can be used to greatly reduce the dimension of the sampling space. On the other hand, one can also build upon nonlinear Kalman filtering techniques, which are very computationally efficient, and expand their capabilities to non-Gaussian distributions.
Another challenge associated with structural health monitoring is the amount of uncertainties and variabilities inherently present in the system, measurements and/or inputs. The second part of this dissertation aims at demonstrating that online Bayesian filtering algorithms are very well-suited for SHM applications due to their ability to accurately quantify and take into account these uncertainties in the learning process. First, these algorithms are well-suited to address ill-conditioned problems, where not all parameters can be learnt from the available noisy data, a problem which frequently arises when considering large dimensional nonlinear systems. Then, in the case of unknown stochastic inputs, a method is derived to take into account in this sequential filtering framework unmeasured stationary excitations whose spectral properties are known but uncertain
Robust multi-objective optimisation of a descent guidance strategy for a TSTO spaceplane
This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, MODHOC, which combines the Direct Finite Elements transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for the Orbital-500R, a commercial semi-reusable, two-stage launch system under development by Orbital Access Lt
Recursive neuro fuzzy techniques for online identification and control
Dissertação para obtenção do Grau de Mestre em
Engenharia Electrotécnica e de ComputadoresThe main goal of this thesis will be focused on developing an adaptative closed loop
control solution, using fuzzy methodologies. A positive theoretical and experimental
contribution, regarding modelling and control of fuzzy and neuro fuzzy systems, is expected
to be achieved.
Proposed non-linear identification solution will use for modelling and control, a recurrent
neuro fuzzy architecture. Regarding model solution, a state space approach will be
considered during fuzzy consequent local models design. Developed controller will be
based on model parameters, being expected not only a stable closed loop solution, but
also a static error with convergence towards zero. Model and controller fuzzy subspaces,
will be partitioned throughout process dynamical universe, allowing fuzzy local models
and controllers commutation and aggregation.
With the aim of capturing process under control dynamics using a real time approach,
the use of recursive optimization techniques are to be adopted. Such methods will be
applied during parameter and state estimation, using a dual decoupled Kalman filter extended
with unscented transformation.
Two distinct processes one single-input (SISO) other multi-input (MIMO), will be used
during experimentation. It is expected from experiments, a practical validation of proposed
solution capabilities for control and identification. Presented work will not be
completed, without first presenting a global analysis of adopted concepts and methods,
describing new perspectives for future investigations
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A new method for generating sigma points and weights for nonlinear filtering
In this paper, a new method termed as new sigma
point Kalman filter (NSKF), is proposed for generating sigma
points and weights for estimating the states of a stochastic
nonlinear dynamic system. The sigma points and their corresponding
weights are generated such that the points nearer to
the mean (in inner product sense) have a higher probability
of occurrence, and the mean vector and covariance matrix are
matched exactly. Performance of the new algorithm is compared
with the existing unscented Kalman filter (UKF), the cubature
Kalman filter (CKF), the cubature quadrature Kalman filter
(CQKF) and higher order unscented filter (HOUF) for two
different problems. Comparison is done by calculating the root
mean square error (RMSE), relative computational time and
track-loss. From simulation results, it can be concluded that the
proposed algorithm performs with superior estimation accuracy
when compared to the UKF, CKF, CQKF and HOUF
Multi-objective optimisation under uncertainty with unscented temporal finite elements
This paper presents a novel method for multi-objective optimisation under uncertainty developed to study a range of mission trade-offs, and the impact of uncertainties on the evaluation of launch system mission designs. A memetic multi-objective optimisation algorithm, named MODHOC, which combines the Direct Finite Elements in Time transcription method with Multi Agent Collaborative Search, is extended to account for model uncertainties. An Unscented Transformation is used to capture the first two statistical moments of the quantities of interest. A quantification model of the uncertainty was developed for the atmospheric model parameters. An optimisation under uncertainty was run for the design of descent trajectories for a spaceplane-based two-stage launch system
Nonlinear estimation with sparse temporal measurements
Nonlinear estimators based on the Kalman filter, the extended Kalman filter (EKF) and unscented Kalman filter (UKF) are commonly used in practical application. The Kalman filter is an optimal estimator for linear systems; the EKF and UKF are sub-optimal approximations of the Kalman filter. The EKF uses a first-order Taylor series approximation to linearize nonlinear models; the UKF uses an approximation of the states' joint probability distribution. Long measurement intervals exacerbate approximation error in each approach, particularly in covariance estimation. EKF and UKF performance under varied measurement frequency is studied through two problems, a single dimension falling body and simple pendulum. The EKF is shown more sensitive to measurement frequency than the UKF in the falling body problem. However, both estimators display insensitivity to measurement frequency in the simple pendulum problem. The literature's lack of consensus as to whether the EKF or UKF is the superior nonlinear estimator may be explained through covariance approximation error. Tools are presented to analyze EKF and UKF measurement frequency sensitivity. Covariance is propagated forward using the approximations of the EKF and UKF. Each propagated covariance is compared for similarity with a Monte Carlo propagation. The similarity of the covariance matrices is shown to predict filter performance. Portions of the state trajectory susceptible to EKF divergence are found using the Frobenius norm of the Jacobian matrix, limiting the need to consider covariance propagation along the entire state trajectory. Long measurement intervals also reveal a commonly overlooked challenge in UKF application: sigma point selection methods may produce sigma point vec-tors that violate physical state constraints. Although the UKF can function under this condition over short measurement intervals, unexpected failure may occur without consideration of physical constraints. A novel constrained UKF, using the scaled unscented transform, is proposed to address this issue.http://archive.org/details/nonlinearestimat1094550547Commander, United States NavyApproved for public release; distribution is unlimited
Personalized noninvasive imaging of volumetric cardiac electrophysiology
Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia