Poorly damped electromechanical modes detection in a power system and corresponding stability margins prediction are very important in power system planning and operation, and can provide significant help to power system operators with preventing stability problems. <br /><br /> Stochastic subspace identification is proposed in this thesis as a technique to extract the critical mode(s) from the measured ambient noise without requiring artificial disturbances (e. g. a line outage), allowing these critical modes to be used as an on-line index, which is referred here to as System Identification Stability Indices (SISI) to predict the closest oscillatory instability. The SISI is not only independent of system models and truly representative of the actual system, but also computationally efficient. In addition, readily available signals in a power system and several identification methods are categorized, and merits and pitfalls of each one are addressed in this work. <br /><br /> The damping torque of linearized models of power systems is studied in this thesis as another possible on-line security index. This index is estimated by means of proper system identification techniques applied to both power system transient response and ambient noise. The damping torque index is shown to address some of drawbacks of the SISI. <br /><br /> This thesis also demonstrates the connection between the second order statistical properties, including confidence intervals, of the estimated electromechanical modes and the variance of model parameters. These analyses show that Monte-Carlo type of experiments or simulations can be avoided, hence resulting in a significant reduction in the number of samples. <br /><br /> In these types of studies, the models available in simulation packages are extremely important due to their unquestionable impact on modal analysis results. Hence, in this thesis, the validity of generator subtransient model and a typical STATCOM transient stability (TS) model are also investigated by means of system identification, illustrating that under certain conditions the STATCOM TS model can yield results that are too optimistic, which can lead to errors in power system planning and operation. <br /><br /> In addition to several small test systems used throughout this thesis, the feasibility of the proposed indices are tested on a realistic system with 14,000 buses, demonstrating their usefulness in practice.

Blind source separation methods such as independent component analysis (ICA) and second order blind identification (SOBI) have shown considerable potential in the area of ambient vibration system identification. The objective of these methods is to separate the modal responses, or sources, from the measured output responses, without the knowledge of excitation. Several frequency domain and time domain methods have been proposed and successfully implemented in the literature. Whereas frequency-domain methods pose several challenges typical of dealing with signals in the frequency-domain, popular time-domain methods such as NExT/ERA and SSI pose limitations in dealing with noise, low sensor density, modes having low energy content, or in dealing with systems having closely-spaced modes, such as those found in structures with passive energy dissipation devices, for example, tuned mass dampers.Motivated by these challenges, the current research focuses on developing methods to address the problem of separability of sources with low energy content, closely-spaced modes, and under-determined blind identification, that is, when the number of response measurements is less than the number of sources. These methods, requiring the time and frequency diversities of the measured outputs, are referred to as hybrid time and time-frequency source separation methods. The hybrid methods are classified into two categories. In the first one, the basic principles of modified SOBI are extended using the stationary wavelet transform (SWT) in order to improve the separability of sources, thereby improving the quality of identification. In the second category, empirical mode decomposition is employed to extract the intrinsic mode functions from measurements, followed by an estimation of the mode shape matrix using iterative and/or non iterative procedures within the framework of modified-SOBI. Both experimental and large-scale structural simulation results are included to demonstrate the applicability of these hybrid approaches to structural system identification problems.

In order to provide a novel perspective for videography of high speed sporting events, a highly capable trajectory tracking control methodology is developed for a custom designed Kadet Senior Unmanned Aerial Vehicle (UAV). The accompanying high fidelity system identification ensures that accurate flight models are used to design the control laws. A parallel vision based target tracking technique is also demonstrated and implemented on a Graphical Processing Unit (GPU), to assist in real-time tracking of the target. Nonlinear control techniques like feedback linearization require a detailed and accurate system model. This thesis discusses techniques used for estimating these models using data collected during planned test flights. A class of methods known as the Output Error Methods are discussed with extensions for dealing with wind turbulence. Implementation of these methods, including data acquisition details, on the Kadet Senior are also discussed. Results for this UAV are provided. For comparison, additional results using data from a BAC-221 simulation are also provided as well as typical results from the work done at the Dryden Flight Research Center. The proposed controller combines feedback linearization with linear tracking control using the internal model approach, and relies on a trajectory generating exosystem. Three different aircraft models are presented each with increasing levels of complexity, in an effort to identify the simplest controller that yields acceptable performance. The dynamic inversion and linear tracking control laws are derived for each model, and simulation results are presented for tracking of elliptical and periodic trajectories on the Kadet Senior.

Power amplifiers (PAs) are important components of communication systems and are inherently nonlinear. When a non-constant modulus signal goes through a nonlinear PA, spectral regrowth (broadening) appears in the PA output, which in turn causes adjacent channel interference (ACI). Stringent limits on the ACI are imposed by regulatory bodies, and thus the extent of the PA nonlinearity must be controlled. PA linearization is often necessary to suppress spectral regrowth, contain adjacent channel interference, and reduce bit error rate (BER). This dissertation addresses the following aspects of power amplifier research: modeling, linearization, and spectral regrowth analysis. We explore the passband and baseband PA input/output relationships and show that they manifest differently when the PA exhibits long-term, short-term, or no memory effects. The so-called quasi-memoryless case is especially clarified. Four particular nonlinear models with memory are further investigated. We provide experimental results to support our analysis. The benefits of using the orthogonal polynomials as opposed to the conventional polynomials are explored, in the context of digital baseband PA modeling and predistorter design. A closed-form expression for the orthogonal polynomial basis is derived. We demonstrate the improvement in numerical stability associated with the use of orthogonal polynomials for predistortion. Spectral analysis can help to evaluate the suitability of a given PA for amplifying certain signals or to assist in predistortion linearization algorithm design. With the orthogonal polynomials that we derived, spectral analysis of the nonlinear PA becomes a straightforward task. We carry out nonlinear spectral analysis with digitally modulated signal as input. We demonstrate an analytical approach for evaluating the power spectra of filtered QPSK and OQPSK signals after nonlinear amplification. Many communications devices are nonlinear and have a peak power or peak amplitude constraint. In addition to possibly amplifying the useful signal, the nonlinearity also generates distortions. We focus on signal-to-noise-and-distortion ratio (SNDR) optimization within the family of amplitude limited memoryless nonlinearities. We obtain a link between the capacity of amplitude-limited nonlinear channels with Gaussian noise to the SNDR.

Linearized stability analysis methodologies, system identification algorithms and optimal control approaches that are applicable to large scale, flexible multibody dynamic systems are presented in this thesis. For stability analysis, two classes of closely related algorithms based on a partial Floquet approach and on an autoregressive approach, respectively, are presented in a common framework that underlines their similarity and their relationship to other methods. The robustness of the proposed approach is improved by using optimized signals that are derived from the proper orthogonal modes of the system. Finally, a signal synthesis procedure based on the identified frequencies and damping rates is shown to be an important tool for assessing the accuracy of the identified parameters; furthermore, it provides a means of resolving the frequency indeterminacy associated with the eigenvalues of the transition matrix for periodic systems. For system identification, a robust algorithm is developed to construct subspace plant models. This algorithm uniquely combines the methods of minimum realization and subspace identification. It bypasses the computation of Markov parameters because the free impulse response of the system can be directly computed in the present computational environment. Minimum realization concepts were applied to identify the stability and output matrices. On the other hand, subspace identification algorithms construct a state space plant model of linear system by using computationally expensive oblique matrix projection operations. The proposed algorithm avoids this burden by computing the Kalman filter gain matrix and model dependency on external inputs in a small sized subspace. Balanced model truncation and similarity transformation form the theoretical foundation of proposed algorithm. Finally, a forward innovation model is constructed and estimates the input-output behavior of the system within a specified level of accuracy. The proposed system identification algorithms are computationally inexpensive and consist of purely post processing steps that can be used with any multi-physics computational tool or with experimental data. Optimal control methodologies that are applicable to comprehensive large-scale flexible multibody systems are presented. A classical linear quadratic Gaussian controller is designed, including subspace plant identification, the evaluation of linear quadratic regulator feedback gain and Kalman filter gain matrices and online control implementation.

This thesis proposes an adaptive control approach to regulate the hydrogen feed of a fuel cell. The goal of the controller is to maintain the so-called hydrogen excess ratio, defined as the ratio between the hydrogen fed to the cell stake and those consumed in the stake, at a desired level when the fuel cell is under load variation. Maintaining the hydrogen excess ratio at an appropriate level would avoid hydrogen starvation, which is crucial for slowing degeneration of the fuel cell membranes and prolonging the life of the cell stake. The control approach we propose is based on the receding horizon linear quadratic optimal control algorithm with an on-line turning scheme which updates the plant model according to real-time measurement. To ease the computational complexity and make real-time turning realizable, we adopt a simple autoregressive with external disturbance (ARX) model to approximate the complicate chemical/electrical process of the fuel cell. The proposed adaptive control approach is implemented on an experimental platform. The experimental results show that the proposed control works with reasonably good performance.

A basic understanding of the hydrodynamic response behavior of the two-body system is important for a wide variety of offshore operations. This is a complex problem and model tests can provide data that in turn can be used to retrieve key information concerning the response characteristics of such systems. The current study demonstrates that the analysis of these data using a combination of statistical tools and system identification techniques can efficiently recover the main hydrodynamic parameters useful in design. The computation of the statistical parameters, spectral densities and coherence functions provides an overview of the general response behavior of the system. The statistical analysis also guides the selection of the nonlinear terms that will be used in the reverse multi-input / single-output (R-MI/SO) system identification method in this study. With appropriate linear and nonlinear terms included in the equation of motion, the R-MISO technique is able to estimate the main hydrodynamic parameters that characterize the offshore system. In the past, the R-MISO method was primarily applied to single body systems, while in the current study a ship moored to a fixed barge was investigated. The formulation included frequency-dependant hydrodynamic parameters which were evaluated from the experimental measurements. Several issues specific to this extension were addressed including the computation load, the interpretation of the results and the validation of the model. Only the most important cross-coupling terms were chosen to be kept based on the estimation of their energy. It is shown that both the heading and the loading condition can influence system motion behavior and that the impact of the wave in the gap between the two vessels is important. The coherence was computed to verify goodness-of-fit of the model, the results were overall satisfying.

Novel methods of system identification are developed in this dissertation. First set of methods are designed to realize time varying linear dynamical system models from input-output experimental data. The preliminary results obtained in a recent paper by the author are extended to establish a new algorithm called the Time Varying Eigensystem Realization Algorithm (TVERA). The central aim of this algorithm is to obtain a linear, time varying, discrete time model sequence of the dynamic system directly from the input-output data. Important results relating to concepts concerning coordinate systems for linear time varying systems are developed (discrete time theory) and an intuitive understanding of equivalent realizations is provided. A procedure to develop first few time step models is detailed, providing a unified solution to the time varying identification problem. The practical problem of identifying the time varying generalized Markov parameters required for TVERA is presented as the next result. In the process, we generalize the classical time invariant input output AutoRegressive model with an eXogenous input (ARX) models to the time varying case and realize an asymptotically stable observer as a byproduct of the calculations. It is further found that the choice of the generalized time varying ARX model (GTV-ARX) can be set to realize a time varying dead beat observer. Methods to use the developed algorithm(s) in this research are then considered for application to the identification of system models that are bilinear in nature. The fact that bilinear plant models become linear for constant inputs is used in the development of an algorithm that generalizes the classical developments of Juang. An intercept problem is considered as a candidate for application of the time varying identification scheme, where departure motion dynamics model sequence is calculated about a nominal trajectory with suboptimal performance owing to the presence of unstructured perturbations. Control application is subsequently demonstrated. The dynamics of a particle in a rotating tube is considered next for identification using the time varying eigensystem realization algorithm. Continuous time bilinear system identification method is demonstrated using the particle example and the identification of an automobile brake model.

One of the key challenges in the Oil & Gas industry is to best manage reservoirs under different conditions, constrained by production rates based on various economic scenarios, in order to meet energy demands and maximize profit. To address the energy demand challenges, a transformation in the paradigm of the utilization of "real-time" data has to be brought to bear, as one changes from a static decision making to a dynamical and data-driven management of production in conjunction with real-time risk assessment. The use of modern methods of computational modeling and simulation may be the only means to account for the two major tasks involved in this paradigm shift: (1) large-scale computations; and (2) efficient utilization of the deluge of data streams. Recently, history matching and optimization were brought together in the oil industry into an integrated and more structured approach called optimal closed-loop reservoir management. Closed-loop control algorithms have already been applied extensively in other engineering fields, including aerospace, mechanical, electrical and chemical engineering. However, their applications to porous media flow, such as - in the current practices and improvements in oil and gas recovery, in aquifer management, in bio-landfill optimization, and in CO2 sequestration have been minimal due to the large-scale nature of existing problems that generate complex models for controller design and real-time implementation. Their applicability to a realistic field is also an open topic because of the large-scale nature of existing problems that generate complex models for controller design and real-time implementation, hindering its applicability. Basically, three sources of high-dimensionality can be identified from the underlying reservoir models: size of parameter space, size of state space, and the number of scenarios or realizations necessary to account for uncertainty. In this paper we will address type problem of high dimensionality by focusing on the mitigation of the size of the state-space models by means of model-order reduction techniques in a systems framework. We will show how one can obtain accurate reduced order models which are amenable to fast implementations in the closed-loop framework .The research will focus on System Identification (System-ID) (Jansen, 2009) and Model Predictive Control (MPC) (Gildin, 2008) to serve this purpose. A mathematical treatment of System-ID and MPC as applied to reservoir simulation will be presented. Linear MPC would be studied on two specific reservoir models after generating low-order reservoir models using System-ID methods. All the comparisons are provided from a set of realistic simulations using the commercial reservoir simulator called Eclipse. With the improvements in oil recovery and reductions in water production effectively for both the cases that were considered, we could reinforce our stance in proposing the implementation of MPC and System-ID towards the ultimate goal of "real-time" production optimization.

In this study, the use of modified genetic algorithms (MGA) in the parameterization of the Transmission Mechanisms is facilitated. The new algorithm is proposed from the genetic algorithm with some additional strategies, and yields a faster convergence and a more accurate search. Firstly, this near-optimum search technique, MGA-based ID method, is used to identify the parameters of a system described by an ARMAX model in the presence of white noise and to compare with the LMS (Least mean-squares) method and GA method. Then, this proposed algorithm is applied to the identification of the Transmission Mechanisms of DC motor. The parameters of the friction force and DC motor are estimated in a single identification experiment. It is also shown that this technique is capable of identifying the whole transmission system. Finally, the Minimum Variance Controller (MVC) is taken to track the desired speed trajectory and then a comparison to the conventional digital PID controller is shown. Experiment results are included to demonstrate the excellent performance of the MVC.