Networked control systems (NCSs) are a kind of distributed control systems in which the data between control components are exchanged via communication networks. Because of the attractive advantages of NCSs such as reduced system wiring, low weight, and ease of system diagnosis and maintenance, the research on NCSs has received much attention in recent years. The first part (Chapter 2 - Chapter 4) of the thesis is devoted to designing new controllers for NCSs by incorporating the network-induced delays. The thesis also conducts research on filtering of multirate systems and identification of Hammerstein systems in the second part (Chapter 5 - Chapter 6).<br /><br /> Network-induced delays exist in both sensor-to-controller (S-C) and controller-to-actuator (C-A) links. A novel two-mode-dependent control scheme is proposed, in which the to-be-designed controller depends on both S-C and C-A delays. The resulting closed-loop system is a special jump linear system. Then, the conditions for stochastic stability are obtained in terms of a set of linear matrix inequalities (LMIs) with nonconvex constraints, which can be efficiently solved by a sequential LMI optimization algorithm. Further, the control synthesis problem for the NCSs is considered. The definitions of <em>H₂</em> and <em>H_∞</em> norms for the special system are first proposed. Also, the plant uncertainties are considered in the design. Finally, the robust mixed <em>H₂/H_&infin;</em> control problem is solved under the framework of LMIs. <br /><br /> To compensate for both S-C and C-A delays modeled by Markov chains, the generalized predictive control method is modified to choose certain predicted future control signal as the current control effort on the actuator node, whenever the control signal is delayed. Further, stability criteria in terms of LMIs are provided to check the system stability. The proposed method is also tested on an experimental hydraulic position control system. <br /><br /> Multirate systems exist in many practical applications where different sampling rates co-exist in the same system. The <em>l₂-l_&infin;</em> filtering problem for multirate systems is considered in the thesis. By using the lifting technique, the system is first transformed to a linear time-invariant one, and then the filter design is formulated as an optimization problem which can be solved by using LMI techniques. <br /><br /> Hammerstein model consists of a static nonlinear block followed in series by a linear dynamic system, which can find many applications in different areas. New switching sequences to handle the two-segment nonlinearities are proposed in this thesis. This leads to less parameters to be estimated and thus reduces the computational cost. Further, a stochastic gradient algorithm based on the idea of replacing the unmeasurable terms with their estimates is developed to identify the Hammerstein model with two-segment nonlinearities. <br /><br /> Finally, several open problems are listed as the future research directions.

In this thesis, we first present a variance estimation technique based on the standardized time series methodology for steady-state simulations. The proposed variance estimator has competitive bias and variance compared to the existing estimators in the literature. We also present the technique of rebatching to further reduce the bias and variance of our variance estimator. Second, we present two fully sequential indifference-zone procedures to select the best system from a number of competing simulated systems when best is defined by the maximum or minimum expected performance. These two procedures have parabola shaped continuation regions rather than the triangular continuation regions employed in several papers. The rocedures we present accommodate unequal and unknown ariances across systems and the use of common random numbers. However, we assume that basic observations are independent and identically normally distributed. Finally, we present procedures for finding a set of feasible or near-feasible systems among a finite number of simulated systems in the presence of multiple stochastic constraints, especially when the number of systems or constraints is large.

The biodegradation kinetics of Geropon TC-42 (trademark) by an acclimated culture was investigated in anoxic batch reactors to determine biokinetic coefficients to be implemented in two biofilm mathematical models. Geropon TC-42 (trademark) is the surfactant commonly used in space habitation. The two biofilm models differ in that one assumes a constant biofilm density and the other allows biofilm density changes based on space occupancy theory. Extant kinetic analysis of a mixed microbial culture using Geropon TC-42 (trademark) as sole carbon source was used to determine cell yield, specific growth rate, and the half-saturation constant for S0/X0 ratios of 4, 12.5, and 34.5. To estimate cell yield, linear regression analysis was performed on data obtained from three sets of simultaneous batch experiments for three S0/X0 ratios. The regressions showed non-zero intercepts, suggesting that cell multiplication is not possible at low substrate concentrations. Non-linear least-squares analysis of the integrated equation was used to estimate the specific growth rate and the half-saturation constant. Net specific growth rate dependence on substrate concentration indicates a self-inhibitory effect of Geropon TC-42 (trademark). The flow rate and the ratio of the concentrations of surfactant to nitrate were the factors that most affected the simulations. Higher flow rates resulted in a shorter hydraulic retention time, shorter startup periods, and faster approach to a steady-state biofilm. At steady-state, higher flow resulted in lower surfactant removal. Higher influent surfactant/nitrate concentration ratios caused a longer startup period, supported more surfactant utilization, and biofilm growth. Both models correlate to the empirical data. A model assuming constant biofilm density is computationally simpler and easier to implement. Therefore, a suitable anoxic packed bed reactor for the removal of the surfactant Geropon TC-42 (trademark) can be designed by using the estimated kinetic values and a model assuming constant biofilm density.

The work presented in this dissertation centers on the theme of regression and computation methodology. Functional data is an important class of longitudinal data, and principal component analysis is an important approach to regression with this type of data. Here we present an additive hierarchical bivariate functional data model employing principal components to identify random e ects. This additive model extends the univariate functional principal component model. These models are implemented in the pfda package for R. To t the curves from this class of models orthogonalized spline basis are used to reduce the dimensionality of the t, but retain exibility. Methods for handing spline basis functions in a purely analytical manner, including the orthogonalizing process and computing of penalty matrices used to t the principal component models are presented. The methods are implemented in the R package orthogonalsplinebasis. The projects discussed involve complicated coding for the implementations in R. To facilitate this I created the NppToR utility to add R functionality to the popular windows code editor Notepad . A brief overview of the use of the utility is also included.

Unmanned Underwater Vehicles (UUV) in open-seas are highly nonlinear with system motions. Because of the complex interaction of the body with environment it is difficult to control them efficiently. Linearization is applied to system in order to design controllers developed for linear systems. To overcome the effects of disturbances, a mathematical model which will compensate all disturbances and effects of linearization is required. In this study first a mathematical model is formed wherein the linear and nonlinear hydrodynamic coeffi- cients are calculated with strip theory. After the basic mathematical model is developed, it is simplified and decoupled into speed, steering and diving subsystems. Consequently PID (Proportional Derivative Integral), SMC (SlidingMode Control) and LQR (Linear Quadratic Regulator)/LQG (Linear Quadratic Gaussian) control methods can be applied on each subsystem to design controllers. Some of the system parameters can be estimated from state vector data based on measurements using the methods of linear sequential estimation and genetic algorithms. As for the final part of the study, an online obstacle avoidance algorithm which avoids local optimums using Boolean operators is presented. In addition a simple guidance algorithm is suggested for waypoint navigation. Due to the fact that ULISAR UUV is still on construction phase, we were unable to test our algorithms. But in the near future, we plan to study all these algorithms on the UUV ULISAR.

In some production environments the defect rates are considerably low such that measurement of fraction of nonconforming items reaches parts per million level. In such environments, monitoring the number of conforming items between consecutive nonconforming items, namely the time between events (TBE) is often suggested. However, in the design of control charts for TBE monitoring a common practice is the assumptions of known process parameters. Nevertheless, in many applications the true values of the process parameters are not known. Their estimates should be determined from a sample obtained from the process at a time when it is expected to operate in a state of statistical control. Additional variability introduced through sampling may significantly effect the performance of a control chart. In this study, the effect of parameter estimation on the performance of Time Between Events Exponentially Weighted Moving Average (TBE EWMA) schemes is examined. Conditional performance is evaluated to show the effect of estimation. Marginal performance is analyzed in order to make recommendations on sample size requirements. Markov chain approach is used for evaluating the results.

In vector control of induction motors it is essential to know the parameters of the motor. Known approaches to this problem have some drawbacks. This thesis work is planned to develop solutions to the existing problems. The proposed solutions will be implemented and tested.

In this thesis a number of improvements have been established for specific methods which utilize sequential Monte Carlo (SMC), aka. Particle filtering (PF) techniques. The first problem is the Bayesian multi-target tracking (MTT) problem for which we propose the use of non-parametric Bayesian models that are based on time varying extension of Dirichlet process (DP) models. The second problem studied in this thesis is an important application area for the proposed DP based MTT method / the tracking of vocal tract resonance frequencies of the speech signals. Lastly, we investigate SMC based parameter estimation problem of nonlinear non-Gaussian state space models in which we provide a performance improvement for the path density based methods by utilizing regularization techniques.

Vehicle handling models are commonly used in the design and analysis of vehicle dynamics. Especially, with the advances in vehicle control systems need for accurate and simple vehicle handling models have increased. These models have parameters, some of which are known or easily obtainable, yet some of which are unknown or difficult to obtain. These parameters are obtained by system identification, which is the study of building model from experimental data. In this thesis, identification of vehicle handling models is based on data obtained from the simulation of complex vehicle dynamics model from ADAMS representing the real vehicle and a general methodology has been developed. Identified vehicle handling models are the linear bicycle model and vehicle roll models with different tire models. Changes of sensitivity of the model outputs to model parameters with steering input frequency have been examined by sensitivity analysis to design the test input. To show that unknown parameters of the model can be identified uniquely, structural identifiability analysis has been performed. Minimizing the difference between the data obtained from the simulation of ADAMS vehicle model and the data obtained from the simulation of simple handling models by mathematical optimization methods, unknown parameters have been estimated and handling models have been identified. Estimation task has been performed using MATLAB Simulink Parameter Estimation Toolbox. By model validation it has been shown that identified handling models represent the vehicle system successfully.

Short range thrusting projectiles are one of the various threats against armored vehicles and helicopters on the battlefield. Developing a countermeasure for this kind of projectiles is very crucial since they are vast in number and easy to operate on the battlefield. A countermeasure may consist of fire point prediction of the projectile and attack the launcher of it, or it may be the impact point prediction of the projectile and apply a hard-kill counter measure on its way to the ally target. For both of the countermeasure concepts, dynamics and parameters of the projectile must be estimated precisely. In this thesis, dynamic models for thrusting and ballistic flight modes of thrusting projectile are obtained. Three different tracking filters are suggested for precise tracking of the projectiles and their estimation performances are compared. These filters are the Extended Kalman Filter (EKF), the Particle Filter (PF) and the Marginalized Particle Filter (MPF).