The application of robust estimation methods to the power system state estimation problem was investigated. Techniques using both nonlinear and combinatorial optimization were considered, based on the requirements that the method developed should be statistically robust, and fast enough to be used in a real-time environment. Some basic concepts from robust statistics are introduced. The various estimation methods considered are reviewed, and the implementation of the selected estimator is described. Simulation results for several IEEE test systems are included. Other applications of the proposed technique, such as leverage point identification in large sparse systems, and robust meter placement are described. / Ph. D.

No description available.

Power and effect size analysis are important methods in the psychological sciences. It is well known that classical statistical tests are not robust with respect to power and type II error. However, relatively little attention has been paid in the psychological literature to the effect that non-normality and outliers have on the power of a given statistical test (Wilcox, 1998). Robust measures of location exist that provide much more powerful tests of statistical hypotheses, but their usefulness in power estimation for sample size selection, with real data, is largely unknown. Furthermore, practical approaches to power planning (Cohen, 1988) usually focus on normal theory settings and in general do not make available nonparametric approaches to power and effect size estimation. Beran (1986) proved that it is possible to nonparametrically estimate power for a given statistical test using bootstrap methods (Efron, 1993). However, this method is not widely known or utilized in data analysis settings. This research study examined the practical importance of combining robust measures of location with nonparametric power analysis. Simulation and analysis of real world data sets are used. The present study found that: 1) bootstrap confidence intervals using Mestimators gave shorter confidence intervals than the normal theory counterpart whenever the data had heavy tailed distributions; 2) bootstrap empirical power is higher for Mestimators than the normal theory counterpart when the data had heavy tailed distributions; 3) the smoothed bootstrap controls type I error rate (less than 6%) under the null hypothesis for small sample sizes; and 4) Robust effect sizes can be used in conjuction with Cohen's (1988) power tables to get more realistic sample sizes given that the data distribution has heavy tails.

No description available.

Cette thèse a pour objectif la modélisation et la commande d'un robot parallèle actionné pneumatiquement destiné à des applications de prise et dépose d'objets. Les actionneurs pneumatiques sont des actionneurs à bas coût et ayant des rapports poids/puissance plus importants que les actionneurs électriques. Ceci a pour avantage de réduire le coût de revient du robot en augmentant sa capacité de charge. Cependant, du fait des fortes non linéarités qui les caractérisent (compressibilité de l'air, caractéristique de la valve, frottement, hystérésis ...), le principal obstacle à leur utilisation en robotique est leur commande de façon précise et robuste. C'est pour cela que plusieurs stratégies de commande ont été proposées et implémentées expérimentalement sur un banc d'essai utilisant trois types d'actionneurs pneumatiques: deux vérins et des muscles artificiels travaillant en mode antagoniste. Ces stratégies sont des schémas en cascade qui –après une linéarisant exacte obtenue sur la base du système non linéaire- combinent un contrôleur externe de position et une boucle interne de force (équivalente à la différence de pression) dans le cas des vérins pneumatiques. Pour les muscles artificiels, le même principe est utilisé sauf que la boucle interne de force est remplacée par une boucle qui régule le couple en contrôlant les pressions de chacun des deux muscles. Un contrôleur prédictif généralisé (GPC) est synthétisé pour la boucle de position permettant ainsi de réduire sensiblement les temps de réponse. Pour la boucle interne de pression, un contrôleur robuste multi-objectif combinant des performances H infinie avec des contraintes de placement de pôle a été synthétisé. L'utilisation d'inégalités matricielles affines (LMI) a permis de combiner les objectifs du contrôleur de façon très intuitive. Le choix de ce contrôleur robuste est motivé par la nécessité de rejeter les fortes variations de charge qui caractérisent les applications de prise-et-dépose d'objets. Deux autres stratégies pour la commande prédictive en effort ont été synthétisées sur les vérins et les muscles et ont donné des résultats très encourageants. Les résultats expérimentaux obtenus ont montré l'apport de ces lois de commande en termes de performances (réduction des temps de réponse, erreurs de suivi faibles) et de robustesse (bon rejet des perturbations). Une étude comparative des trois actionneurs testés a conduit au choix du vérin à double effet standard car offrant le meilleur compromis entre performances, robustesse et coût de l'actionneur. En se basant sur ce choix, un nouveau prototype de robot parallèle à deux degrés de liberté utilisant les vérins standards a été conçu, modélisé et commandé en utilisant les différentes stratégies en cascade proposées. L'implémentation expérimentale des algorithmes de commande a conduit à des résultats encourageants en termes de qualité de suivi et de rejet de perturbations. / The thesis objective is the control of a parallel robot driven with pneumatic actuators for pick-and-place applications. The advantage of using pneumatic actuators rather than electrical ones is that they are cheaper and have a bigger power-to-weight ratio which can increase the payload abilities of the robot. However, due to their strong nonlinearities such as air compressibility, valve characteristic, friction, and hysteresis, they are still difficult to control precisely and in a robust way.That is why the main contribution of the thesis is in the control area where different control schemes have been proposed and experimentally implemented on a test bench that involves three types of pneumatic actuators: two cylinders and agonist/antagonist artificial muscles. After the modeling and identification of the nonlinear models, different strategies have been developed: for cylinders, a cascade scheme which uses an outer position control loop and an inner force (or pressure difference) loop is used. For muscles, the inner force loop is replaced by a torque loop controlled by acting on the pressures in each muscle. For position, a Generalized Predictive Controller (GPC) is synthesized reducing sensibly the time responses. For the inner pressure loop, an LMI based multi objective controller is synthesized combining H infinity performances and pole placement constraints. The choice of a robust controller is motivated by the necessity of rejecting load variation disturban ces that characterize pick-and-place applications. On the other hand, two predictive control strategies with feedback linearization were implemented showing very encouraging results.The different experimental results have shown the interest of such strategies in terms of performances (time response reduction, good dynamic tracking) and robustness (disturbance rejection). The comparison of the three tested actuators led to the choice of the standard double acting cylinder because it offers the best compromise in terms of performances and cost. This cylinder was then used to design a planar parallel robot and the implementation of the proposed cascade strategies. The experimental tests showed encouraging results in terms of disturbance rejection and ability of tracking dynamic references and performing pick-and-place cycles.

In modern cyber-physical systems (CPSs) where the control signals are generally transmitted via shared communication networks, there is a desire to balance the closed-loop control performance with the communication cost necessary to achieve it. In this context, aperiodic real-time scheduling of control tasks comes into being and has received increasing attention recently. It is well known that model predictive control (MPC) is currently widely utilized in industrial control systems and has greatly increased profits in comparison with the proportional integral-derivative (PID) control. As communication and networks play more and more important roles in modern society, there is a great trend to upgrade and transform traditional industrial systems into CPSs, which naturally requires extending conventional MPC to communication-efficient MPC to save network resources. Motivated by this fact, we in this thesis propose robust MPC and scheduling co-design algorithms to networked CPSs possibly affected by both parameter uncertainties and additive disturbances. In Chapter 2, a dynamic event-triggered robust tube-based MPC for constrained linear systems with additive disturbances is developed, where a time-varying pre-stabilizing gain is obtained by interpolating multiple static state feedbacks and the interpolating coefficient is determined via optimization at the time instants when the MPC-based control is triggered. The original constraints are properly tightened to achieve robust constraint optimization and a sequence of dynamic sets used to test events are derived according to the optimized coefficient. We theoretically show that the proposed algorithm is recursively feasible and the closed-loop system is input-to-state stable (ISS) in the attraction region. Numerical results are presented to verify the design. In Chapter 3, a self-triggered min-max MPC strategy is developed for constrained nonlinear systems subject to both parametric uncertainties and additive disturbances, where the robust constraint satisfaction is achieved by considering the worst case of all possible uncertainty realizations. First, we propose a new cost function that relaxes the penalty on the system state in a time period where the controller will not be invoked. With this cost function, the next triggering time instant can be obtained at current time instant by solving a min-max optimization problem where the maximum triggering period becomes a decision variable. The proposed strategy is proved to be input-to-state practical stable (ISpS) in the attraction region at triggering time instants under some standard assumptions. Extensions are made to linear systems with additive disturbances, for which the conditions reduce to a linear matrix inequality (LMI). Comprehensive numerical experiments are performed to verify the correctness of the theoretical results. / Graduate

Due to its generality and flexibility, the state-space model has become one of the most popular models in modern time domain analysis for the description and prediction of time series data. The model is often used to characterize processes that can be conceptualized as "signal plus noise," where the realized series is viewed as the manifestation of a latent signal that has been corrupted by observation noise. In the state-space framework, parameter estimation is generally accomplished by maximizing the innovations Gaussian log-likelihood. The maximum likelihood estimator (MLE) is efficient when the normality assumption is satisfied. However, in the presence of contamination, the MLE suffers from a lack of robustness. Basu, Harris, Hjort, and Jones (1998) introduced a discrepancy measure (BHHJ) with a non-negative tuning parameter that regulates the trade-off between robustness and efficiency. In this manuscript, we propose a new parameter estimation procedure based on the BHHJ discrepancy for fitting state-space models. As the tuning parameter is increased, the estimation procedure becomes more robust but less efficient. We investigate the performance of the procedure in an illustrative simulation study. In addition, we propose a numerical method to approximate the asymptotic variance of the estimator, and we provide an approach for choosing an appropriate tuning parameter in practice. We justify these procedures theoretically and investigate their efficacy in simulation studies. Based on the proposed parameter estimation procedure, we then develop a new model selection criterion in the state-space framework. The traditional Akaike information criterion (AIC), where the goodness-of-fit is assessed by the empirical log-likelihood, is not robust to outliers. Our new criterion is comprised of a goodness-of-fit term based on the empirical BHHJ discrepancy, and a penalty term based on both the tuning parameter and the dimension of the candidate model. We present a comprehensive simulation study to investigate the performance of the new criterion. In instances where the time series data is contaminated, our proposed model selection criterion is shown to perform favorably relative to AIC. Lastly, using the BHHJ discrepancy based on the chosen tuning parameter, we propose two versions of an influence diagnostic in the state-space framework. Specifically, our diagnostics help to identify cases that influence the recovery of the latent signal, thereby providing initial guidance and insight for further exploration. We illustrate the behavior of these measures in a simulation study.

The thesis focuses on the analysis of various extensions of the classical multi-period single-item stochastic inventory problem. Specifically, we investigate two particular approaches of modeling risk in the context of inventory management: risk-averse models and robust formulations. We analyze the classical newsvendor problem utilizing a coherent risk measure as the objective function. Properties of coherent risk measures allow us to offer a unifying treatment of risk averse and min-max type formulations. We show that the structure of the optimal policy of the risk-averse model is similar to that of the classical expected value problem for both single and multi-period cases. The result carries over even when there is a fixed ordering cost. We expand our analysis to robust formulations of multi-period inventory problems. We consider both independent and dependent uncertainty sets and prove the optimality of base-stock policies for the general problem formulation. We focus on budget of uncertainty approach and develop a heuristic that can also be employed for a class of parametric dependency structures. We compare our proposed heuristic against alternative solution techniques.

Traditional methods in engineering design involve producing solutions at a single level. However, in complex engineering design problems, such as concurrent product and materials design, various levels of model complexity are considered. A design process in which design problems are defined and analyzed at various levels of design complexity is referred to as multilevel design. One example of multilevel design is the design of a material, product, assembly, and system. Dividing a design problem into multiple levels increases the possibility for introducing and propagating uncertainty. Design solutions that perform predictably in the presence of uncertainty are robust designs. Robust design concepts that were originally developed for designs at a single level can be applied to a multilevel design process. The Inductive Design Exploration Method (IDEM) is an existing design method used to produce robust multilevel design solutions. In this thesis, the strategy presented in IDEM is incorporated into design templates in order to extend its overall usefulness. Design templates are generic, reusable, modules that provide the theoretical and computational framework for solving design problems. Information collected, stored, and analyzed from design templates is leveraged for a variety of design problems. In this thesis, the possibilities of a template-based approach to multilevel design are explored. Two example problems, which employ the developed multilevel robust design template, are considered. Multilevel design templates are created for the design of a cantilever beam and its associated material and the design of a blast resistant panel. The design templates developed for example problems can be extended to facilitate a generic, modular, template-based approach to multilevel robust design.

Networked control systems (NCSs) present many advantages such as easy installation and maintenance, flexible layouts and structures of components, and efficient allocation and distribution of resources. Consequently, they find potential applications in a variety of emerging industrial systems including multi-agent systems, power grids, tele-operations and cyber-physical systems. The study of NCSs with nonlinear dynamics (i.e., nonlinear NCSs) is a very significant yet challenging topic, and it not only widens application areas of NCSs in practice, but also extends the theoretical framework of NCSs with linear dynamics (i.e., linear NCSs). Numerous issues are required to be resolved towards a fully-fledged theory of industrial nonlinear NCS design. In this dissertation, three important problems of nonlinear NCSs are investigated: The robust filtering problem, the robust model predictive control (MPC) problem and the robust distributed MPC problem of large-scale nonlinear systems. In the robust filtering problem of nonlinear NCSs, the nonlinear system model is subject to uncertainties and external disturbances, and the measurements suffer from time delays governed by a Markov process. Utilizing the Lyapunov theory, the algebraic Hamilton-Jacobi inequality (HJI)-based sufficient conditions are established for designing the H_infty nonlinear filter. Moreover, the developed results are specialized for a special type of nonlinear systems, by presenting the HJI in terms of matrix inequalities. For the robust MPC problem of NCSs, three aspects are considered. Firstly, to reduce the computation and communication load, the networked MPC scheme with an efficient transmission and compensation strategy is proposed, for constrained nonlinear NCSs with disturbances and two-channel packet dropouts. A novel Lyapunov function is constructed to ensure the input-to-state practical stability (ISpS) of the closed-loop system. Secondly, to improve robustness, a networked min-max MPC scheme are developed, for constrained nonlinear NCSs subject to external disturbances, input and state constraints, and network-induced constraints. The ISpS of the resulting nonlinear NCS is established by constructing a new Lyapunov function. Finally, to deal with the issue of unavailability of system state, a robust output feedback MPC scheme is designed for constrained linear systems subject to periodical measurement losses and external disturbances. The rigorous feasibility and stability conditions are established. For the robust distributed MPC problem of large-scale nonlinear systems, three steps are taken to conduct the studies. In the first step, the issue of external disturbances is addressed. A robustness constraint is proposed to handle the external disturbances, based on which a novel robust distributed MPC algorithm is designed. The conditions for guaranteeing feasibility and stability are established, respectively. In the second step, the issue of communication delays are dealt with. By designing the waiting mechanism, a distributed MPC scheme is proposed, and the feasibility and stability conditions are established. In the third step, the robust distributed MPC problem for large-scale nonlinear systems subject to control input constraints, communication delays and external disturbances are studied. A dual-mode robust distributed MPC strategy is designed to deal with the communication delays and the external disturbances simultaneously, and the feasibility and the stability conditions are developed, accordingly. / Graduate / 0548 / 0544