All practical control systems are subject to constraints, namely constraints aris¬ing from the actuator’s limited range and rate capacity (input constraints) or from imposed operational limits on plant variables (output constraints). A linear control system typically yields the desirable small signal performance. However, the presence of input constraints often causes undesirable large signal behavior and potential insta¬bility. An anti-windup control consists of a remedial solution that mitigates the effect of input constraints on the closed-loop without affecting the small signal behavior. Conversely, an override control addresses the control problem involving output con¬straints and also follows the idea that large signal control objectives do not alter small signal performance. Importantly, these two remedial control methodologies must in¬corporate model uncertainty into their design to be considered reliable in practice. In this dissertation, shared principles of design for the remedial compensation problem are identified which simplify the picture when analyzing, comparing and synthesiz¬ing for the variety of existing remedial schemes. Two performance objectives, each one related to a different type of remedial compensation, and a general structural representation associated with both remedial compensation problems will be consid¬ered. The effect of remedial control on the closed-loop will be evaluated in terms of two general frameworks which permit the unification and comparison of all known remedial compensation schemes. The difference systems describing the performance objectives will be further employed for comparison of remedial compensation schemes under uncertainty considerations and also for synthesis of compensators. On the ba¬sis of the difference systems and the general structure for remedial compensation, systematic remedial compensation synthesis algorithms for anti-windup and override compensation will be given and compared. Successful application of the proposed robust remedial control synthesis algorithms will be demonstrated via simulation.

All practical control systems are subject to constraints, namely constraints aris¬ing from the actuator’s limited range and rate capacity (input constraints) or from imposed operational limits on plant variables (output constraints). A linear control system typically yields the desirable small signal performance. However, the presence of input constraints often causes undesirable large signal behavior and potential insta¬bility. An anti-windup control consists of a remedial solution that mitigates the effect of input constraints on the closed-loop without affecting the small signal behavior. Conversely, an override control addresses the control problem involving output con¬straints and also follows the idea that large signal control objectives do not alter small signal performance. Importantly, these two remedial control methodologies must in¬corporate model uncertainty into their design to be considered reliable in practice. In this dissertation, shared principles of design for the remedial compensation problem are identified which simplify the picture when analyzing, comparing and synthesiz¬ing for the variety of existing remedial schemes. Two performance objectives, each one related to a different type of remedial compensation, and a general structural representation associated with both remedial compensation problems will be consid¬ered. The effect of remedial control on the closed-loop will be evaluated in terms of two general frameworks which permit the unification and comparison of all known remedial compensation schemes. The difference systems describing the performance objectives will be further employed for comparison of remedial compensation schemes under uncertainty considerations and also for synthesis of compensators. On the ba¬sis of the difference systems and the general structure for remedial compensation, systematic remedial compensation synthesis algorithms for anti-windup and override compensation will be given and compared. Successful application of the proposed robust remedial control synthesis algorithms will be demonstrated via simulation.

Proefschrift Universiteit van Amsterdam. / Met lit. opg. - Met samenvatting in het Nederlands.

No description available.

No description available.

Non-consequentialists agree that Luke may not kill Lorelai in order to prevent Kirk from killing Richard and Emily. According to this view, Luke faces a deontic constraint: he is forbidden from killing Lorelai, even though doing so will bring about fewer killings overall. The justification of constraints, in my view, faces two challenges. First, constraints must meet the Irrationality Challenge: it needs to be demonstrated that there is nothing inconsistent about the claim that Luke should allow more killings to come about. And, secondly, a successful explanation of constraints must meet the Authority Challenge: we need to know why Luke’s reason not to kill Lorelai is normatively categorical. This dissertation takes up different aspects of Authority Challenge. The first introductory chapter aims to motivate the question of authority as a pressing challenge to non-consequentialism. I argue that the violation of constraints is not just motivated by the thought that they are rationally inconsistent, but by the claim that their intuitive importance cannot be explained. Chapters two and three take up the connection between the authority of constraints and their interpersonal character. In chapter two, I argue that Stephen Darwall’s account of the second-person standpoint cannot yield an account of constraints that satisfies the Authority Challenge and that T.M. Scanlon’s contractualism offers us a better way of accounting for the interpersonal significance of constraints. Chapter three argues that Frances Kamm’s inviolability approach cannot be reconciled with the intuitive distinction between acting wrongly and wronging someone. The arguments of this chapter are meant to demonstrate that in order for wronging to carry any normative significance, it must play a foundational role in our account of permissibility. The fourth chapter argues that Moderate deontologists—those who posit a threshold on the killing of the innocent—cannot make sense of the intuitive authority of deontic constraints. The failure of Moderate deontology, I argue, reveals the overlooked appeal of Absolutism. The fifth chapter argues that the authority of restrictions extends to a prohibition on killing non-responsible threats. I argue that a prohibition on killing non-responsible threats accords with the demands of fairness. / Thesis (Ph.D, Philosophy) -- Queen's University, 2013-08-29 10:37:45.739

Master of Science / Department of Physics / Bharat Ratra / Recent observations in cosmology suggest that the universe is undergoing accelerating expansion. Mysterious component responsible for acceleration is called "Dark Energy" contributing to 70% of total energy density of the universe. Simplest DE model is [Lambda]CDM, where Einstein’s cosmological constant plays role of the dark energy. Despite the fact that it is consistent with observational data, it leaves some important theoretical questions unanswered. To overcome these difficulties different Dark energy models are proposed. Two of these models XCDM parametrization and slow rolling scalar field model [phi]CDM, along with "standard" [Lambda]CDM are disscussed here, constraining their parameter set. In this thesis we start with a general theoretical overview of basic ideas and distance measures in cosmology. In the following chapters we use H II starburst galaxy apparent magnitude versus redshift data from Siegel et al.(2005) to constrain DE model parameters. These constraints are generally consistent with those derived using other data sets, but are not as restrictive as the tightest currently available constraints. Also we constrain above mentioned cosmological models in light of 32 age measurements of passively evolving galaxies as a function of redshift and recent estimates of the product of the cosmic microwave background acoustic scale and the baryon acoustic oscillation peak scale.

Siu Fai Keung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 79-83). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- (Weighted) Constraint Satisfaction --- p.1 / Chapter 1.2 --- Set Variables --- p.2 / Chapter 1.3 --- Motivations and Goals --- p.3 / Chapter 1.4 --- Overview of the Thesis --- p.4 / Chapter 2 --- Background --- p.6 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.6 / Chapter 2.1.1 --- Backtracking Tree Search --- p.8 / Chapter 2.1.2 --- Consistency Notions --- p.10 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.14 / Chapter 2.2.1 --- Branch and Bound Search --- p.17 / Chapter 2.2.2 --- Consistency Notions --- p.19 / Chapter 2.3 --- Classical CSPs with Set Variables --- p.23 / Chapter 2.3.1 --- Set Variables and Set Domains --- p.24 / Chapter 2.3.2 --- Set Constraints --- p.24 / Chapter 2.3.3 --- Searching with Set Variables --- p.26 / Chapter 2.3.4 --- Set Bounds Consistency --- p.27 / Chapter 3 --- Weighted Constraint Satisfaction with Set Variables --- p.30 / Chapter 3.1 --- Set Variables --- p.30 / Chapter 3.2 --- Set Domains --- p.31 / Chapter 3.3 --- Set Constraints --- p.31 / Chapter 3.3.1 --- Zero-arity Constraint --- p.33 / Chapter 3.3.2 --- Unary Constraints --- p.33 / Chapter 3.3.3 --- Binary Constraints --- p.36 / Chapter 3.3.4 --- Ternary Constraints --- p.36 / Chapter 3.3.5 --- Cardinality Constraints --- p.37 / Chapter 3.4 --- Characteristics --- p.37 / Chapter 3.4.1 --- Space Complexity --- p.37 / Chapter 3.4.2 --- Generalization --- p.38 / Chapter 4 --- Consistency Notions and Algorithms for Set Variables --- p.41 / Chapter 4.1 --- Consistency Notions --- p.41 / Chapter 4.1.1 --- Element Node Consistency --- p.41 / Chapter 4.1.2 --- Element Arc Consistency --- p.43 / Chapter 4.1.3 --- Element Hyper-arc Consistency --- p.43 / Chapter 4.1.4 --- Weighted Cardinality Consistency --- p.45 / Chapter 4.1.5 --- Weighted Set Bounds Consistency --- p.46 / Chapter 4.2 --- Consistency Enforcing Algorithms --- p.47 / Chapter 4.2.1 --- "Enforcing Element, Node Consistency" --- p.48 / Chapter 4.2.2 --- Enforcing Element Arc Consistency --- p.51 / Chapter 4.2.3 --- Enforcing Element Hyper-arc Consistency --- p.52 / Chapter 4.2.4 --- Enforcing Weighted Cardinality Consistency --- p.54 / Chapter 4.2.5 --- Enforcing Weighted Set Bounds Consistency --- p.56 / Chapter 5 --- Experiments --- p.59 / Chapter 5.1 --- Modeling Set Variables Using 0-1 Variables --- p.60 / Chapter 5.2 --- Softening the Problems --- p.61 / Chapter 5.3 --- Steiner Triple System --- p.62 / Chapter 5.4 --- Social Golfer Problem --- p.63 / Chapter 5.5 --- Discussions --- p.66 / Chapter 6 --- Related Work --- p.68 / Chapter 6.1 --- Other Consistency Notions in WCSPs --- p.68 / Chapter 6.1.1 --- Full Directional Arc Consistency --- p.68 / Chapter 6.1.2 --- Existential Directional Arc Consistency --- p.69 / Chapter 6.2 --- Classical CSPs with Set Variables --- p.70 / Chapter 6.2.1 --- Bounds Reasoning --- p.70 / Chapter 6.2.2 --- Cardinality Reasoning --- p.70 / Chapter 7 --- Concluding Remarks --- p.72 / Chapter 7.1 --- Contributions --- p.72 / Chapter 7.2 --- Future Work --- p.74 / List of Symbols --- p.76 / Bibliography --- p.79

Woo Hiu Chun. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 91-99). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Weighted Constraint Satisfaction Problems --- p.3 / Chapter 1.3 --- Redundant Modeling --- p.4 / Chapter 1.4 --- Motivations and Goals --- p.5 / Chapter 1.5 --- Outline of the Thesis --- p.6 / Chapter 2 --- Background --- p.8 / Chapter 2.1 --- Constraint Satisfaction Problems --- p.8 / Chapter 2.1.1 --- Backtracking Tree Search --- p.9 / Chapter 2.1.2 --- Local Consistencies --- p.12 / Chapter 2.1.3 --- Local Consistencies in Backtracking Search --- p.17 / Chapter 2.1.4 --- Permutation CSPs --- p.19 / Chapter 2.2 --- Weighted Constraint Satisfaction Problems --- p.20 / Chapter 2.2.1 --- Branch and Bound Search --- p.23 / Chapter 2.2.2 --- Local Consistencies --- p.26 / Chapter 2.2.3 --- Local Consistencies in Branch and Bound Search --- p.32 / Chapter 2.3 --- Redundant Modeling --- p.34 / Chapter 3 --- Generating Redundant WCSP Models --- p.37 / Chapter 3.1 --- Model Induction for CSPs --- p.38 / Chapter 3.1.1 --- Stated Constraints --- p.39 / Chapter 3.1.2 --- No-Double-Assignment Constraints --- p.39 / Chapter 3.1.3 --- At-Least-One-Assignment Constraints --- p.40 / Chapter 3.2 --- Generalized Model Induction for WCSPs --- p.43 / Chapter 4 --- Combining Mutually Redundant WCSPs --- p.47 / Chapter 4.1 --- Naive Approach --- p.47 / Chapter 4.2 --- Node Consistency Revisited --- p.51 / Chapter 4.2.1 --- Refining Node Consistency Definition --- p.52 / Chapter 4.2.2 --- Enforcing m-NC* c Algorithm --- p.55 / Chapter 4.3 --- Arc Consistency Revisited --- p.58 / Chapter 4.3.1 --- Refining Arc Consistency Definition --- p.60 / Chapter 4.3.2 --- Enforcing m-AC*c Algorithm --- p.62 / Chapter 5 --- Experiments --- p.67 / Chapter 5.1 --- Langford's Problem --- p.68 / Chapter 5.2 --- Latin Square Problem --- p.72 / Chapter 5.3 --- Discussion --- p.75 / Chapter 6 --- Related Work --- p.77 / Chapter 6.1 --- Soft Constraint Satisfaction Problems --- p.77 / Chapter 6.2 --- Other Local Consistencies in WCSPs --- p.79 / Chapter 6.2.1 --- Full Arc Consistency --- p.79 / Chapter 6.2.2 --- Pull Directional Arc Consistency --- p.81 / Chapter 6.2.3 --- Existential Directional Arc Consistency --- p.82 / Chapter 6.3 --- Redundant Modeling and Channeling Constraints --- p.83 / Chapter 7 --- Concluding Remarks --- p.85 / Chapter 7.1 --- Contributions --- p.85 / Chapter 7.2 --- Future Work --- p.87 / List of Symbols --- p.88 / Bibliography

Lam Yee Gordon. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves 109-117). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Constraint Satisfaction Problems --- p.1 / Chapter 1.2 --- Motivations and Goals --- p.2 / Chapter 1.3 --- Outline of the Thesis --- p.4 / Chapter 2 --- Background --- p.5 / Chapter 2.1 --- CSP --- p.5 / Chapter 2.2 --- Classes of Variable --- p.6 / Chapter 2.3 --- Solution of a CSP --- p.7 / Chapter 2.4 --- Constraint Solving Techniques --- p.8 / Chapter 2.4.1 --- Local Consistencies --- p.8 / Chapter 2.4.2 --- Constraint Tightness --- p.10 / Chapter 2.4.3 --- Tree Search --- p.10 / Chapter 2.5 --- Graph --- p.14 / Chapter 3 --- Common Channeling Constraints --- p.16 / Chapter 3.1 --- Models --- p.16 / Chapter 3.2 --- Channeling Constraints --- p.17 / Chapter 3.2.1 --- Int-Int Channeling Constraint (II) --- p.18 / Chapter 3.2.2 --- Set-Int Channeling Constraint (SI) --- p.21 / Chapter 3.2.3 --- Set-Set Channeling Constraint (SS) --- p.24 / Chapter 3.2.4 --- Int-Bool Channeling Constraint (IB) --- p.25 / Chapter 3.2.5 --- Set-Bool Channeling Constraint (SB) --- p.27 / Chapter 3.2.6 --- Discussions --- p.29 / Chapter 4 --- Realization in Existing Solvers --- p.31 / Chapter 4.1 --- Implementation by if-and-only-if constraint --- p.32 / Chapter 4.1.1 --- "Realization of iff in CHIP, ECLiPSe, and SICStus Prolog" --- p.32 / Chapter 4.1.2 --- Realization of iff in Oz and ILOG Solver --- p.32 / Chapter 4.2 --- Implementations by Element Constraint --- p.38 / Chapter 4.2.1 --- "Realization of ele in CHIP, ECLiPSe, and SICStus Prolog" --- p.40 / Chapter 4.2.2 --- Realization of ele in Oz and ILOG Solver --- p.40 / Chapter 4.3 --- Global Constraint Implementations --- p.41 / Chapter 4.3.1 --- "Realization of glo in CHIP, SICStus Prolog, and ILOG Solver" --- p.42 / Chapter 5 --- Consistency Levels --- p.43 / Chapter 5.1 --- Int-Int Channeling (II) --- p.44 / Chapter 5.2 --- Set-Int Channeling (SI) --- p.49 / Chapter 5.3 --- Set-Set Channeling Constraints (SS) --- p.53 / Chapter 5.4 --- Int-Bool Channeling (IB) --- p.55 / Chapter 5.5 --- Set-Bool Channeling (SB) --- p.57 / Chapter 5.6 --- Discussion --- p.59 / Chapter 6 --- Algorithms and Implementation --- p.61 / Chapter 6.1 --- Source of Inefficiency --- p.62 / Chapter 6.2 --- Generalized Element Constraint Propagators --- p.63 / Chapter 6.3 --- Global Channeling Constraint --- p.66 / Chapter 6.3.1 --- Generalization of Existing Global Channeling Constraints --- p.66 / Chapter 6.3.2 --- Maintaining GAC on Int-Int Channeling Constraint --- p.68 / Chapter 7 --- Experiments --- p.72 / Chapter 7.1 --- Int-Int Channeling Constraint --- p.73 / Chapter 7.1.1 --- Efficient AC implementations --- p.74 / Chapter 7.1.2 --- GAC Implementations --- p.75 / Chapter 7.2 --- Set-Int Channeling Constraint --- p.83 / Chapter 7.3 --- Set-Set Channeling Constraint --- p.89 / Chapter 7.4 --- Int-Bool Channeling Constraint --- p.89 / Chapter 7.5 --- Set-Bool Channeling Constraint --- p.91 / Chapter 7.6 --- Discussion --- p.93 / Chapter 8 --- Related Work --- p.101 / Chapter 8.1 --- Empirical Studies --- p.101 / Chapter 8.2 --- Theoretical Studies --- p.102 / Chapter 8.3 --- Applications --- p.103 / Chapter 8.4 --- Other Kinds of Channeling Constraints --- p.104 / Chapter 9 --- Concluding Remarks --- p.106 / Chapter 9.1 --- Contributions --- p.106 / Chapter 9.2 --- Future Work --- p.108 / Bibliography --- p.109