A thesis submitted for the degree of Doctor of Philosophy 2013 / This thesis presents a range of related pricing kernel models that are driven by incomplete information about a series of future unknowns. These unknowns may, for instance, represent fundamental macroeconomic, political or social random variables that are revealed at future times. They may also represent latent or hidden factors that are revealed asymptotically. We adopt the information-based approach of Brody, Hughston and Macrina (BHM) to model the information processes associated with the random variables. The market filtration is generated collectively by these information processes. By directly modelling the pricing kernel, we generate information-sensitive arbitrage-free models for the term structure of interest rates, the excess rate of return required by investors, and security prices. The pricing kernel is modelled by a supermartingale to ensure that nominal interest rates remain non-negative. To begin with, we primarily investigate finite-time pricing kernel models that are sensitive to Brownian bridge information. The BHM framework for the pricing of credit-risky instruments is extended to a stochastic interest rate setting. In addition, we construct recovery models, which take into consideration information about, for example, the state of the economy at the time of default. We examine various explicit examples of analytically tractable information-driven pricing kernel models. We develop a model that shares many of the features of the rational lognormal model, and investigate examples of heat kernel models. It is shown that these models may result in discount bonds and interest rates being bounded by deterministic functions. In certain situations, incoming information about random variables may exhibit jumps. To this end, we construct a more general class of nite-time pricing kernel models that are driven by Levy random bridges. Finally, we model the aggregate impact of uncertainties on a nancial market by randomised mixtures of Levy and Markov processes respectively. It is assumed that market participants have incomplete information about the underlying random mixture. We apply results from non-linear ltering theory and construct Flesaker-Hughston models and in nite-time heat kernel models based on these randomised mixtures.

A thesis submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, for the degree Doctor of Philosophy. / Real-time computer control is characterized by the need for a high degree of lnteraction between some machine or physical process, its controlling computer, and the human operator. recently there has been a trend towards the use of distributed real-time computer systems which potentially offer greater functional flexibility, better maintainability and better reliability than centralized systems, The increasing demands that are being placed on real-time computer control systems have highlighted the deficiencies of current heuristic design techniques and emphasised the need Ior solid theoretical design precepts. / AC2017

There are at present a large number of theoretical and algorithmic results relating to one-variable polynomial matrices arising from one-dimensional multivariable systems. In recent years many of the theoretical results have been extended to two-variable polynomial matrices arising from two-dimensional multi variable systems, such as delay-differential or partial differential systems. However there has been no major attempt to extend the algorithmic results associated with single variable polynomial matrices to two-variable or multivariable polynomial matrices. This thesis investigates further some of the extensions of the algebra of one-dimensional multivariable systems to two-dimensional multivariable systems.

In the mobile multipath environment the received signal can be adversely affected by unwanted, random envelope modulation. With AM-type systems these envelope variations are impressed directly on to the receiver's demodulated output. Automatic Gain Control (AGC) can be used to reduce the unwanted envelope fading without distorting the required modulation. This thesis begins by analysing mobile radio propagation in some detail and develops worst-case deterministic test signals to study the performance of AGC. The dynamic performance of a variety of AGC systems is then described along with their application to mobile radio receivers. Initially, conventional feedback AGC systems are discussed. These are shown to be capable of suppressing deep, unwanted fading only if it occurs at rates well below that of the wanted envelope modulation. The effects of time delay and the use of coherent envelope detectors are analysed and shown to worsen the dynamic performance of feedback AGC. Feedforward AGC systems, which can possess greatly improved dynamics, are then introduced. A form of feedforward AGC is developed which can achieve a specified dynamic performance with the narrowest of all control bandwidths. Finally, the effects of signal-carrier decorrelation, noise and interference are shown to limit the ultimate performance of all AGC systems.

This thesis concerns the development of a variable speed control system for a transverse-flux heteropolar linear synchronous motor and a control scheme to optimise the efficiency and minimise the interaction existing between the lift and tractive force produced by the motor. A data acquisition system was designed and built to manipulate and display data from transducers fitted to the motor on to a mini-computer. Control programs have been developed to acquire both electrical and mechanical transient conditions. A variable frequency 3 phase armature current controller and a field current controller have been developed for powering the motor. Both controllers employ power transistors as switching elements and utilise slitwidth modulation techniques. Practical aspects of designing a transistorised current controller are discussed. Strategies for controlling a synchronous motor are examined. A system for controlling the motor at a constant torque angle of 90° bas been developed to optimise the efficiency and minimise the interaction between the two forces. Experimental results suggest that the controlled motor cannot slip out of synchronism under any conditions and is self-starting. A 'software implemented* variable speed control loop is implemented on a microprocessor-based controller. Certain aspects of designing this controller are presented. Practical results show that the motor achieves a smooth drive even at low speed. Further enhancement of a constajit speed drive independent of the influence of the change in field current within a designed range is demonstrated by experimental results. Although the system is primarily designed for the linear synchronous motor, it is also applicable to other synchronous motors. A proposed position control system utilising a linear synchronous motor is also introduced.

by Wong Yiu Kwong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1980. / Bibliography: leaves 82-83.

by Ka-lun Tung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1995. / Includes bibliographical references (leaves 168-[175]). / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Control with Uncertainties --- p.1 / Chapter 1.1.1 --- Adaptive Control --- p.2 / Chapter 1.1.2 --- H∞ Robust Control --- p.3 / Chapter 1.2 --- A Unified Framework: Adaptive Robust Control --- p.4 / Chapter 1.3 --- System Identification for Robust Control --- p.6 / Chapter 1.3.1 --- Choice of input signal --- p.7 / Chapter 1.4 --- Objectives and Contributions --- p.8 / Chapter 1.5 --- Thesis Outline --- p.9 / Chapter 2 --- Background on Robust Control --- p.11 / Chapter 2.1 --- Notation and Terminology --- p.12 / Chapter 2.1.1 --- Notation --- p.12 / Chapter 2.1.2 --- Linear System Terminology --- p.13 / Chapter 2.1.3 --- Norms --- p.15 / Chapter 2.1.4 --- More Terminology: A Standard Feedback Configuration --- p.17 / Chapter 2.2 --- Norms and Power for Signals and Systems --- p.18 / Chapter 2.3 --- Plant Uncertainty Model --- p.20 / Chapter 2.3.1 --- Multiplicative Unstructured Uncertainty --- p.21 / Chapter 2.3.2 --- Additive Unstructured Uncertainty --- p.22 / Chapter 2.3.3 --- Structured Uncertainty --- p.23 / Chapter 2.4 --- Motivation for H∞ Control Design --- p.23 / Chapter 2.4.1 --- Robust stabilization: Multiplicative Uncertainty and Weight- ing function W3 --- p.24 / Chapter 2.4.2 --- Robust stabilization: Additive Uncertainty and Weighting function W2 --- p.25 / Chapter 2.4.3 --- Tracking Problem --- p.26 / Chapter 2.4.4 --- Disturbance Rejection (or Sensitivity Minimization) --- p.27 / Chapter 2.5 --- The Robust Control Problem Statement --- p.28 / Chapter 2.5.1 --- The Mixed-Sensitivity Approach --- p.29 / Chapter 2.6 --- An Augmented Generalized Plant --- p.30 / Chapter 2.6.1 --- The Augmented Plant --- p.30 / Chapter 2.6.2 --- Adaptation of Augmented Plant to Sensitivity Minimiza- tion Problem --- p.32 / Chapter 2.6.3 --- Adaptation of Augmented Plant to Mixed-Sensitivity Prob- lem --- p.33 / Chapter 2.7 --- Using MATLAB Robust Control Toolbox --- p.34 / Chapter 3 --- Statistical Plant Set Estimation for Robust Control --- p.36 / Chapter 3.1 --- An Overview --- p.37 / Chapter 3.2 --- The Schroeder-phased Input Design --- p.39 / Chapter 3.3 --- The Statistical Additive Uncertainty Bounds --- p.40 / Chapter 3.4 --- Additive Uncertainty Characterization --- p.45 / Chapter 3.4.1 --- "Linear Programming Spectral Overbounding and Factor- ization Algorithm (LPSOF) [20,21]" --- p.45 / Chapter 4 --- Basic System Identification and Model Reduction Algorithms --- p.48 / Chapter 4.1 --- The Eigensystem Realization Algorithm --- p.49 / Chapter 4.1.1 --- Basic Algorithm --- p.49 / Chapter 4.1.2 --- Estimating Markov Parameters from Input/Output data: Observer/Kalman Filter Identification (OKID) --- p.51 / Chapter 4.2 --- The Frequency-Domain Identification via 2-norm Minimization --- p.54 / Chapter 4.3 --- Balanced Realization and Truncation --- p.55 / Chapter 4.4 --- Frequency Weighted Balanced Truncation --- p.56 / Chapter 5 --- Plant Model Reduction and Robust Control Design --- p.59 / Chapter 5.1 --- Problem Formulation --- p.59 / Chapter 5.2 --- Iterative Reweighting Scheme --- p.60 / Chapter 5.2.1 --- Rationale Behind the Scheme --- p.62 / Chapter 5.3 --- Integrated Model Reduction/ Robust Control Design with Iter- ated Reweighting --- p.63 / Chapter 5.4 --- A Design Example --- p.64 / Chapter 5.4.1 --- The Plant and Specification --- p.64 / Chapter 5.4.2 --- First Iteration --- p.65 / Chapter 5.4.3 --- Second Iteration --- p.67 / Chapter 5.5 --- Approximate Fractional Frequency Weighting --- p.69 / Chapter 5.5.1 --- Summary of Past Results --- p.69 / Chapter 5.5.2 --- Approximate Fractional Frequency Weighting Approach [40] --- p.70 / Chapter 5.5.3 --- Simulation Results --- p.71 / Chapter 5.6 --- Integrated System Identification/Control Design with Iterative Reweighting Scheme --- p.74 / Chapter 6 --- Controller Reduction and Robust Control Design --- p.82 / Chapter 6.1 --- Motivation for Controller Reduction --- p.83 / Chapter 6.2 --- Choice of Frequency Weightings for Controller Reduction --- p.84 / Chapter 6.2.1 --- Stability Margin Considerations --- p.84 / Chapter 6.2.2 --- Closed-Loop Transfer Function Considerations --- p.85 / Chapter 6.2.3 --- A New Way to Determine Frequency Weighting --- p.86 / Chapter 6.3 --- A Scheme for Iterative Frequency Weighted Controller Reduction (IFWCR) --- p.87 / Chapter 7 --- A Comparative Design Example --- p.90 / Chapter 7.1 --- Plant Model Reduction Approach --- p.90 / Chapter 7.2 --- Weighted Controller Reduction Approach --- p.94 / Chapter 7.2.1 --- A Full Order Controller --- p.94 / Chapter 7.2.2 --- Weighted Controller Reduction with Stability Considera- tions --- p.94 / Chapter 7.2.3 --- Iterative Weighted Controller Reduction --- p.96 / Chapter 7.3 --- Summary of Results --- p.101 / Chapter 7.4 --- Discussions of Results --- p.101 / Chapter 8 --- A Comparative Example on a Benchmark problem --- p.105 / Chapter 8.1 --- The Benchmark plant [54] --- p.106 / Chapter 8.1.1 --- Benchmark Format and Design Information --- p.106 / Chapter 8.1.2 --- Control Design Specifications --- p.107 / Chapter 8.2 --- Selection of Performance Weighting function --- p.108 / Chapter 8.2.1 --- Reciprocal Principle --- p.109 / Chapter 8.2.2 --- Selection of W1 --- p.110 / Chapter 8.2.3 --- Selection of W2 --- p.110 / Chapter 8.3 --- System Identification by ERA --- p.112 / Chapter 8.4 --- System Identification by Curve Fitting --- p.114 / Chapter 8.4.1 --- Spectral Estimate --- p.114 / Chapter 8.4.2 --- Curve Fitting Results --- p.114 / Chapter 8.5 --- Robust Control Design --- p.115 / Chapter 8.5.1 --- The selection of W1 weighting function --- p.115 / Chapter 8.5.2 --- Summary of Design Results --- p.116 / Chapter 8.6 --- Stress Level 1 --- p.117 / Chapter 8.6.1 --- System Identification Results --- p.117 / Chapter 8.6.2 --- Design Results --- p.119 / Chapter 8.6.3 --- Step Response --- p.121 / Chapter 8.7 --- Stress Level 2 --- p.124 / Chapter 8.7.1 --- System Identification Results --- p.124 / Chapter 8.7.2 --- Step Response --- p.125 / Chapter 8.8 --- Stress Level 3 --- p.128 / Chapter 8.8.1 --- System Identification Results --- p.128 / Chapter 8.8.2 --- Step Response --- p.129 / Chapter 8.9 --- Comparisons with Other Designs --- p.132 / Chapter 9 --- Conclusions and Recommendations for Further Research --- p.133 / Chapter 9.1 --- Conclusions --- p.133 / Chapter 9.2 --- Recommendations for Further Research --- p.135 / Chapter A --- Design Results of Stress Levels 2 and3 --- p.137 / Chapter A.1 --- Stress Level 2 --- p.137 / Chapter A.2 --- Stress Level 3 --- p.140 / Chapter B --- Step Responses with Reduced Order Controller --- p.142 / Chapter C --- Summary of Results of Other Groups on the Benchmark Prob- lem --- p.145 / Chapter C.1 --- Indirect and implicit adaptive predictive control [45] --- p.146 / Chapter C.2 --- H∞ Robust Control [51] --- p.150 / Chapter C.3 --- Robust Stability Degree Assignment [53] --- p.152 / Chapter C.4 --- Model Reference Adaptive Control [46] --- p.154 / Chapter C.5 --- Robust Pole Placement using ACSYDE (Automatic Control Sys- tem Design) [47] --- p.156 / Chapter C.6 --- Adaptive PI Control [48] --- p.157 / Chapter C.7 --- Adaptive Control with supervision [49] --- p.160 / Chapter C.8 --- Partial State Model Reference (PSRM) Control [50] --- p.162 / Chapter C.9 --- Contstrainted Receding Horizon Predictive Control (CRHPC) [52] --- p.165 / Bibliography --- p.168

Tai Chi-hung. / Thesis (M.Phil.)--Chinese University of Hong Kong. / Bibliography: leaves 40-41.

Chen Zhiyong. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2002. / Includes bibliographical references (leaves 54-57). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Nonlinear Control --- p.1 / Chapter 1.2 --- Global Stabilization --- p.2 / Chapter 1.3 --- Output Regulation --- p.3 / Chapter 1.4 --- Contributions of the Thesis --- p.4 / Chapter 2 --- Global Robust Stabilization of Cascaded Polynomial Systems --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- Preliminaries --- p.6 / Chapter 2.3 --- Basic Results --- p.8 / Chapter 2.4 --- The Algorithm --- p.11 / Chapter 2.5 --- An Example --- p.14 / Chapter 2.6 --- Concluding Remarks --- p.16 / Chapter 3 --- Output Regulation of Singular Nonlinear Systems by Normal Output Feedback --- p.18 / Chapter 3.1 --- Introduction --- p.18 / Chapter 3.2 --- Preliminaries --- p.20 / Chapter 3.3 --- Main Result --- p.24 / Chapter 3.4 --- An Example --- p.34 / Chapter 3.5 --- Concluding Remarks --- p.35 / Chapter 4 --- Robust Output Regulation of Singular Nonlinear Systems --- p.37 / Chapter 4.1 --- Introduction --- p.37 / Chapter 4.2 --- Problem Description and Standard Assumptions --- p.38 / Chapter 4.3 --- A Preliminary Result --- p.40 / Chapter 4.4 --- Solvability of the Problem --- p.48 / Chapter 4.5 --- Concluding Remarks --- p.51 / Chapter 5 --- Conclusions --- p.52 / Bibliography --- p.54 / Biography --- p.58

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