Šiame darbe išnagrinėtas tiesinių sistemų parametrų įvertinimo metodas. Visas darbas susideda iš dviejų dalių: teorinės ir praktinės. Teorinėje dalyje aprašytas modelis, modelio kūrimas bei jo struktūra. Pateiktas Markovo įvertinimas arba įvertinimas apibendrintuoju mažiausiųjų kvadratų metodu, taip pat apibendrintojo modelio aprašymas, jo naudojimas. Praktinėje dalyje atlikti ir aprašyti eksperimentai. Modeliavimo eksperimentai atlikti naudojant programinį paketą MATLAB. Skaičiavimams atlikti buvo sukurtos ir panaudotos funkcijos matrica, period. Eksperimentų rezultatai pateikti lentelėse ir grafikuose. Padarytos išvados apie blokinio parametrų įvertinimo metodo efektyvumą. / In this work a block parameter estimation method for linear time-invariant systems is discussed. The whole work consists of two parts: theoretical and practical. The theoretical part is based on the description of the model, its creation and structure. Furthermore, Markov estimation or an estimation of the least squares generalized method and the description of the experiments and their description. The experiments of modeling have been performed using MATLAB program. In addition, the functions matrica, period were created and used to do the estimations. The results of the experiments are illustrated in charts and diagrams. Finally, the conclusions about the efficiency of the block parameter estimation method are done.

This thesis presents rules that characterize the root locus for polynomials that are nonlinear in the root-locus parameter k. Classical root locus applies to polynomials that are affine in k. In contrast, this thesis considers polynomials that are quadratic or cubic in k. In particular, we focus on constructing the root locus for linear feedback control systems, where the closed-loop denominator polynomial is quadratic or cubic in k. First, we present quadratic root-locus rules for a controller class that yields a closed-loop denominator polynomial that is quadratic in k. Next, we develop cubic root-locus rules for a controller class that yields a closed-loop denominator polynomial that is cubic in k. Finally, we extend the quadratic root-locus rules to accommodate a larger class of controllers. We also provide controller design examples to demonstrate the quadratic and cubic root locus. For example, we show that the triple integrator can be high-gain stabilized using a controller that yields a closed-loop denominator polynomial that is quadratic in k. Similarly, we show that the quadruple integrator can be high-gain stabilized using a controller that yields a closed-loop denominator polynomial that is cubic in k.

Well-designed demand response is expected to play a vital role in operatingpower systems by reducing economic and environmental costs. However,the current system is operated without much information on the benefits ofend-users, especially the small ones, who use electricity. This thesis proposes aframework of operating power systems with demand models including the diversityof end-users’ benefits, namely adaptive load management (ALM). Sincethere are a large number of end-users having different preferences and conditionsin energy consumption, the information on the end-users’ benefits needsto be aggregated at the system level. This leads us to model the system ina multi-layered way, including end-users, load serving entities, and a systemoperator. On the other hand, the information of the end-users’ benefits can beuncertain even to the end-users themselves ahead of time. This information isdiscovered incrementally as the actual consumption approaches and occurs. Forthis reason ALM requires a multi-temporal model of a system operation andend-users’ benefits within. Due to the different levels of uncertainty along thedecision-making time horizons, the risks from the uncertainty of informationon both the system and the end-users need to be managed. The methodologyof ALM is based on Lagrange dual decomposition that utilizes interactive communicationbetween the system, load serving entities, and end-users. We showthat under certain conditions, a power system with a large number of end-userscan balance at its optimum efficiently over the horizon of a day ahead of operationto near real time. Numerical examples include designing ALM for theright types of loads over different time horizons, and balancing a system with a large number of different loads on a congested network. We conclude thatwith the right information exchange by each entity in the system over differenttime horizons, a power system can reach its optimum including a variety ofend-users’ preferences and their values of consuming electricity.

The subject of this thesis is the design and practical application of a model-based controller with variable structure control (VSC). Robot manipulators are highly non-linear systems, however they form a specific class in the non-linear group. Exact mathematical descriptions of the robot dynamics can be achieved and further, robot manipulators have specific useful properties that can be used for the design of advanced controllers. The inclusion of the inverse dynamic description of the robot manipulator as a feedforward term of the controller (model-based controller) is used to transform two non-linear systems i.e. the controller and the robot, into one linear system. The limitation of this technique arises from the accuracy of the inverse dynamic model. The linearisation only takes place if the model is known exactly. To deal with the uncertainties that arise in the model, a control methodology based on variable structure control is proposed. The design of the controller is based on a Lyapunov approach and engineering considerations of the robot. A candidate Lyapunov function of a pseudo-energy form is selected to start the controller design. The general form of the controller is selected to satisfy the negative definiteness of the Lyapunov function. The initial uncertainties between the actual robot dynamics and the model used in the controller are dealt with using a classical VSC regulator. The deficiencies of this approach are evident however because of the chattering phenomenum. The model uncertainties are examined from an engineering point of view and adjustable bounds are then devised for the VSC regulator, and simulations confirm a reduction in the chattering. Implementation on the SPRINTA robot reveals further limitations in the proposed methodology and the bound adjustment is enhanced to take into account the position of the robot and the tracking errors. Two controllers based on the same principle are then obtained and their performances are compared to a PID controller, for three types of trajectory. Tests reveal the superiority of the devised control methodology over the classic PID controller. The devised controller demonstrates that the inclusion of the robot dynamics and properties in the controller design with adequate engineering considerations lead to improved robot responses.

In this thesis, we study fault tolerant control (FTC) for the decentralized robust servomechanism problem (DRSP) of a colocated large flexible space structure (LFSS) under sensor and actuator failures (SAF). The control objective is to devise a decentralized controller that maintains the stability of the LFSS, tracks a constant reference for healthy outputs, regulates against an unknown constant disturbance for healthy outputs, and is robust against parametric uncertainties, so that ``spillover effects'' do not occur. Two FTC frameworks are considered: An active FTC framework that assumes SAF are known, and a passive FTC framework for which SAF are unknown. The active FTC framework extends existing work on DRSP of a nominal LFSS, and applies a PID controller that has fault-dependent adjustments. Necessary and sufficient conditions for a solution to exist are determined, notably an easy-to-test rank condition. For the passive FTC framework, a PD controller that stabilizes an LFSS under unknown SAF is found. Although perfect tracking and regulation are not attained under the PD controller, by applying high gains, the errors for healthy outputs can be reduced to any desired level. However, outputs with failed sensors and healthy actuators can reach undesirably high magnitude under high gains. To improve performance under low gains, insights on steady-state outputs are applied to develop a feed-forward control that has good performance in tracking, but not regulation. Further analysis on the PD controller reveals a method to diagnose SAF using steady-state outputs. As a result, the PD controller and PID controller are found to have complementary advantages, leading to an 3-stage integrated FTC procedure. First, the PD controller can stabilize the LFSS under unknown SAF (passive FTC). Next, fault diagnosis is performed while the LFSS is stabilized. Finally, a reconfigured PID controller applying diagnosed SAF enables healthy outputs to meet control objectives (active FTC). Three examples, including a benchmark space platform with 200 states obtained by finite-element analysis, are used to illustrate the results throughout this thesis.

In this thesis, we study fault tolerant control (FTC) for the decentralized robust servomechanism problem (DRSP) of a colocated large flexible space structure (LFSS) under sensor and actuator failures (SAF). The control objective is to devise a decentralized controller that maintains the stability of the LFSS, tracks a constant reference for healthy outputs, regulates against an unknown constant disturbance for healthy outputs, and is robust against parametric uncertainties, so that ``spillover effects'' do not occur. Two FTC frameworks are considered: An active FTC framework that assumes SAF are known, and a passive FTC framework for which SAF are unknown. The active FTC framework extends existing work on DRSP of a nominal LFSS, and applies a PID controller that has fault-dependent adjustments. Necessary and sufficient conditions for a solution to exist are determined, notably an easy-to-test rank condition. For the passive FTC framework, a PD controller that stabilizes an LFSS under unknown SAF is found. Although perfect tracking and regulation are not attained under the PD controller, by applying high gains, the errors for healthy outputs can be reduced to any desired level. However, outputs with failed sensors and healthy actuators can reach undesirably high magnitude under high gains. To improve performance under low gains, insights on steady-state outputs are applied to develop a feed-forward control that has good performance in tracking, but not regulation. Further analysis on the PD controller reveals a method to diagnose SAF using steady-state outputs. As a result, the PD controller and PID controller are found to have complementary advantages, leading to an 3-stage integrated FTC procedure. First, the PD controller can stabilize the LFSS under unknown SAF (passive FTC). Next, fault diagnosis is performed while the LFSS is stabilized. Finally, a reconfigured PID controller applying diagnosed SAF enables healthy outputs to meet control objectives (active FTC). Three examples, including a benchmark space platform with 200 states obtained by finite-element analysis, are used to illustrate the results throughout this thesis.

Inference of regulatory networks has received attention of researchers from many fields. The challenge offered by this problem is its being a typical modeling problem under insufficient information about the process. Hence, we need to derive the apriori unavailable information from the empirical observations. Modeling by inference consists of selecting or defining the most appropriate model structure and inferring the parameters. An appropriate model structure should have the following properties. The model parameters should be inferable. Given the observation and the model class, all parameters used in the model should have a unique solution restriction of the solution space). The forward model should be accurately computable (restriction of the solution space). The model should be capable of exhibiting the essential qualitative features of the system (limit of the restriction). The model should be relevant with the process (limit of the restriction). A piecewise linear formulation, described by a switching state transition matrix and a switching state transition vector with a Boolean function indicating the switching conditions is proposed for the inference of gene regulatory networks. This thesis mainly concerns using a formulation of switching networks obeying all the above mentioned requirements and developing an inference algorithm for estimating the parameters of the formulation. The methodologies used or developed during this study are applicable to various fields of science and engineering.

Thesis (Ph. D.)--University of Washington, 2007. / Vita. Includes bibliographical references (p. 127-131).

Thesis (Ph.D.)--University of Western Australia, 2007.

Thesis (M.E.)--University of Canterbury, 2008. / Typescript (photocopy). "August 2008." Includes bibliographical references (p. [123]-126). Also available via the World Wide Web.