The method of using Fuzzy Sets Theory(FST) and Fuzzy Reasoning(FR) to aid reliability evaluation in a complex and uncertain environment is studied, with special reference to electrical power generating system reliability evaluation. Device(component) reliability prediction contributes significantly to a system's reliability through their ability to identify source and causes of unreliability. The main factors which affect reliability are identified in Reliability Prediction Process(RPP). However, the relation between reliability and each affecting factor is not a necessary and sufficient one. It is difficult to express this kind of relation precisely in terms of quantitative mathematics. It is acknowledged that human experts possesses some special characteristics that enable them to learn and reason in a vague and fuzzy environment based on their experience. Therefore, reliability prediction can be classified as a human engineer oriented decision process. A fuzzy knowledge based reliability prediction framework, in which speciality rather than generality is emphasised, is proposed in the first part of the thesis. For this purpose, various factors affected device reliability are investigated and the knowledge trees for predicting three reliability indices, i.e. failure rate, maintenance time and human error rate are presented. Human experts' empirical and heuristic knowledge are represented by fuzzy linguistic rules and fuzzy compositional rule of inference is employed as inference tool. Two approaches to system reliability evaluation are presented in the second part of this thesis. In first approach, fuzzy arithmetic are conducted as the foundation for system reliability evaluation under the fuzzy envimnment The objective is to extend the underlying fuzzy concept into strict mathematics framework in order to arrive at decision on system adequacy based on imprecise and qualitative information. To achieve this, various reliability indices are modelled as Trapezoidal Fuzzy Numbers(TFN) and are proceeded by extended fuzzy arithmetic operators. In second approach, the knowledge of system reliability evaluation are modelled in the form of fuzzy combination production rules and device combination sequence control algorithm. System reliability are evaluated by using fuzzy inference system. Comparison of two approaches are carried out through case studies. As an application, power generating system reliability adequacy is studied. Under the assumption that both unit reliability data and load data are subjectively estimated, these fuzzy data are modelled as triangular fuzzy numbers, fuzzy capacity outage model and fuzzy load model are developed by using fuzzy arithmetic operations. Power generating system adequacy is evaluated by convoluting fuzzy capacity outage model with fuzzy load model. A fuzzy risk index named "Possibility Of Load Loss" (POLL) is defined based on the concept of fuzzy containment The proposed new index is tested on IEEE Reliability Test System (RTS) and satisfactory results are obtained Finally, the implementation issues of Fuzzy Rule Based Expert System Shell (FRBESS) are reported. The application of ERBESS to device reliability prediction and system reliability evaluation is discussed.

A Lie group G is said to be uniformly finitely generated by one parameter subgroups exp (tX^1) , i = l,...,n , if there exists a positive integer k such that every element of G may be expressed as a product of at most k elements chosen alternatively from these one-parameter subgroups. In this text we construct sets of left invariant vector fields on S0(n) , in particular pairs {A,B} , whose one-parameter subgroups uniformly finitely generate S0(n) . As a consequence, we also partially solve the uniform controllability problem for a m class of systems x(t) = ( m Σ i u1 (t)X1)x(t) , x ϵ S0(n) (X1,i = l,...,m)L A = so(n) by putting an upper bound on the number of switches in the trajectories, in positive time, of X1...,X m that are required to join any two points of S0(n) . This result is also extended to any connected and paracompact 1/ C -manifold of dimension n using a result of N. Levitt and H. Sussmann. An upper bound is put on the minimum number of switches of trajectories, in positive time, required to join any two states on M by two vector fields on M. This bound depends only on the dimension of M.

Symplectic Geometry has proved a powerful method in extending the knowledge of the classical theory of Hamiltonian mechanics without external variables. In this thesis these methods are applied to a class of Hamiltonian systems with controls in order to answer fundamental questions arising from Systems Theory and Classical Mechanics. The theoretical aspects of this thesis deal with the extension of the Lie algebraic results of Engel and Lie on nilpotent and solvable Lie algebras, respectively, by the introduction of symplectic structures. It provides revealing results on the internal structure of symplectic vector spaces acted on by nilpotent or solvable Lie algebras. Then, using the methods of Kostant and Kirillov, these results are globalized to look at nilpotent transitive actions on simply connected symplectic manifolds and the consequent internal structures. This theory is then applied to realizations of finite Volterra series with the additional property that the realization is Hamiltonian. These realizations are known to have an underlying nilpotent structure. A canonical realization is found and then shown to be closely linked with the theory of interconnections. Finally, the concepts of complete integrability on free Hamiltonian systems is put into a feasible framework for Hamiltonian systems with controls which have an associated nilpotent Lie algebra. It is found that it is still possible to integrate these systems by quadratures but the structure is now much more complex.

Many security systems employing different methods have been proposed to protect buried oil pipelines transporting petroleum products from the well head via the refinery to: depots and other receiving stations. Currently there is a security gap in the monitoring of these buried pipelines in real time and in keeping them protected from third party interference. This thesis addresses the problem of monitoring these systems by developing an automated image analysis system with the aid of a low-cost multisensory Unmanned Aerial Vehicle (UAV) for monitoring of buried pipeline right-of-way (ROW). The method used in this research is based on the identification of threat objects of interest from the video frame sequences of the pipeline right-of-way acquired by the UAV. This is achieved by training the system to recognise objects of interest using trained correlation filters. To determine the geographical location of detected objects, the Video frame sequences captured by the UAV platform were ortho-rectified to form ortho-images which were then mosaicked to form a seamless Digital Surface Model (DSM) covering the test area using a photogrammetry model. The DSM formed from the mosaicking of ortho-images is then emerged with a digital globe for geo-referencing of detected objects. Experiments were carried out on a test field located in United Kingdom and Nigeria, where video and telemetry data were collected, then processed using the techniques created in this research. The results demonstrated that the developed correlation filter was able to detect objects of interest despite the distortions that come with the object image, due to the fact that the expected distortion was compensated for using the training images. When compared with the 6 control points in the digital globe the accuracy of the two-dimension DSM gave a misalignment error of between 2 and 3 metres.

This thesis investigates some problems on nonlinear system identification, parameter estimation, and signal processing. Random signal spectral analysis and system frequency response estimation are studied from incomplete time series. Both recursive and direct estimators are presented based on either an unbiased or minimum mean square error criterion. Nonlinear system identification and parameter estimation are studied. A quantisation technique is developed to give a clear geometrical interpretation for structure detection and parameter estimation. A new concept, state amplitude distance between current and previous operating states, is introduced, and results in a Variable Weighted Least Squares (VWLS) algorithm. A modified version makes on-line application possible. Jump resonance is predicted by the VWLS algorithm as one of the applications. Self-tuning controllers, including a nonlinear general predictive controller and a nonlinear deadbeat controller, are designed. A vector backward shift operator is defined to simplify the expression of the Hammerstein model, and is introduced to analyse the general feedback controller design problem for nonlinear plant described by the Hammerstein model. A fast root-solver developed facilitates nonlinear model treatment in on-line applications. Theoretical results are confirmed by simulation studies.

A strategy typically employed for mobile robot navigation in an unknown environment is to follow a nominal straight-line path to the goal point. During travelling on the nominal path, the robot uses distance information, e.g. derived from sonar sensors, and geometric information to determine the spatial relations between the robot and the environment. Navigation in an unknown environment is still a challenging issue especially in the presence of cluttered objects or obstructions. There are two possible ways to path planning in an unknown environment: the first is to map the environment and navigate based on the map; the second is to assign a nominal path, which the robot follows whilst at the same time it senses obstacles and reacts to achieve a collision free trajectory. In both cases the robot circumnavigates obstructions and generates a new path from the initial location to the goal point. Often the strategies used for navigation employ simple path planning techniques aided by specific methods to recognize objects and construct a structure for the environment. In Chronis’ PhD thesis is this area, a ring of low level sonar sensors is used to get spatial relations between a mobile robot and its environment. The eventual goal is to use spatial relations for navigation of the mobile robot in an unstructured, unknown environment. However, Chronis’ work does not construct any model of perceived structures in the environment and does not involve any tolerance to sensor failure. The approach described in this thesis improves this earlier work in precisely these two areas. The proposed approach uses low level sensors, such as sonar sensors, to achieve navigation in an unknown and cluttered environment. It integrates sonar sensors and geometric information to construct structures of the environment and consequently establish a system that navigates effectively and quickly through cluttered objects and obstructions. It is shown that this strategy achieves efficiency and effectiveness in mobile robot navigation. The approach is also shown to be robust and tolerant to sensor failures. The strategy is not dependent on the number or type of sensors on the robot and does not assume a particular type of robot; it can work with any sensory method that can provide an object representation in two dimensions.

This thesis describes the fuzzy logic based control system for an automated guided vehicle ( AGV ) designed to navigate from one position and orientation to another while avoiding obstacles. A vehicle with an onboard computer system and a beacon based location system has been used to provide experimental confirmation of the methods proposed during this research. A simulation package has been written and used to test control techniques designed for the vehicle. A series of navigation rules based upon the vehicle's current position relative to its goal produce a fuzzy fit vector, the entries in which represent the relative importance of sets defined over all the possible output steering angles. This fuzzy fit vector is operated on by a new technique called rule spreading which ensures that all possible outputs have some activation. An obstacle avoidance controller operates from information about obstacles near to the vehicle. A method has been devised for generating obstacle avoidance sets depending on the size, shape and steering mechanism of a vehicle to enable their definition to accurately reflect the geometry and dynamic performance of the vehicle. Using a set of inhibitive rules the obstacle avoidance system compiles a mask vector which indicates the potential for a collision if each one of the possible output sets is chosen. The fuzzy fit vector is multiplied with the mask vector to produce a combined fit vector representing the relative importance of the output sets considering the demands of both navigation and obstacle avoidance. This is operated on by a newly developed windowing technique which prevents any conflicts produced by this combination leading to an undesirable output. The final fit vector is then defuzzified to give a demand steering angle for the vehicle. A separate fuzzy controller produces a demand velocity. In tests carried out in simulation and on the research vehicle it has been shown that the control system provides a successful guidance and obstacle avoidance scheme for an automated vehicle.

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This project uses advanced modelling techniques to produce a design for a computer based advisory system for the operator of a critical, complex, non-linear system, typified by a nuclear reactor. When such systems are in fault the operator has to promptly assess the problem and commence remedial action. Additional accurate and rapid information to assist in this task would clearly be of benefit. The proposed advisory system consists of two main elements. The plant state is determined and then the future condition predicted. These two components are linked by a common data flow. The diagnosed condition is also used as input for the predictive section. Artificial Neural Networks (ANNs) are used to perform both diagnosis and predictions. An ANN, a simplified model of the brain, can be trained to classify a set of known inputs. It can then classify unknown inputs The predictive element is first investigated. The number of conditions that can be predicted by a single ANN is identified as a key factor. Two distinct solutions are considered. The first uses the important features of the fault to determine an empirical relationship for combining transients. The second uses ANNs to model a range of system transients. A simple model is developed and refined to represent an important section of a nuclear reactor. The results show good predicted values for a extensive range of fault scenarios. The second approachis selected for implementation in the advisory system. The diagnostic element is explored using a set of key transients. A series of ANNs for diagnosing these conditions are developed using a range of strategies. The optimum combination was selected for implementation in the advisory system. The key plant variables which contributed most to the ANN inputs were identified. An implementation of the advisory system is described. The system should be a single suite of programs with the predictive and diagnostic sections supported by a controller module for organising information. The project concludes that the construction of such a system is possible with the latest technologies.