This thesis is to develop a novel consensus algorithm or protocol for multi-agent systems in the event of communication link failure over the network. The structure or topology of the system is modeled by an algebraic graph theory, and defined as a discrete time-invariant system with a second-order dynamics. The communication link failure is governed by a Bernoulli process. Lyapunov-based methodologies and Linear Matrix Inequality (LMI) techniques are then applied to find an appropriate controller gain by satisfying the sufficient conditions of the error dynamics. Therefore, the controller with the calculated gain is guaranteed to drive the system to reach a consensus. Finally, simulation and experiment studies are carried out by using two Mobile Robot Pioneer 3-DXs and one Pioneer 3-AT as a team to verify the proposed work.

One of the protocols used for communication between coaches inside a train is the National Train Bus (NTB). The result of this work is support for diagnostics on the NTB - mainly watching the process data-flow and monitoring of chosen variables distributed along the train, with consideration to their character and validity.

A dissertation submitted to the Faculty of Engineenug, University of the Witwatersrand, in fulfilment. of the requirements for the degree of Master of Science in Engineering, / A multi variable control strategy for a grinding circuit at East Driefontein has been implemented by others and has enabled it to operate under stable conditions. The next development needed was to find conditions under which tne efficiency of the circuit was the greatest. Optimum set points exist for the multi variable controller to keep the circuit at its most efficient state. This project was done to determine these set points, and how it affected the operation of the circuit. The strategy involved the collection of process data, the development of mathematical models and the determination of these optimum set points by simulation, This option reduced interference with routine production operations, which is often a prohibiting factor during any development work on an existing process. The optimum set points were successfully determined, and were compared with previous findings and current plant practice. / AC2017

No description available.

No description available.

Abstract The amount of digital data surrounding us has exploded within the past years. In industry, data are gathered from different production phases with the intent to use the data to improve the overall manufacturing process. However, management and utilization of these huge data sets is not straightforward. Thus, a computer-driven approach called data mining has become an attractive research area. Using data mining methods, new and useful information can be extracted from enormous data sets. In this thesis, diverse industrial problems are approached using data mining methods based on similarity. Similarity information is shown to give an additional advantage in different phases of manufacturing. Similarity information is utilized with smaller-scale problems, but also in a broader perspective when aiming to improve the whole manufacturing process. Different ways of utilizing similarity are also introduced. Methods are chosen to emphasize the similarity aspect; some of the methods rely entirely on similarity information, while other methods just preserve similarity information as a result. The actual problems covered in this thesis are from quality control, process monitoring, improvement of manufacturing efficiency and model maintenance. They are real-world problems from two different application areas: spot welding and steel manufacturing. Thus, this thesis clearly shows how the industry can benefit from the presented data mining methods.

This paper explores the potential of process data distribution in real time using the RTP protocol. Firstly, there is an evaluation of existing protocols that are used for this purpose. Secondly, there is a description of the trio of protocols RTP, RTCP, and RTSP, explained their functions and explored possibilities for their expansion. On the basis of this acquired knowledge, problems which may occur with the distribution of process data by means of RTP are identified and their possible solutions are proposed. To demonstrate the data distribution via RTP, the application, that collects GPS position from the PDA and then provides clients with them, has been designed and implemented. Clients can display these data on the map.

Beim Siegeln von Verpackungen können nicht-qualitätsgerechte Siegelnähte dadurch entstehen, dass sich Teile des Packguts zwischen den zu fügenden Packmittelbahnen befinden. Im Gegensatz zum konventionellen Wärmekontaktsiegeln besteht beim Ultraschallsiegeln die Möglichkeit, für jeden einzelnen Siegelvorgang Prozessdaten zu erfassen. Aus dem Bereich des Ultraschallschweißens ist bekannt, dass sich anhand dieser Daten der Prozess überwachen und dessen Güte bewerten lässt. Die vorliegende Arbeit untersucht den Informationsgehalt dieser Prozessdaten hinsichtlich störungsbehafteter Siegelnähte und leistet damit einen Beitrag zur Verbesserung der Verpackungsqualität und damit im besten Fall auch zu weniger Lebensmittelverlusten. Es wird dazu zunächst eine Systematisierung von möglichen Störprinzipen beim Siegeln hinsichtlich ihrer physikalischen Wirkprinzipe vorgenommen. Darauf aufbauend konnte ein Versuchsstand und eine Methodik zur Untersuchung der Thematik entwickelt werden. Abschließend wurden ausgewählte Störprinzipe untersucht, wobei sich ein sehr starker Packstoff- und Einfluss der Prozessführung herausstellte. Es konnte ein prinzipieller Zusammenhang zwischen Auswirkung einer Störung auf die Nahtqualität und die Prozessdaten gezeigt werden, welcher sich jedoch auf Basis der erarbeiteten Ergebnisse nicht verallgemeinern lässt.

Thesis. 1975. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / Vita. / Includes bibliographical references. / Ph.D.

Abstract This thesis presents methods for modelling conditional variance and uncertainty of prediction at a query point on the basis of industrial process data. The introductory part of the thesis provides an extensive background of the examined methods and a summary of the results. The results are presented in detail in the original papers. The application presented in the thesis is modelling of the mean and variance of the mechanical properties of steel plates. Both the mean and variance of the mechanical properties depend on many process variables. A method for predicting the probability of rejection in a quali?cation test is presented and implemented in a tool developed for the planning of strength margins. The developed tool has been successfully utilised in the planning of mechanical properties in a steel plate mill. The methods for modelling the dependence of conditional variance on input variables are reviewed and their suitability for large industrial data sets are examined. In a comparative study, neural network modelling of the mean and dispersion narrowly performed the best. A method is presented for evaluating the uncertainty of regression-type prediction at a query point on the basis of predicted conditional variance, model variance and the effect of uncertainty about explanatory variables at early process stages. A method for measuring the uncertainty of prediction on the basis of the density of the data around the query point is proposed. The proposed distance measure is utilised in comparing the generalisation ability of models. The generalisation properties of the most important regression learning methods are studied and the results indicate that local methods and quadratic regression have a poor interpolation capability compared with multi-layer perceptron and Gaussian kernel support vector regression. The possibility of adaptively modelling a time-varying conditional variance function is disclosed. Two methods for adaptive modelling of the variance function are proposed. The background of the developed adaptive variance modelling methods is presented.