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Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: The Coal to Liquid facility, Sasol, Secunda operates as a train of processes. Disturbances and capacity restrictions can occur throughout the plant and the throughput fluctuates whenever disturbances occur. When capacity restrictions occur in a subplant and more substances enter the sub-plant than can be processed, the extra substances are flared or dumped and therefore lost. To reduce losses and extra costs and to maximise the throughput of the whole plant, supervisory control is implemented over the whole plant system. Each process in the process train is controlled with regulatory controllers and the overall process is then controlled with a supervisory controller. These two sets of controllers operate in two different layers of control, with the regulatory controllers the faster inner layer. The supervisory control is the outer layer of the two control layers. The supervisory controller takes over the work of the human operator by deciding on the changes in total throughput as well as the set points for each individual process. These set points for each process are then followed with the regulatory controllers. For the regulatory control of the system, different control methods are investigated and compared. The different control methods that are looked at are PI control, Linearised State Feedback control, Fuzzy Logic control and Model Reference Adaptive Control. After an investigation into the various control methods Fuzzy Logic control was chosen for the regulatory as well as the supervisory control levels. Fuzzy Logic control is a rule based control method. Fuzzy variables are everyday terms such as very slow or nearly full. These terms are easy to understand by the operator and multi-variable control is possible with Fuzzy Logic control without an accurate mathematical representation of the system. These facts made Fuzzy Logic control ideal for this implementation. To improve the profit of the Coal to Liquid facility the throughput was maximised. The combination of regulatory and supervisory controllers minimised losses and rejected disturbances. This resulted in a smoother output with maximum profit. / AFRIKAANSE OPSOMMING: Die Steenkool-na-Olie fasiliteit, Sasol, Secunda funksioneer as ’n trein van prosesse. Versteurings en kapasiteit beperkings kan deur die hele aanleg voorkom en die deurset wissel voortdurend wanneer versteurings voorkom. Wanneer kapasiteit beperkings voorkom in ’n aanleg en meer stowwe word in die aanleg ingestuur as wat dit kan verwerk, word die ekstra stowwe gestort en dit gaan verlore. Om verliese en kostes te verminder en om die deurset van die hele aanleg te vergroot, is oorhoofse beheer geïmplementeer oor die hele stelsel. Elke proses in die trein van chemiese prosesse word beheer met regulerende beheerders. Die totale proses word dan beheer met ’n oorhoofse beheerder. Hierdie twee tipes beheerders funksioneer in twee lae van beheer met die regulerende beheerders die vinniger binneste laag. Die oorhoofse beheerder vorm die buitenste laag van die twee beheer lae en neem die werk van die menslike operateur oor deur die veranderinge in die totale deurset, sowel as die stelpunte vir elke afsonderlike proses, te bepaal. Hierdie stelpunte vir elke proses word dan met die regulerende beheerders gevolg. Verskillende beheer metodes is ondersoek vir die regulerende beheer van die stelsel. Die verskillende beheer metodes waarna gekyk word, is PI beheer, Geliniariseerde Toestands Terugvoer beheer, Wasige Logiese beheer en Model Verwysing Aanpassende beheer. Na ’n ondersoek na die verskillende beheer metodes is Wasige Logiese beheer gekies vir die regulerende asook die oorhoofse beheer. Wasige Logiese beheer is ’n reël gebasseerde beheer metode. Wasige Logika veranderlikes is alledaagse terme soos baie stadig of byna vol. Hierdie terme is maklik om te verstaan deur die operateur. Meervoudige-veranderlike beheer is moontlik met Wasige Logiese beheer sonder ’n akkurate wiskundige voorstelling van die stelsel. Hierdie feite maak Wasige Logiese beheer ideaal vir hierdie doel. Om die wins van die Steenkool-na-Olie fasiliteit te verbeter, is die deurset gemaksimeer. Die kombinasie van regulerende- en toesighoudende beheerders beperk verliese en verwerp versteurings. Dit lei tot ’n gladder uitset en ’n maksimum wins.

The testing of any physiological diagnostic system in-vivo depends critically on the stability of the anesthetized animal used. That is, if the systemic physiological parameters are not tightly controlled, it is exceedingly difficult to assess the precision and accuracy of the system or interpret the consequence of disease. In order to ensure that all measurements taken using the experimental system are not affected by fluctuations in physiological state, the animal must be maintained in a tightly controlled physiologic range. The main goal of this project was to develop a robust monitoring and control system capable of maintaining the physiological parameters of the anesthetized animal in a predetermined range, using the instrumentation already present in the laboratory, and based on the LabVIEWR software interface. A single user interface was developed that allowed for monitoring and control of key physiological parameters including body temperature (BT), mean arterial blood pressure (MAP) and end tidal CO2 (ETCO2). Embedded within this interface was a fuzzy logic based control system designed to mimic the decision making of an anesthetist. The system was tested by manipulating the blood pressure of a group of anesthetized animal subjects using bolus injections of epinephrine and continuous infusions of phenylephrine (a vasoconstrictor) and sodium nitroprusside (a vasodilator). This testing showed that the system was able to significantly reduce the deviation from the set pressure (as measured by the root mean square value) while under control in the hypotension condition (p < 0.10). Though both the short-term and hypertension testing showed no significant improvement, the control system did successfully manipulate the anesthetic percentage in response to changes in MAP. Though currently limited by the control variables being used, this system is an important first step towards a fully automated monitoring and control system and can be used as the basis for further research.

In recent years, teleoperation has shown great potentials in different fields such as spatial, mining, under-water, etc. When teleoperation is required to be bilateral, the time delay induced by a potentially large physical distance prevents a good performance of the controller, especially in case of contact. When bilateral teleoperation is introduced to the field of medicine, a new challenge arises: the controller needs to be used in both hard and soft environments. For example, in the context of telesurgery, the robot can enter in contact with both bone (hard) and organ (soft). In an attempt to enrich existing controller designs to better suit the medical needs, an adaptive fuzzy logic controller is designed in this text. It simulates human intelligence and adapts the controller to environments of different stiffness coefficients. It is compared to three other classical controllers used in the field of bilateral teleopeartion and demonstrates very interesting potential.

In recent years, teleoperation has shown great potentials in different fields such as spatial, mining, under-water, etc. When teleoperation is required to be bilateral, the time delay induced by a potentially large physical distance prevents a good performance of the controller, especially in case of contact. When bilateral teleoperation is introduced to the field of medicine, a new challenge arises: the controller needs to be used in both hard and soft environments. For example, in the context of telesurgery, the robot can enter in contact with both bone (hard) and organ (soft). In an attempt to enrich existing controller designs to better suit the medical needs, an adaptive fuzzy logic controller is designed in this text. It simulates human intelligence and adapts the controller to environments of different stiffness coefficients. It is compared to three other classical controllers used in the field of bilateral teleopeartion and demonstrates very interesting potential.

Factors of very rapidly rising oil prices, the running out time limits on continued use of fossil fuels, as well as elements of the Kyoto Protocol, have greatly arouses the increasing emphasis on natural and renewable energy sources. 40 minutes of total solar radiation on earth could provide enough power to meet the energy needs of all human beings for approximately one year. The potential of solar energy is virtually limitedless. Moreover, by means of solar powered batteries, solar energy can be directly converted to electric power. Since it neither pollutes the environment or ecology, solar is an extremely clean source of energy. The life-span of solar cell is very long, possibly 20 years or more. The capability of solar batteries to provide energy is approximately proportional to the intensity of the sunlight. This thesis proposes the use of Artificial intelligence for "Sun Track Solar Energy System". This system employs Fuzzy Logic Control Theory, combined with Grey Relational Analysis, for tracking the angle of the sun, and further control the motor to adjust the angle for tracking, so direct sunlight could be acquired to increase power output. As a result of the experiment, comparing the electricity generated from the fix angle solar battery with the AI-based Sun Track Solar Energy System, the latter one has an efficiency increase up to 23% for the same amount of sunlight.

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In recent years, teleoperation has shown great potentials in different fields such as spatial, mining, under-water, etc. When teleoperation is required to be bilateral, the time delay induced by a potentially large physical distance prevents a good performance of the controller, especially in case of contact. When bilateral teleoperation is introduced to the field of medicine, a new challenge arises: the controller needs to be used in both hard and soft environments. For example, in the context of telesurgery, the robot can enter in contact with both bone (hard) and organ (soft). In an attempt to enrich existing controller designs to better suit the medical needs, an adaptive fuzzy logic controller is designed in this text. It simulates human intelligence and adapts the controller to environments of different stiffness coefficients. It is compared to three other classical controllers used in the field of bilateral teleopeartion and demonstrates very interesting potential.

In recent years, teleoperation has shown great potentials in different fields such as spatial, mining, under-water, etc. When teleoperation is required to be bilateral, the time delay induced by a potentially large physical distance prevents a good performance of the controller, especially in case of contact. When bilateral teleoperation is introduced to the field of medicine, a new challenge arises: the controller needs to be used in both hard and soft environments. For example, in the context of telesurgery, the robot can enter in contact with both bone (hard) and organ (soft). In an attempt to enrich existing controller designs to better suit the medical needs, an adaptive fuzzy logic controller is designed in this text. It simulates human intelligence and adapts the controller to environments of different stiffness coefficients. It is compared to three other classical controllers used in the field of bilateral teleopeartion and demonstrates very interesting potential.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.