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.

Finding a product with high quality and reasonable price online is a difficult task due to the fuzzy nature of data and queries. In order to handle the fuzzy problem, a new type-2 fuzzy reasoning based decision support system, the Web Shopping Expert for online users is proposed. In the Web Shopping Expert, an interval type-2 fuzzy logic system is used and a fuzzy output can be obtained using the up-low limit technique, which offers an opportunity to directly employ all the rules and methods of the type-1 fuzzy sets onto the type-2 fuzzy sets. To achieve the best performance the fuzzy inference system is optimized by the least square and numerical method. The key advantages of the least square method are the efficient use of samples and the simplicity of the implementation. The Web Shopping Expert based on the interval type-2 fuzzy inference system provides more reasonable conclusions for online users.

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.

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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.

Master’s thesis deals with designing decision making system based on fuzzy logic principles which evaluates suppliers offers. In this Master’s thesis there were created two decision making systems based on the demands of the company. First decision making system was designed within the environment of MS Excel, the second one was designed in MATLAB software. In addition of creating the decision making system the thesis puts emphasis on the analysis of the current decision making model in the company and this section will serve as a springboard to designing a decision making system based on fuzzy logic. At the beginning of the work there are included all the necessary theoretical framework on which I based the writing of this thesis.

Today’s society is facing a large increase of automation and smart devices. Everything from coffee machines to fridges include some kind of electronics and embedded systems. The focus of this Bachelor’s thesis was to dive deeper into how these automated devices can be controlled and more specifically a robot arm. The main purpose revolved around constructing a robotic arm that could be controlled through three different methods using MATLAB. These three were manual control, numerical analysis control and with a neural network based control. The prototype was created by assembling six servo motors onto 3D-printed parts. The arm consisted of three main parts which were a base, an arm and a gripper. The system was controlled by an Arduino micro-controller connected to a computer. The results show that the manual control method was easy to implement, fast and reliable. It allows control of all the angels for each servo motor, which also means controlling each individual degree of freedom. The numerical way, using Newton-Raphson’s method, broadened the abilities to control the arm but was slower. The third and final solution was to use fuzzy-logic. This ended up being a powerful method allowing for great control with low latency. While unreliable, the method showed great potential and with refinement could surpass the others. The conclusion was that the neural network method was the overall best method for controlling and manoeuvring the robot arm using MATLAB. / Dagens samhälle står inför en stor ökning av automatisering och smarta produkter. Allt från kaffemaskiner till kyl och frys innehåller någon form av elektronik och inbäddade system. Det huvudsakliga syftet med detta kanditatexamensarbete var att gräva djupare i hur dessa automatiserade produkter kan kontrolleras och mer specifikt i detta fall, en robotarm. Projektet handlade om att konstruera en robotarm som kunde styras och kontrolleras genom tre olika metoder i programmet MATLAB. Dessa tre har vi valt att kalla manuell kontroll, numerisk kontroll och neuralt nätverksbaserad kontroll. Prototypen tillverkades genom att montera sexservomotorer på 3D-utskrivna delar. Armen bestod av tre huvuddelar, en bas, en arm och en gripklo. Systemet styrdes av en Arduino mikrokontroll ansluten till en dator. Resultaten visar att den manuella kontrollmetoden var enkel att implementera, snabb samt var tillförlitlig. Den gav precis styrning av alla vinklar för varje servomotor, vilket också innebar att den gav god styrning av varje frihetsgrad. Den numeriska metoden, mer bestämt Newton Raphson’s metod, vidgade möjligheterna att kontrollera armen men var långsammare. Den tredje och sista lösningen var att använda ett neuralt nätverk, fuzzy logic. Detta visade sig vara ett kraftfullt sätt att styra roboten med låg latens. Det neurala nätverket visade sig dock vara opålitligt, men metoden visade stor potential för vidare utveckling och kan då prestera mycket bättre än de andra två metoderna. Slutsatsen var att det neurala nätverket var den generellt bästa metoden för att kontrollera och manövrera robotarmen via programmeringsprogrammet MATLAB.

This thesis reports the work of using simulated annealing to design more efficient fuzzy logic systems to model problems with associated uncertainties. Simulated annealing is used within this work as a method for learning the best configurations of type-1 and type-2 fuzzy logic systems to maximise their modelling ability. Therefore, it presents the combination of simulated annealing with three models, type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and general type-2 fuzzy logic systems to model four bench-mark problems including real-world problems. These problems are: noise-free Mackey-Glass time series forecasting, noisy Mackey-Glass time series forecasting and two real world problems which are: the estimation of the low voltage electrical line length in rural towns and the estimation of the medium voltage electrical line maintenance cost. The type-1 and type-2 fuzzy logic systems models are compared in their abilities to model uncertainties associated with these problems. Also, issues related to this combination between simulated annealing and fuzzy logic systems including type-2 fuzzy logic systems are discussed. The thesis contributes to knowledge by presenting novel contributions. The first is a novel approach to design interval type-2 fuzzy logic systems using the simulated annealing algorithm. Another novelty is related to the first automatic design of general type-2 fuzzy logic system using the vertical slice representation and a novel method to overcome some parametrisation difficulties when learning general type-2 fuzzy logic systems. The work shows that interval type-2 fuzzy logic systems added more abilities to modelling information and handling uncertainties than type-1 fuzzy logic systems but with a cost of more computations and time. For general type-2 fuzzy logic systems, the clear conclusion that learning the third dimension can add more abilities to modelling is an important advance in type-2 fuzzy logic systems research and should open the doors for more promising research and practical works on using general type-2 fuzzy logic systems to modelling applications despite the more computations associated with it.