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För att ett fordon ska kunna bromsa, accelerera och svänga är friktion mellan däcken och vägen ett måste. Vid för mycket gaspådrag under en acceleration kan det hända att hjulen förlorar fäste och börjar spinna loss, något som leder till både försämrad kontroll över fordonet och att däcken slits ut i förtid. Traction controlsystemet förhindrar hjulen från att spinna loss och försöker maximera friktionen. Målet med detta examensarbete är att utvärdera olika reglerprinciper samt att undersöka olika möjligheter för att reglera friktionen mellan däck och väg. Det är ett svårt reglerproblem, dels på grund av dess olinjäritet, dels på grund av det faktum att friktionen är en okänd parameter. För att kunna undersöka olika reglermöjligheter har en modell över hjuldynamiken och en modell över drivlinan tagits fram i Matlabs simuleringsprogram Simulink. Därutöver har tre regulatorer designats: en fuzzy-regulator, en fuzzy-P-regulator och en PI-regulator. Regulatorerna utvärderades i tre tester som bland annat testade deras robusthet. Fuzzy-regulatorn och fuzzy-P-regulatorn lyckades reglera systemet bra. PI-regulatorn gjorde däremot inte ett tillfredsställande jobb, mest på grund av dess behov av ett börvärde. / Traction is necessary for a vehicle to be able to brake, accelerate and turn. When pushing the accelerator pedal too hard during an acceleration, the wheel can loose traction and start spinning, which leads to a worsen vehicle control and also wears out the tyres faster. The traction control system prevents the wheels from spinning and tries to make the tyres maintain maximum traction. The purpose of this master’s thesis is to evaluate different control methods and to investigate possible ways to control the traction. This is a difficult control problem due to its nonlinearity and the fact that the friction is an unknown parameter. For the investigation, a model of the wheel dynamics and a model of the powertrain have been developed in Matlab’s simulation program Simulink. Furthermore, three different controllers have been designed; a fuzzy controller, a fuzzy-P controller and a PI controller. The controllers were evaluated in three test cycles that among others tested their robustness. The fuzzy controller and the fuzzy-P controller managed to control the system very well. The PI controller, however, did not work satisfactory, mainly because of its need of a desired value.

Independent mobility is critical to quality of life for people of all ages, and impaired mobility leaves one with both physical and mental disadvantages. Unfortunately, there are some individuals unable to operate an electric wheelchair due to physical, perceptual, or cognitive deficits. The prime objective of this research was to develop a prototype system which can provide mobility assistant to individuals who would otherwise find it difficult or impossible to operate a power wheelchair. To accomplish this goal, a prototype system consisting of several components including an embedded microcontroller and multiple sensors has been designed which can be added to a standard power wheelchair and make it smart. The control system algorithm designed for this prototype model is based on the fuzzy logic control theory and its main purpose is to augment the user ability to navigate the wheelchair and will provide a safe and comfortable journey to the user.

In this thesis, an algorithm that improves the performance of the extended Kalman filter (EKF) on the mobile robot localization issue is proposed, which is aided by the cooperation of neural network and fuzzy logic. An EKF is used to fuse the information acquired from both the robot optical encoders and the external sensors in order to estimate the current robot position and orientation. Then the error covariance of the EKF is tracked by the covariance matching technique. When the output of the matching technique does not meet the desired condition, a fuzzy logic is employed to adjust the error covariance matrix to modify it back to the desired value range. Since the fuzzy logic is lack of the capability of learning, a neural network is presented in the algorithm to train the EKF. The simulation results demonstrate that, with the comparison to the odometry and the standard EKF method under the same error divergence condition, the proposed extended Kalman filter effectively improves the accuracy of the localization of the mobile robot system and effectively prevents the filter divergence.

This thesis presents work on the use of intelligent algorithms to solve a real-world machine vision problem in the automotive industry. Compared to commercial systems, the algorithm developed is both more robust to changes in the inspection environment and more intuitive for the user to configure. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2007-07-12 10:24:43.367

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In order to investigate the use of fuzzy logic in decision-support systems (DSS) for dairy cattle breeding, a first-generation prototype software system was developed. The objectives were to determine the advantages and limitations of fuzzy logic for this type of application, and to establish a basis for the development of more complete DSS in the future. The goal of the prototype DSS was to make culling decisions on the basis of monthly production data. An analysis of the development process of this prototype demonstrated the importance of creating a thorough methodology for the elicitation and implementation of knowledge. A framework for the development of fuzzy decision-support systems was established, consisting of four phases: the project groundwork phase, elicitation of knowledge from the expert, implementation of that knowledge, and system validation. In this framework, it is proposed that, in the case of multiple experts, knowledge can be amalgamated or aggregated. Once this framework was established, a second-generation prototype DSS was developed. Contrary to the first-generation prototype, where the encoded expertise was limited to three experts from the same domain, the second-generation prototype considered the knowledge of two individuals from each of three domains (Dairy researchers, Producers, and Dairy herd improvement specialists). An aggregation approach was used which involved the development and maintenance of separate modules, each containing the compiled expertise of one of the six experts.

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One of the fundamental aspects of a virtual environment is the virtual agents that inhabit them. In many applications, virtual agents are required to perceive input information from their environment and make decisions appropriate to their task based on their programmed reaction to those inputs. The research presented in this thesis focuses on the reactive behaviour of the agents. We propose a new control architecture to allow agents to behave autonomously in navigation tasks in unknown environments. Our behaviour-based architecture uses fuzzy logic to solve problems of agent control and action selection and which can coordinate conflicts among different operations of reactive behaviours. A Fuzzy Associative Memory (FAM) is used as the process of encoding and mapping the input fuzzy sets to the output fuzzy set and to optimise the fuzzy rules. Our action selection algorithm is based on the fuzzy α-level method with the Hurwicz criterion. The main objective of the thesis was to implement agent navigation from point to point by a coordination of planning, sensing and control. However, we believe that the reactive architecture emerging from this research is sufficiently general that it could be applied to many applications in widely differing domains where real-time decision making under uncertainty is required. To illustrate this generality, we show how the architecture is applied to a different domain. We chose the example of a computer game since it clearly demonstrates the attributes of our architecture: real-time action selection and handling uncertainty. Experimental results are presented for both implementations which show how the fuzzy method is applied, its generality and that it is robust enough to handle different uncertainties in different environments. In summary, the proposed reactive architecture is shown to solve aspects of behaviour control for autonomous virtual agents in virtual environments and can be applied to various application domains.