The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.

Adverse weather such as hurricanes can significantly affect the reliability of composite power systems. Predicting the impact of hurricanes can help utilities for better preparedness and make appropriate restoration arrangements. In this dissertation, the impact of hurricanes on the reliability of composite power systems is investigated. Firstly, the impact of adverse weather on the long-term reliability of composite power systems is investigated by using Markov cut-set method. The Algorithms for the implementation is developed. Here, two-state weather model is used. An algorithm for sequential simulation is also developed to achieve the same goal. The results obtained by using the two methods are compared. The comparison shows that the analytical method can obtain comparable results and meantime it can be faster than the simulation method. Secondly, the impact of hurricanes on the short-term reliability of composite power systems is investigated. A fuzzy inference system is used to assess the failure rate increment of system components. Here, different methods are used to build two types of fuzzy inference systems. Considering the fact that hurricanes usually last only a few days, short-term minimal cut-set method is proposed to compute the time-specific system and nodal reliability indices of composite power systems. The implementation demonstrates that the proposed methodology is effective and efficient and is flexible in its applications. Thirdly, the impact of hurricanes on the short-term reliability of composite power systems including common-cause failures is investigated. Here, two methods are proposed to archive this goal. One of them uses a Bayesian network to alleviate the dimensionality problem of conditional probability method. Another method extends minimal cut-set method to accommodate common-cause failures. The implementation results obtained by using the two methods are compared and their discrepancy is analyzed. Finally, the proposed methods in this dissertation are also applicable to other applications in power systems.

In this thesis we have considered a cognitive radio network (CRN) with a pair of primary user (PU) and secondary user (SU) in spectrum sharing networks in path-loss and without path-loss propagation environments under identically distributed m-Nakagami fading channel. The thesis consists of three parts. In the first part we propose an optimized Takagi-Sugeno Fuzzy Inference System (FIS) based power control strategy in cognitive radio networks (CRN) in spectrum sharing network in without path-loss propagation environment. The second part proposes an optimized Takagi-Sugeno FIS based power control strategy in cognitive radio networks in spectrum sharing network in path-loss propagation environment. For without path-loss propagation environment the proposed FIS takes the interference channel gain ratio between SU transmitter (CUtx) and PU receiver (PUrx) and Signal to Noise Ratio (SNR) towards PU transmitter (PUtx) as antecedents and outputs the power scaling factor for SU. For path-loss propagation environment the proposed FIS takes the relative distance ratio between CUtx and PUrx and SNR towards PUtx as antecedents and outputs the power scaling factor for SU. The output power scaling factor is used to vary the transmit power of SU such that it does not degrade the quality of service (QoS) of PU link. The third part presents an implementation of orthogonal frequency division multiplexing (OFDM) transmission technique in CRN. The OFDM technique has intellectual attractive features like coping with the inter symbol interference (ISI), while providing increasing spectral efficiency and improved performance. This can be used in emergency conditions where transmission requires reliability and high data rate. The OFDM transmission technique is applied towards SU transmitter in CRN, which enables SU to utilize the spectrum efficiently under various fading environments. Spectrum sharing networks in with and without path-loss propagation environments and OFDM transmission were tested for bit error rate (BER) performance after fading effects from m-Nakagami fading channel. We conclude that by applying Takagi-Sugeno Fuzzy Inference System (FIS) based power control strategy we can improve the BER performance of PU when compared with no power control strategy and with other fuzzy based power control technique. OFDM transmission technique gives us better data rate and slightly improved BER in CRN hence making it suitable for use in emergency conditions. / mobile: 0735032048 (Muneeb Goraya)

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.

[pt] Esta dissertação investiga a utilização de sistemas Neuro- Fuzzy Hierárquicos BSP (Binary Space Partitioning) para aplicações em classificação de padrões, previsão, sistemas de controle e extração de regras fuzzy. O objetivo é criar um modelo Neuro-Fuzzy Hierárquico BSP do tipo Mamdani a partir do modelo Neuro-Fuzzy Hierárquico BSP Class (NFHB-Class) que é capaz de criar a sua própria estrutura automaticamente e extrair conhecimento de uma base de dados através de regras fuzzy, lingüisticamente interpretáveis, que explicam a estrutura dos dados. Esta dissertação consiste de quatros etapas principais: estudo dos principais sistemas hierárquicos; análise do sistema Neuro-Fuzzy Hierárquico BSP Class, definição e implementação do modelo NFHB-Mamdani e estudo de casos. No estudo dos principais sistemas hierárquicos é efetuado um levantamento bibliográfico na área. São investigados, também, os principais modelos neuro-fuzzy utilizados em sistemas de controle - Falcon e o Nefcon. Na análise do sistema NFHB- Class, é verificado o aprendizado da estrutura, o particionamento recursivo, a possibilidade de se ter um maior número de entrada - em comparação com outros sistemas neuro-fuzzy - e regras fuzzy recursivas. O sistema NFHB- Class é um modelo desenvolvido especificamente para classificação de padrões, como possui várias saídas, não é possível utilizá-lo em aplicações em controle e em previsão. Para suprir esta deficiência, é criado um novo modelo que contém uma única saída. Na terceira etapa é definido um novo modelo Neuro-Fuzzy Hierárquico BSP com conseqüentes fuzzy (NFHB-Mamdani), cuja implementação utiliza a arquitetura do NFHBClass para a fase do aprendizado, teste e validação, porém, com os conseqüentes diferentes, modificando a estratégia de definição dos conseqüentes das regras. Além de sua utilização em classificação de padrões, previsão e controle, o sistema NFHB-Mamdani é capaz de extrair conhecimento de uma base de dados em forma de regras do tipo SE ENTÃO. No estudo de casos são utilizadas duas bases de dados típicas para aplicações em classificação: Wine e o Iris. Para previsão são utilizadas séries de cargas elétricas de seis companhias brasileiras diferentes: Copel, Cemig, Light, Cerj, Eletropaulo e Furnas. Finalmente, para testar o desempenho do sistema em controle faz-se uso de uma planta de terceira ordem como processo a controlar. Os resultados obtidos para classificação, na maioria dos casos, são superiores aos melhores resultados encontrados pelos outros modelos e algoritmos aos quais foram comparados. Para previsão de cargas elétricas, os resultados obtidos estão sempre entre os melhores resultados fornecidos por outros modelos aos quais formam comparados. Quanto à aplicação em controle, o modelo NFHB-Mamdani consegue controlar, de forma satisfatória, o processo utilizado para teste. / [en] This paper investigates the use of Binary Space Partitioning (BSP) Hierarchical Neuro-Fuzzy Systems for applications in pattern classification, forecast, control systems and obtaining of fuzzy rules. The goal is to create a BSP Hierarchical Neuro-Fuzzy Model of the Mamdani type from the BSP Hierarchical Neuro-Fuzzy Class (NFHB-Class) which is able to create its own structure automatically and obtain knowledge from a data base through fuzzy rule, interpreted linguistically, that explain the data structure. This paper is made up of four main parts: study of the main Hierarchical Systems; analysis of the BSP Hierarchical Neuro-Fuzzy Class System, definition and implementation of the NFHB-Mamdani model, and case studies. A bibliographical survey is made in the study of the main Hierarchical Systems. The main Neuro-Fuzzy Models used in control systems - Falcon and Nefcon -are also investigated. In the NFHB-Class System, the learning of the structure is verified, as well as, the recursive partitioning, the possibility of having a greater number of inputs in comparison to other Neuro-Fuzzy systems and recursive fuzzy rules. The NFHB-Class System is a model developed specifically for pattern classification, since it has various outputs, it is not possible to use it in control application and forecast. To make up for this deficiency, a new unique output model is developed. In the third part, a new BSP Hierarchical Neuro-Fuzzy model is defined with fuzzy consequents (NFHB-Mamdani), whose implementation uses the NFHB-Class architecture for the learning, test, and validation phase, yet with the different consequents, modifying the definition strategy of the consequents of the rules. Aside from its use in pattern classification, forecast, and control, the NFHB-Mamdani system is capable of obtaining knowledge from a data base in the form of rules of the type IF THEN. Two typical data base for application in classification are used in the case studies: Wine and Iris. Electric charge series of six different Brazilian companies are used for forecasting: Copel, Cemig, Light, Cerj, Eletropaulo and Furnas. Finally, to test the performance of the system in control, a third order plant is used as a process to be controlled. The obtained results for classification, in most cases, are better than the best results found by other models and algorithms to which they were compared. For forecast of electric charges, the obtained results are always among the best supplied by other models to which they were compared. Concerning its application in control, the NFHB-Mamdani model is able to control, reasonably, the process used for test.

The transition of emphasis in business competition from a technology-led age to a market-oriented era has led to a rapid shift from the conventional "economy of scale" towards the "economy of scope" in contemporary manufacturing. Hence, it is necessary and essential to be able to respond to the dynamic market and customer requirements systematically and consistently. The central theme of this research is to rationalise and improve the conventional means of analysing and interpreting the linguistic and often imprecise customer requirements in order to identify the essential product features and determine their appropriate design targets dynamically and quantitatively through a series of well proven methodologies and techniques. The major objectives of this research are: a) To put forward a hybrid approach for decoding and processing the Voice of Customer (VoC) in order to interpret the specific customer requirements and market demands into definitive product design features, and b) To quantify the essential product design features with the appropriate technical target values for facilitating the downstream planning and control activities in delivering the products or services. These objectives would be accomplished through activities as follows: • Investigating and understanding the fundamental nature and variability of customer attributes (requirements); • Surveying and evaluating the contemporary approaches in handling customer attributes; • Proposing an original and generic hybrid model for categorising, prioritising and interpreting specific customer attributes into the relevant product attributes with tangible target values; • Developing a software system to facilitate the implementation of the proposed model; • Demonstrating the functions of the hybrid model through a practical case study. This research programme begins with a thorough overview of the roles, the changing emphasis and the dynamic characteristics of the contemporary customer demand with a view to gaining a better understanding on the fundamental nature and variability of customer attributes. It is followed by a review of a number of well proven tools and techniques including QFD, HoQ, Affinity Diagram and AHP etc. on their applicability and effectiveness in organising, analysing and responding to dynamic customer requirements. Finally, an intelligent hybrid model amalgamating a variety of these techniques and a fuzzy inference sub-system is proposed to handle the diverse, ever-changing and often imprecise VoC. The proposed hybrid model is subsequently demonstrated in a practical case study.

Made available in DSpace on 2014-06-11T19:31:37Z (GMT). No. of bitstreams: 0 Previous issue date: 2007-12-03Bitstream added on 2014-06-13T21:02:57Z : No. of bitstreams: 1 benini_lc_dr_botfca.pdf: 1063271 bytes, checksum: ac62bd61becd308d39b1c058ea679181 (MD5) / Este trabalho tem por objetivo principal apresentar o desenvolvimento de um sistema inteligente, utilizando a Teoria Fuzzy, para estimar valores aproximados da densidade do solo a partir de medidas diretas (campo) sem a necessidade de ensaios laboratoriais e, consequentemente, identificar a compactação do solo por meio destes valores estimados. A densidade do solo é um dos principais parâmetros utilizado para a identificação do grau de compactação do solo, e está relacionada com outros parâmetros tais como a resistência à penetração do solo, o teor de água e a textura do solo. Para o desenvolvimento do trabalho foram considerados três parâmetros do solo: a resistência à penetração representado pelo índice de cone (em kPa), o teor de água dado pela umidade do solo (em porcentagem, %), e a textura dada pela quantidade de argila presente no solo (em porcentagem, %). Foram, ainda, considerados solos preparados (passagem de arado, de grade, de escarificador, e outros) e solos não preparados (nenhum tipo de preparado ou em solo de plantio direto). Segundo a porcentagem de argila no solo, estes foram divididos em solo tipo I (teor de argila menor que 30%), solo tipo II (teor de argila entre 30% e 50%), solo tipo III (teor de argila maior que 50%) para o solo não preparado, e solo tipo I (teor de argila menor que 30%) e solo tipo III (teor de argila maior que 50%) para o solo preparado. O modelo matemático proposto para determinar as estimativas da densidade do solo foi desenvolvido com base em dados experimentais representados pelas três características do solo: índice de cone, umidade e argila. Utilizando os dados experimentais os modelos foram identificados por meio de um algoritmo neuro-fuzzy, em função da resistência à penetração, teor de água e textura do solo, onde se pode analisar a densidade do solo para os distintos valores das variáveis de entradas... / The present work aims to develop a intelligent system using fuzzy theory in order to estimate approximate values for the soil density taking in account direct measurements (in loco) disregarding laboratorial essays and, consequently, to identify the compactation of the soil through those estimated values. The soil density is one of the main parameters used to identify the soil compactation level, and it is also related to other parameters such as resistance to the soil penetration, water content and soil texture. Three soil parameters were considered for the development of this work: resistance to the soil penetration represented by the cone index (in kPa), the water content given by the soil humidity (percentage, %), and the texture given by the quantity of clay present in the soil (percentage, %). Also, prepared soils were considered (plough step, grid, disk harrow, and others) as well as non prepared soils (no kind of soil preparation or direct planted soil). According to the percentage of clay in the soil, they were classified as soil type I (clay content less than 30%), soil type II (clay content between 30% and 50%), soil type III (clay content higher than 50%) for the case of non prepared soil. For the case of prepared soil it was considered only soils type I (clay content less than 30%) and type III (clay content higher than 50%). The mathematical model considered to estimate the soil density was developed on the basis of given experimental data having the three soil characteristics: Cone index, humidity and clay content. Using the experimental data the models were identified by means of a neuro-fuzzy algorithm in function of the resistance to the penetration, water content and soil texture, through which one can analyze the soil density for different values of the model entrance variables. The experimental data and the estimated ones by the model...(Complete abstract click electronic access below)

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.