Dissertations / Theses on the topic 'Turbojet, Modeling, Control, Fuzzy Logic'

Gas turbines are widely used as power sources in many industrial and transportation applications. This kind of engine is the most preferred prime movers in aircrafts, power plants and some marine vehicles. They have different configurations according to their mechanical constructions such as turbo-prop, turbo-shaft, turbojet, etc. These engines have different efficiencies and specifications and some advantages and disadvantages compared to Otto-Cycle engines. In this thesis, a small turbojet engine is investigated in order to find different control algorithms. AMT Olympus HP small turbojet engine has been used to determine the mathematical model of a gas turbine engine. Some important experimental data were taken from AMT Olympus engine by making many experiments. All components of the engine have been modeled by using laws of thermodynamics and some arithmetic calculations such as numerical solution of nonlinear differential equations, digitizing compressor and turbine map etc. This mathematical model is employed to create control algorithm of the engine. At first, standard control strategies had been considered such as P (proportional), PI (proportional integral), and PID (proportional-integral-differential) controllers. Because of the nonlinearities in gas turbines, standard control algorithms are not commonly used in literature. At the second stage fuzzy logic controllers have been designed to control the engine efficiently. This control algorithm was combined with mathematical of the engine in MATLAB environment and input-output relations were investigated. Finally, fuzzy logic control algorithm was embedded into an electronic controller.

Doutoramento em Engenharia Eletrotécnica
A última fronteira da Inteligência Artificial será o desenvolvimento de um sistema computacional autónomo capaz de "rivalizar" com a capacidade de aprendizagem e de entendimento humana. Ainda que tal objetivo não tenha sido até hoje atingido, da sua demanda resultam importantes contribuições para o estado-da-arte tecnológico atual. A Lógica Difusa é uma delas que, influenciada pelos princípios fundamentais da lógica proposicional do raciocínio humano, está na base de alguns dos sistemas computacionais "inteligentes" mais usados da atualidade. A teoria da Lógica Difusa é uma ferramenta fundamental na suplantação de algumas das limitações inerentes à representação de informação incerta em sistemas computacionais. No entanto esta apresenta ainda algumas lacunas, pelo que diversos melhoramentos à teoria original têm sido introduzidos ao longo dos anos, sendo a Lógica Difusa de Tipo-2 uma das mais recentes propostas. Os novos graus de liberdade introduzidos por esta teoria têm-se demonstrado vantajosos, particularmente em aplicações de modelação de sistemas não-lineares complexos. Uma das principais vantagens prende-se com o aumento da robustez dos modelos assim desenvolvidos comparativamente àqueles baseados nos princípios da Lógica Difusa de Tipo-1 sem implicar necessariamente um aumento da sua dimensão. Tal propriedade é particularmente vantajosa considerando que muitas vezes estes modelos são utilizados como suporte ao desenvolvimento de sistemas de controlo que deverão ser capazes de assegurar o comportamento ótimo de um processo em condições de operação variáveis. No entanto, o estado-da-arte da teoria de controlo de sistemas baseada em modelos não tem integrado todos os melhoramentos proporcionados pelo desenvolvimento de modelos baseados nos princípios da Lógica Difusa de Tipo-2. Por essa razão, a presente tese propõe-se a abordar este tópico desenvolvendo uma metodologia de síntese de Controladores Preditivos baseados em modelos Takagi-Sugeno seguindo os princípios da Lógica Difusa de Tipo-2. De modo a cumprir este objetivo, quatro linhas de investigação serão debatidas neste trabalho.Primeiramente proceder-se-á ao desenvolvimento de uma metodologia de treino de Modelos Difusos de Tipo-2 simplificada, focada em dois paradigmas: manter a clareza dos intervalos de incerteza introduzidos sobre um Modelo Difuso de Tipo-1; assegurar a validade dos diversos modelos localmente lineares que constituem a estrutura Takagi- Sugeno, de modo a torná-los adequados a métodos de síntese de controladores baseados em modelos. O modelo desenvolvido é tipicamente utilizado para extrapolar o comportamento do sistema numa janela temporal futura. No entanto, quando usados em aproximações de sistemas não lineares, os modelos do tipo Takagi-Sugeno estabelecem um compromisso entre exatidão e complexidade computacional. Assim, é proposta a utilização dos princípios da Lógica Difusa de Tipo-2 para reduzir a influência dos erros de modelação nas estimações obtidas através do ajuste dos intervalos de incerteza dos parâmetros do modelo. Com base na estrutura Takagi-Sugeno, um método de linearização local de modelos não-lineares será utilizado em cada ponto de funcionamento do sistema de modo a obter os parâmetros necessários para a síntese de um controlador otimizado numa janela temporal futura de acordo com os princípios da teoria de Controlo Preditivo Generalizado - um dos algoritmos de Controlo Preditivo mais utilizado na indústria. A qualidade da resposta do sistema em malha fechada e a sua robustez a perturbações serão então comparadas com implementações do mesmo algoritmo baseadas em métodos de modelação mais simples. Para concluir, o controlador proposto será implementado num System-on-Chip baseado no core ARM Cortex-M4. Com o propósito de facilitar a realização de testes de implementação de algoritmos de controlo em sistemas embutidos, será apresentada também uma plataforma baseada numa arquitetura Processor-In-the-Loop, que permitirá avaliar a execução do algoritmo proposto em sistemas computacionais com recursos limitados, aferindo a existência de possíveis limitações antes da sua aplicação em cenários reais. A validade do novo método proposto é avaliada em dois cenários de simulação comummente utilizados em testes de sistemas de controlo não-lineares: no Controlo da Temperatura de uma Cuba de Fermentação e no Controlo do Nível de Líquidos num Sistema de Tanques Acoplados. É demonstrado que o algoritmo de controlo desenvolvido permite uma melhoria da performance dos processos supramencionados, particularmente em casos de mudança rápida dos regimes de funcionamento e na presença de perturbações ao processo não medidas.
The development of an autonomous system capable of matching human knowledge and learning capabilities embedded in a compact yet transparent way has been one of the most sought milestones of Artificial Intelligence since the invention of the first mechanical general purpose computers. Such accomplishment is yet to come but, in its pursuit, important contributions to the state-of-the-art of current technology have been made. Fuzzy Logic is one of such, supporting some of the most used frameworks for embedding human-like knowledge in computational systems. The theory of Fuzzy Logic overcame some of the difficulties that the inherent uncertainty in information representations poses to the development of computational systems. However, it does present some limitations so, aiming to further extend its capabilities, several improvements over its original formalization have been proposed over the years such as Type-2 Fuzzy Logic - one of its most recent advances. The additional degrees of freedom of Type-2 Fuzzy Logic are showing greater potential to supplant its original counterpart, especially in complex non-linear modeling tasks. One of its main outcomes is its capability of improving the developed model’s robustness without necessarily increasing its dimensionality comparatively to a Type-1 Fuzzy Model counterpart. Such feature is particularly advantageous if one considers these model as a support for developing control systems capable of maintaining a process’s optimal performance over changing operating conditions. However, state-of-the art model-based control theory does not seem to be taking full advantage of the improvements achieved with the development of Type-2 Fuzzy Logic based models. Therefore, this thesis proposes to address this problem by developing a Model Predictive Control system supported by Interval Type-2 Takagi- Sugeno Fuzzy Models. To accomplish this goal, four main research directions are covered in this work.Firstly, a simpler method for training a Type-2 Takagi-Sugeno Fuzzy Model focused on two main paradigms is proposed: maintaining a meaningful interpretation of the uncertainty intervals embedded over an estimated Type-1 Fuzzy Model; ensuring the validity of several locally linear models that constitute the Takagi-Sugeno structure in order to make them suitable for model-based control approaches. Based on the developed model, a multi-step ahead estimation of the process behavior is extrapolated. However, as Takagi-Sugeno Fuzzy Models establish a trade-off between accuracy and computational complexity when used as a non-linear process approximation, it is proposed to apply the principles of Type-2 Fuzzy Logic to reduce the influence of modeling uncertainties on the obtained estimations by adjusting the model parameters’ uncertainty intervals. Supported by the developed Type-2 Takagi-Sugeno Fuzzy Model, a locally linear approximation of each current operation point is used to obtain the optimal control law over a prediction horizon according to the principles of Generalized Predictive Control - one of the most used Model Predictive Control algorithms in Industry. The improvements in terms of closed loop tracking performance and robustness to unmodeled operation conditions are then assessed comparatively to Generalized Predictive Control implementations based on simpler modeling approaches. Ultimately, the proposed control system is implemented in a general purpose System-on-a-Chip based on a ARM Cortex-M4 core. A Processor-In-the-Loop testing framework, developed to support the implementation of control loops in embedded systems, is used to evaluate the algorithm’s turnaround time when executed in such computationally constrained platform, assessing its possible limitations before deployment in real application scenarios. The applicability of the new methods introduced in this thesis is illustrated in two simulated processes commonly used in non-linear control benchmarking: the Temperature Control of a Fermentation Reactor and the Liquid Level Control of a Coupled Tanks System. It is shown that the developed control system achieves an improved closed loop performance of the above mentioned processes, particularly in the cases of quick changes in the operation regime and in presence of unmeasured external disturbances.

The need for automation, optimality and efficiency has made modern day control and monitoring systems extremely complex and data abundant. However, the complexity of the systems and the abundance of raw data has reduced the understandability and interpretability of data which results in a reduced state awareness of the system. Furthermore, different levels of uncertainty introduced by sensors and actuators make interpreting and accurately manipulating systems difficult. Classical mathematical methods lack the capability to capture human knowledge and increase understandability while modeling such uncertainty. Fuzzy Logic has been shown to alleviate both these problems by introducing logic based on vague terms that rely on human understandable terms. The use of linguistic terms and simple consequential rules increase the understandability of system behavior as well as data. Use of vague terms and modeling data from non-discrete prototypes enables modeling of uncertainty. However, due to recent trends, the primary research of fuzzy logic have been diverged from the basic concept of understandability. Furthermore, high computational costs to achieve robust uncertainty modeling have led to restricted use of such fuzzy systems in real-world applications. Thus, the goal of this dissertation is to present algorithms and techniques that improve understandability and uncertainty modeling using Fuzzy Logic Systems. In order to achieve this goal, this dissertation presents the following major contributions: 1) a novel methodology for generating Fuzzy Membership Functions based on understandability, 2) Linguistic Summarization of data using if-then type consequential rules, and 3) novel Shadowed Type-2 Fuzzy Logic Systems for uncertainty modeling. Finally, these presented techniques are applied to real world systems and data to exemplify their relevance and usage.

In recent years, response analysis of complex structures under impact loads has attracted a great deal of attention. For example, a collision or an accident that produces impact loads that exceed the design load can cause severe damage on the structural components. Although the AASHTO specification is used for impact-resistant bridge design, it has many limitations. The AASHTO specification does not incorporate complex and uncertain factors. Thus, a well-designed structure that can survive a collision under specific conditions in one region may be severely damaged if it were impacted by a different vessel, or if it were located elsewhere with different in-situ conditions. With these limitations in mind, we propose different solutions that use smart control technology to mitigate impact hazard on structures. However, it is challenging to develop an accurate mathematical model of the integrated structure-smart control systems. The reason is due to the complicated nonlinear behavior of the integrated nonlinear systems and uncertainties of high impact forces. In this context, novel algorithms are developed for identification, control and monitoring of nonlinear responses of smart structures under high impact forces. To evaluate the proposed approaches, a smart aluminum and two smart reinforced concrete beam structures were designed, manufactured, and tested in the High Impact Engineering Laboratory of Civil and Environmental Engineering at WPI. High-speed impact force and structural responses such as strain, deflection and acceleration were measured in the experimental tests. It has been demonstrated from the analytical and experimental study that: 1) the proposed system identification model predicts nonlinear behavior of smart structures under a variety of high impact forces, 2) the developed structural health monitoring algorithm is effective in identifying damage in time-varying nonlinear dynamic systems under ambient excitations, and 3) the proposed controller is effective in mitigating high impact responses of the smart structures.

Indiana University-Purdue University Indianapolis (IUPUI)
Electronic Fuel Control (EFC) valve regulates fuel flow to the injector fuel supply line in the Cummins Pressure Time (PT) fuel system. The EFC system controls the fuel flow by means of a variable orifice that is electrically actuated. The supplier of the EFC valves inspects all parts before they are sent out. Their inspection test results provide a characteristic curve which shows the relationship between pressure and current provided to the EFC valve. This curve documents the steady state characteristics of the valve but does not adequately capture its dynamic response. A dynamic test procedure is developed in order to evaluate the performance of the EFC valves. The test itself helps to understand the effects that proposed design changes will have on the stability of the overall engine system. A by product of this test is the ability to evaluate returned EFC valves that have experienced stability issues. The test determines whether an EFC valve is faulted or not before it goes out to prime time use. The characteristics of a good valve and bad valve can be observed after the dynamic test. In this thesis, a mathematical model has been combined with experimental research to investigate and understand the behavior of the characteristics of different types of EFC valves. The model takes into account the dynamics of the electrical and mechanical portions of the EFC valves. System Identification has been addressed to determine the transfer functions of the different types of EFC valves that were experimented. Methods have been used both in frequency domain as well as time domain. Also, based on the characteristic patterns exhibited by the EFC valves, fuzzy logic has been implemented for the use of pattern classification.

"Maintaining an efficient and reliable infrastructure requires continuous monitoring and control. In order to accomplish these tasks, algorithms are needed to process large sets of data and for modeling based on these processed data sets. For this reason, computationally efficient and accurate modeling algorithms along with data compression techniques and optimal yet practical control methods are in demand. These tools can help model structures and improve their performance. In this thesis, these two aspects are addressed separately. A principal component analysis based adaptive neuro-fuzzy inference system is proposed for fast and accurate modeling of time-dependent behavior of a structure integrated with a smart damper. Since a smart damper can only dissipate energy from structures, a challenge is to evaluate the dissipativity of optimal control methods for smart dampers to decide if the optimal controller can be realized using the smart damper. Therefore, a generalized deterministic definition for dissipativity is proposed and a commonly used controller, LQR is proved to be dissipative. Examples are provided to illustrate the effectiveness of the proposed modeling algorithm and evaluating the dissipativity of LQR control method. These examples illustrate the effectiveness of the proposed modeling algorithm and dissipativity of LQR controller."

Orientadores: Flávio Vasconcelos da Silva, Pedro Leite de Santana
Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Química
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Resumo: Os reatores de leito fixo constituem importantes sistemas da engenharia química, com muitas aplicações em diversos processos industriais, notadamente no campo das indústrias de refino do petróleo e petroquímica. Do ponto de vista da analise de processos químicos, se mostra relevante o estabelecimento de modelos matemáticos representativos que possam ser aplicados ao projeto, a otimização e ao controle desses sistemas. Neste trabalho, apresenta-se a modelagem matemática fenomenológica de um reator de leito fixo para a síntese de anidrido ftalico por oxidação de o - xileno, considerando-se as resistências difusionais mássicas e térmicas externas associadas ao processo de reação catalítica, o qual e realizado com catalisadores não porosos a base de óxidos de vanádio e titânio. O modelo matemático foi implementado como um modulo computacional para simulação do processo nos estados estacionário e dinâmico, a partir do qual se fez um estudo de sensibilidade paramétrica que mostrou à forte influencia da temperatura e da concentração de o - xileno na corrente de alimentação, bem como da temperatura do fluido térmico, no comportamento global do reator. Essas constatações permitiram a proposição de estruturas de controle com o objetivo de regular a concentração do produto na saída do reator e manter uma condição térmica operacional segura. Para o controle do reator foram consideradas duas estruturas: (i) um esquema de controle direto da concentração utilizando um controlador convencional PI e um controlador por lógica fuzzy (Fuzzy-PI) e (ii) um esquema de controle cascata concentração-temperatura utilizando também controladores convencionais PI e Fuzzy-PI nas duas malhas (primaria e secundaria) que compõem a estrutura cascata. Cada controlador teve seu desempenho analisado mediante perturbações do tipo degrau impostas nas condições de alimentação dos reagentes (composição e temperatura). Os resultados evidenciaram um bom desempenho das estruturas de controle cascata, que se mostraram eficientes para controlar a concentração do produto na saída do reator e garantir ao mesmo tempo um regime térmico seguro. Na presença de ruído, os controladores Fuzzy-PI apresentaram um desempenho superior ao dos controladores PI convencionais
Abstract: Fixed bed reactors are important systems in chemical engineering with several applications in many industrial processes, mainly in petroleum refining and petrochemical industries. A comprehensive view on these systems through mathematical modeling is crucial for design, optimization and control issues. This work presents a phenomenological mathematical model for a fixed bed reactor applied to the phthalic anhydride synthesis from o-xylene oxidation using supported non-porous V2O5-TiO2 catalyst. All resistances due to mass and heat flows from the fluid phase to the catalytic particle surface are considered in the mathematical formulation. The mathematical model consisted of a Matlab based computational code for the process simulation both in dynamic and steady-state conditions, providing a parametric sensitivity study that showed the intensive influence of the feed conditions in terms of temperature and o-xylene concentration, as well as the effect of thermal fluid temperature on the global reactor behavior. These observations provided control structures to regulate product concentration leaving the reactor and to avoid the formation of excessive hot spots along the catalytic bed, what is a necessary condition to maintain a safe thermal system operation. It were proposed two structures to control the reactor: (i) a straightforward scheme to control the concentration of phthalic anhydride in the reactor outlet using conventional PI and fuzzy PI controllers, and (ii) another scheme based on a cascade control temperature-concentration also using conventional and fuzzy PI controllers in two (primary and secondary) loops. The behavior of each controller was analyzed by imposing stepwise perturbations in inner o-xylene concentration and in the temperature of the feed. The results showed a good performance of cascade-type controllers, providing the proper regulation of the controlled variable and a safe thermal regime for the system operation. On the other hands, fuzzy logic controllers exhibited better performance for the reactor regulation when measurement noise was taken in account
Doutorado
Sistemas de Processos Quimicos e Informatica
Doutor em Engenharia Química

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Financiadora de Estudos e Projetos
A Flexible Manufacturing System is a production system where more than one event can occur simultaneously in parallel, asynchronously or concurrently. These are designed to meet the needs of the market, demand for improved product quality, lower costs and shorter delivery times. Due to the complexity and flexibility of these systems, conflicts can occur when more than one process requests the same resource, such as machines or AGVs, or when a product can be produced in more than one production routing. Thus, a conflict resolution policy is needed in this type of environment. This study uses Fuzzy Coloured Petri Nets for modeling a manufacturing system. The manufacturing system is modeled considering the resources, buffers, production routings, AGV routes and identifying conflict points. The conflict resolution is performed considering both information on the shop floor and production management. Fuzzy rules are constructed to prioritize conflicting processes and a fuzzy rule-based system is modeled on Fuzzy Coloured Petri Nets in the same network as the manufacturing system, making the model of the system independent of other systems of support for the resolution of conflicts. In this work we considered the conflicts that can occur when loading and unloading station and the input and output buffer. According to the source of conflict, a rule base is shaped to assign priorities to processes. From this model it is possible to make the control system and ensure that identified conflicts are resolved.
Um Sistema Flexível de Manufatura é um sistema de produção onde mais de um evento pode ocorrer ao mesmo tempo de forma paralela, assíncrona ou de forma concorrente. Estes foram projetados para atender às necessidades do mercado, que demanda por melhor qualidade dos produtos, redução de custos e prazos de entrega menores. Devido à complexidade e à flexibilidade destes sistemas, conflitos podem ocorrer quando mais de um processo requisita o mesmo recurso, como máquinas ou AGVs, ou quando um produto pode ser produzido em roteiros distintos. Assim, uma política de solução de conflito é necessária neste tipo de ambiente. Este trabalho usa Redes de Petri Coloridas Fuzzy para modelar um sistema de manufatura. O sistema de manufatura é modelado considerando os recursos, buffers, roteiros de produção, rotas dos AGV e identificando os pontos de conflitos. A resolução dos conflitos é realizada considerando tanto informações do chão de fábrica quanto da gestão da produção. Regras fuzzy são construídas para atribuir prioridades aos processos conflitantes e um sistema baseado em regras fuzzy é modelado em Redes de Petri Coloridas Fuzzy na mesma rede do sistema de manufatura, tornado o modelo do sistema independente de outros sistemas de apoio para a solução dos conflitos. Neste trabalho foram considerados os conflitos que podem ocorrer na estação de carga e descarga e nos buffer de entrada e saída. De acordo com o ponto de conflito, uma base de regras é modelada para atribuir prioridades aos processos. A partir desta modelagem é possível realizar o controle do sistema e garantir que os conflitos identificados sejam resolvidos.

博士
國立交通大學
交通運輸研究所
101
On-line traffic signal control typically feeds the real-time traffic data, collected by the sensors, into a build-in controller to produce the timing plans. Thus, it can provide signal-timing plans in response to real-time traffic conditions. Because of its flexibility, applicability and optimality, adaptive signal control tends to be the mainstream of signal controls nowadays. The well-known adaptive signal controllers employ mathematical equations or models to determine “crisp” threshold values as the cores of control mechanism; thus, the control performance could be negatively affected by the uncertainty of traffic conditions. Since a fuzzy control system has excellent performance in data mapping as well as in treating ambiguous or vague judgment, many works have employed fuzzy set theory to develop fuzzy logic controllers (FLC). In FLC systems, both inference engine and defuzzification have been consistently used in previous literature; however, methods for formulating the rule base (logic rules) and data base (membership functions) are subjectively preset, not optimally solved. Employing GAs to construct an FLC system with learning process from examples, hereafter termed as genetic fuzzy logic controller (GFLC), can not only avoid the bias caused by subjective settings of logic rules or membership functions but also greatly enhance the control performance. However, to simultaneously or sequentially learn of logic rules and membership functions may require a rather lengthy chromosome and large search space, resulting into poor performance, a long convergence time and unreasonable learning results (i.e. conflicting or redundant logic rules, irrational shapes of membership functions). To avoid abovementioned shortcomings, based on the iterative GFLC (Chiou and Lan, 2005), this study proposes a stepwise genetic fuzzy logic controller (SGFLC) to learn both logic rules and membership functions. At each learning process, the proposed algorithm selects one logic rule which can best contribute to the overall performance controlled by previously selected logic rules combined with this selected rule. Such a selection procedure will be repeated until no other rule can ever improve the control performance. Therefore, the incumbent combination of logic rules is the near optimal learning results. In order to develop a SGFLC-based signal control requires an efficient traffic simulation model to replicate traffic behaviors and to determine the performance of the control logic. Many studies use microscopic traffic simulation software to simulate the urban signal control and implement the optimized signal policy. However, such simulation software is rather time consuming, making it better for evaluating the control performance for a given signal control model but not suitable for the evolution of genetic generations for model training. For the learning efficiency of SGFLC and the capability in capturing traffic behaviors of Asian urban streets where mixed traffic of cars and motorcycles are prevailing, the mixed traffic cell transmission model (MCTM) is introduced to replicate the traffic behaviors. This study considers traffic flows and queue lengths of cars and motorcycles as the state variables and extension of green time as the control variable, towards the minimization of total vehicle delays. To investigate the control performance of the proposed SGFLC model, comparisons of two pre-timed timing plans and three adaptive signal timing models are conducted at an isolated intersection. Results show our proposed SGFLC model performs the best. Moreover, as traffic flows vary more noticeably, the SGFLC model performs even better than any other models. In the case of a 3-intersection arterial under four coordinated signal systems i.e., simultaneous, progressive, alternate and independent, both experimental example and field case study show that the proposed SGFLC model can perform better than any adaptive control models, suggesting that the proposed SGFLC signal control model is efficient, robust and applicable. Moreover, it is well-known that the control performance of signal coordination would be greatly degraded as the number of coordinated intersections increases. Thus, this study also combines SGFLC with GAs for optimizing the number of coordinated intersections along a long corridor. The experimental example shown that the proposed hybrid model can increase total throughput along the corridor through an optimal coordinated intersections.

Fuzzy logic systems (FLSs) are well known in the literature for their ability to model linguistics and system uncertainties. Due to this ability, FLSs have been successfully used in modeling and control applications such as medicine, finance, communications, and operations research. Moreover, the ability of higher order fuzzy systems to handle system uncertainty has become an interesting topic of research in the field. In particular, type-2 FLSs (T2 FLSs), systems consisting of fuzzy sets with fuzzy grades of membership, a feature that type-1 (T1) does not offer, are most well-known for this capability. The structure of T2 FLSs allows for the incorporation of uncertainty in the input membership grades, a common situation in reasoning with physical systems. General T2 FLSs have a complex structure, thus making them difficult to adopt on a large scale. As a result, interval T2 FLSs (IT2 FLSs), a special class of T2 FLSs, have recently shown great potential in various applications with input-output (I/O) system uncertainties. Due to the sophisticated mathematical structure of IT2 FLSs, little to no systematic analysis has been reported in the literature to use such systems in control design. Moreover, to date, designers have distanced themselves from adopting such systems on a wide scale because of their design complexity. Furthermore, the very few existing control methods utilizing IT2 fuzzy logic control systems (IT2 FLCSs) do not guarantee the stability of their system. Therefore, this thesis presents a systematic method for designing stable IT2 Takagi-Sugeno-Kang (IT2 TSK) fuzzy systems when antecedents are T2 fuzzy sets and consequents are crisp numbers (A2-C0). Five new inference mechanisms are proposed that have closed-form I/O mappings, making them more feasible for FLCS stability analysis. The thesis focuses on control applications for when (a) both plant and controller use A2-C0 TSK models, and (b) the plant uses T1 Takagi-Sugeno (T1 TS) and the controller uses IT2 TS models. In both cases, sufficient stability conditions for the stability of the closed-loop system are derived. Furthermore, novel linear matrix inequality-based algorithms are developed for satisfying the stability conditions. Numerical analyses are included to validate the effectiveness of the new inference methods. Case studies reveal that a well-tuned IT2 TS FLCS using the proposed inference engine can potentially outperform its T1 TSK counterpart, a result of IT2 having greater structural flexibility than T1. Moreover, due to the simple nature of the proposed inference engine, it is easy to implement in real-time control systems. In addition, a novel design methodology is proposed for IT2 TSK FLC for modular and reconfigurable robot (MRR) manipulators with uncertain dynamic parameters. A mathematical framework for the design of IT2 TSK FLCs is developed for tracking purposes that can be effectively used in real-time applications. To verify the effectiveness of the proposed controller, experiments are performed on an MRR with two degrees of freedom which exhibits dynamic coupling behavior. Results show that the developed controller can outperform some well-known linear and nonlinear controllers for different configurations. Therefore, the proposed structure can be adopted for the position control of MRRs with unknown dynamic parameters in trajectory-tracking applications. Finally, a rigorous mathematical analysis of the robustness of FLSs (both T1 and IT2) is presented in the thesis and entails a formulation of the robustness of FLSs as a constraint multi-objective optimization problem. Consequently, a procedure is proposed for the design of robust IT2 FLSs. Several examples are presented to demonstrate the effectiveness of the proposed methodologies. It was concluded that both T1 and IT2 FLSs can be designed to achieve robust behavior in various applications. IT2 FLSs, having a more flexible structure than T1 FLSs, exhibited relatively small approximation errors in the several examples investigated. The rigorous methodologies presented in this thesis lay the mathematical foundations for analyzing the stability and facilitating the design of stabilizing IT2 FLCSs. In addition, the proposed control technique for tracking purposes of MRRs will provide control engineers with tools to control dynamic systems with uncertainty and changing parameters. Finally, the systematic approach developed for the analysis and design of robust T1 and IT2 FLSs is of great practical value in various modeling and control applications.

Need for increased productivity has motivated interest in drilling equipment for high speed and high precision drilling. However, long drills exhibit aggressive and self-excited vibrations known as whirling motions. These vibrations deteriorate the outcome drilled surface qualities, i.e., finish, geometrical dimensioning and tolerances. Deep hole boring is the most complex and expensive drilling operation in terms of expertise needed, operating conditions and size. The present research is to study the whirling vibrations of the boring bar workpiece system in the BTA deep hole boring process. After a review of the literature, a set of objectives is outlined for the thesis. The objectives are to formulate an analytical model, which will include the self-excited motion caused by the internal cutting forces in the Z and Y directions and the active suppression forces used to counteract the cutting forces. This model will be used to carry out simulation studies on the whirling vibrations of the boring bar-workpiece system and to carry out some experiments in order to validate the analytical model. In view of the large number of machining parameters that influence the system dynamics and the resulting surface finish, geometrical dimensioning and tolerancing on the workpiece, a fuzzy logic model is proposed for off line modeling. Such a model will be used to examine the results obtained in the experiments and to predict the best parametric combinations that will provide the required surface finish. This will constitute an open loop control of the vibrations. An active vibration suppression is carried out to reduce the whirling motion of the boring bar-workpiece system, which in turn will improve the surface finish. Two electrodynamic shakers are used in Z-Y plane to overcome the displacement of the boring bar while drilling. The experimental investigations are carried out in the deep hole boring laboratory. Instrumentation involving proximity pickups, oscillator, amplifiers and demodulator are used to measure the displacement response and the orbital motion of the boring bar-workpiece system, and two electrodynamic shakers to suppress the motion. The surface roughness, straightness and circularity are measured which are related to the whirling vibrations and the machining parameters.

Indiana University-Purdue University Indianapolis (IUPUI)
In this research, Environmental Fluid Dynamic Code (EFDC) and Adaptive- Networkbased Fuzzy Inference System Models (ANFIS) were developed and implemented to determine the spatial-temporal distribution of cyanobacterial metabolites: 2-MIB and geosmin, in Eagle Creek Reservoir, IN. The research is based on the current need for understanding algae dynamics and developing prediction methods for algal taste and odor release events. In this research the methodology for prediction of 2-MIB and geosmin production was explored. The approach incorporated a combination of numerical and heuristic modeling to show its capabilities in prediction of cyanobacteria metabolites. The reservoir’s variable data measured at monitoring stations and consisting of chemical/physical and biological parameters with the addition of calculated mixing conditions within the reservoir were used to train and validate the models. The Adaptive – Network based Fuzzy Inference System performed satisfactorily in predicting the metabolites, in spite of multiple model constraints. The predictions followed the generally observed trends of algal metabolites during the three seasons over three years (2008-2010). The randomly selected data pairs for geosmin for validation achieved coefficient of determination of 0.78, while 2-MIB validation was not accepted due to large differences between two observations and their model prediction. Although, these ANFIS results were accepted, the further application of the ANFIS model coupled with the numerical models to predict spatio-temporal distribution of metabolites showed serious limitations, due to numerical model calibration errors. The EFDC-ANFIS model over-predicted Pseudanabaena spp. biovolumes for selected stations. The predicted value was 18,386,540 mm3/m3, while observed values were 942,478 mm3/m3. The model simulating Planktothrix agardhii gave negative biovolumes, which were assumed to represent zero values observed at the station. The taste and odor metabolite, geosmin, was under-predicted as the predicted v concentration was 3.43 ng/L in comparison to observed value of 11.35 ng/l. The 2-MIB model did not validate during EFDC to ANFIS model evaluation. The proposed approach and developed methodology could be used for future applications if the limitations are appropriately addressed.