Modelo fuzzy genético para a estimação de forças em correntes a partir da medição das frequências naturais

Orientador: Milton Dias JuniorDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia MecânicaResumo: As instalações em alto mar possuem linhas de ancoragem, chamadas de amarras, para proporcionar estabilidade, suporte e sustentação às estruturas. Essas linhas de ancoragem são geralmente compostas por cabos, correntes e cordas de fibra sintética. Quando a solicitação de carga é alta, as linhas de ancoragem devem ser constituídas por corrente. O monitoramento da força atuando nestas correntes é vital para a confiabilidade e segurança da produção de energia. Os métodos atuais para supervisionar as cargas nas amarras são caros e têm muitas incertezas envolvidas. Nesse contexto, propõe-se uma nova metodologia para a estimativa de força em correntes através da medição de suas frequências naturais. Um sistema de inferência difuso e otimizado por um algoritmo genético foi desenvolvido para estimar da carga nas correntes. As entradas dos modelos difusos são as frequências naturais das correntes e a saída é a força estimada. As metodologias Mamdani e Sugeno foram implementadas e comparadas. Funções de pertinência triangular e gaussiana foram usadas para modelar as entradas e a saída. As regras foram definidas de acordo com as relações entre as frequências naturais e a força na corrente. Para otimizar o sistema, o algoritmo genético pode usar como dados de treinamento os resultados fornecidos por um modelo matemático ou por um conjunto de medições. O modelo matemático desenvolvido apresenta boa concordância com os dados experimentais. O modelo genético difuso foi simulado e testado, fornecendo boa precisão na estimativa da força. Finalmente, demonstrou-se que a fuzzificação não singleton pode ser uma ferramenta útil quando as entradas são ruidosasAbstract: Offshore facilities have mooring lines to provide stability, support and holding to the structures. These mooring lines are commonly made up of synthetic fiber ropes, cables and chains. When the load solicitation is high, the mooring lines must be made up of chain. The monitoring of the strength of these chains is vital for the reliability and security of the production of energy. The current methods for supervising the loads on the chains are expensive and have many uncertainties involved. In this context, it is proposed a new methodology for the force estimation in chains through the measurements of their natural frequencies. The present dissertation arises as an improvement of this approach. A fuzzy inference system optimized by a genetic algorithm is introduced to enhance the estimation of the load on the chains. The inputs of the fuzzy models are the natural frequencies of the chains and the output is the estimated force. The Mamdani and Sugeno methodologies were implemented and compared. Triangular and Gaussian membership functions were used to model the inputs and the output. The rules were set according to the relations between the natural frequencies and the force on the chain. To optimize the system, the genetic algorithm can use the results provided by a mathematical model or by a set of measurements as training data. The mathematical model has good agreement with the experimental data. The fuzzy genetic model was simulated and tested providing good accuracy in estimating the force. In addition, the non-singleton fuzzification demonstrated that can be a helpful tool when the entries are noisyMestradoMecanica de Solidos e Projeto MecanicoMestre em Engenharia Mecânica33003017CAPE

Fuzzy Technology Design for Early Detection of Diseases in Tobacco Plants

Tobacco is an agricultural product that uses leaves to be processed into pesticides, medicines and cigarettes. Tobacco quality is determined by plant maintenance and reduced pest and disease attacks. To avoid these disturbances, control is needed quickly, precisely and accurately so that the tobacco plant disease cannot spread throughout agricultural land. In making fuzzy, diseases and symptoms in tobacco plants are used as a rule base in making a fuzzy expert system. The expert system created in this research is an expert system using the concept of fuzzy logic to diagnose tobacco plant diseases, using the Mamdani inference method and the defuzzification process using the centroid method (firmness value) to get the right conclusions in diagnosing tobacco plant diseases. From the results of Mamdani's design and manual fuzzy calculations, it can be concluded that the design is ready to be further implemented into the required programming language. From the sample calculation results, it was found that damping off disease has a moderate degree of risk with a value of 41.54. With the construction of this system, it will provide easy information for farmers to carry out and find out what symptoms are contracting diseases in tobacco plants

Research of monitoring and measuring equipment using the Fuzzy-Logic system

В роботі розглянуто вирішення науково-практичної задачі контролю точності вимірювання параметрів технологічного процесу виготовлення карамелі для підвищення її якості за допомогою створення евристичного аналізатору на базі інтерфейсу користувача системи Fuzzy-Logic. Проаналізовано фактори, що впливають на точність вимірювання, доведено можливість застосування апарату Fuzzy-Logic для визначення таких параметрів технологічного процесу, які забезпечують максимальну якість продукції. Проведено комп’ютерне моделювання, яке підтвердило, що створення евристичного аналізатору для визначення якості карамелі доцільно та необхідно для того, щоб не допустити виробництво неякісної продукції. На підставі даних, отриманих з результатів натурних вимірювань параметрів технологічного процесу виготовлення карамельного сиропу проведено розрахунки стандартної невизначеності результатів вимірювань по типам А та В, щоб мати можливість своєчасно прогнозувати відмову датчиків на основі зміни форми закону розподілу результатів вимірювань та назначати міжповірочні інтервали для досліджуваного обладнання.The paper considers the solution of the scientific and practical problem of controlling the accuracy of measuring the parameters of the technological process of making caramel to improve its quality by creating a heuristic analyzer based on the interface of the Fuzzy-Logic system. The factors affecting the measurement accuracy are analyzed, the possibility of using the Fuzzy-logic apparatus to determine such process parameters that ensure maximum product quality is proved. Computer simulation was carried out, which confirmed that the creation of a heuristic analyzer to determine the quality of caramel is appropriate and necessary in order to prevent the production of low-quality products. Based on the data obtained from the results of field measurements of the parameters of the technological process of making caramel syrup, the standard uncertainty of the measurement results for types A and B was calculated in order to be able to timely predict sensor failures based on the change in the shape of the distribution law of the measurement results and determine the calibration interval for the equipment under study

Fuzzy logic applied to system control to enhance commercial appliance performance

The purpose of this research is to determine the usefulness of fuzzy logic and fuzzy control when applied to a commercial appliance. Fuzzy logic is a structured, model-free estimator that approximates a function through linguistic input/output associations. Fuzzy rule-based systems apply these methods to solve many types of real-world problems, especially where a system is difficult to model, is controlled by a human operator or expert, or where ambiguity or vagueness is common. This dissertation presents fuzzy sets, fuzzy systems, and fuzzy control, with an example conveying the use of fuzzy control of a consumer product and an overview of fuzzy logic in the field of artificial intelligence. Ultimately, it demonstrates that the use of fuzzy systems makes a viable addition to the field of artificial intelligence and, perhaps, more generally to the application of other consumer products to reduce energy consumption and increase the ease of operation. Topics such as classical logic, set theory, fuzzy set theory, and fuzzy mathematics are developed in this research to provide a foundation in fuzzy logic. Fuzzy logic is an excellent development of a basic home appliance to provide a powerful and user-friendly device. Fuzzy logic allows an engineer without a great knowledge of control systems and mathematical modeling a viable alternative in product creation. The fuzzy logic toolbox of the program MATLAB\sp{\rm TM} developed by The Mathworks Corporation is used to build and test the fuzzy logic systems explored by this dissertation. Again, in this dissertation the concept of fuzzy logic shall be explored in detail. Background and theoretical information shall be derived to provide a good base for applications. Classical logic, crisp sets, fuzzy sets, and operations on fuzzy sets are explained in order to cover a wide spectrum of applications. The focus or cumulating point will be to apply the fuzzy logic principle to any type of consumer appliance (such as a washing machine). The use of fuzzy logic will allow many household goods to be manufactured more quickly and with more options, and be energy efficient, user friendly, and cost effective

On strategic choices faced by large pharmaceutical laboratories and their effect on innovation risk under fuzzy conditions

ObjectivesWe develop a fuzzy evaluation model that provides managers at different responsibility levels in pharmaceutical laboratories with a rich picture of their innovation risk as well as that of competitors. This would help them take better strategic decisions around the management of their present and future portfolio of clinical trials in an uncertain environment. Through three structured fuzzy inference systems (FISs), the model evaluates the overall innovation risk of the laboratories by capturing the financial and pipeline sides of the risk.Methods and materialsThree FISs, based on the Mamdani model, determine the level of innovation risk of large pharmaceutical laboratories according to their strategic choices. Two subsystems measure different aspects of innovation risk while the third one builds on the results of the previous two. In all of them, both the partitions of the variables and the rules of the knowledge base are agreed through an innovative 2-tuple-based method. With the aid of experts, we have embedded knowledge into the FIS and later validated the model.ResultsIn an empirical application of the proposed methodology, we evaluate a sample of 31 large pharmaceutical laboratories in the period 2008–2013. Depending on the relative weight of the two subsystems in the first layer (capturing the financial and the pipeline sides of innovation risk), we estimate the overall risk. Comparisons across laboratories are made and graphical surfaces are analyzed in order to interpret our results. We have also run regressions to better understand the implications of our results.ConclusionsThe main contribution of this work is the development of an innovative fuzzy evaluation model that is useful for analyzing the innovation risk characteristics of large pharmaceutical laboratories given their strategic choices. The methodology is valid for carrying out a systematic analysis of the potential for developing new drugs over time and in a stable manner while managing the risks involved. We provide all the necessary tools and datasets to facilitate the replication of our system, which also may be easily applied to other settings

Design of a wireless intelligent fuzzy controller network

Since the first application of fuzzy logic in the field of control engineering, fuzzy logic control has been successfully employed in controlling a wide variety of applications, such as commercial appliances, industrial automation, robots, traffic control, cement kilns and automotive engineering. The human knowledge on controlling complex and non-linear processes can be incorporated into a controller in the form of linguistic expressions. Despite these achievements, however, there is still a lack of an empirical or analytical design study which adequately addresses a systematic auto-tuning method. Indeed, tuning is one of the most crucial parts in the overall design of fuzzy logic controllers and it has become an active research field. Various techniques have been utilised to develop algorithms to fine-tune the controller parameters from a trial and error method to very advanced optimisation techniques. The structure of fuzzy logic controllers is not straightforward as is the case in PID controllers. In addition, there is also a set of parameters that can be adjusted, and it is not always easy to find the relationship between the parameters and the controller performance measures. Moreover, in general, controllers have a wide range of setpoints; changing from one value to another requiring the controller parameters to be re-tuned in order to maintain a satisfactory performance over the entire range of setpoints. This thesis deals with the design and implementation of a new intelligent algorithm for fuzzy logic controllers in a wireless network structure. The algorithm enables the controllers to learn about their plants and systematically tune their gains. The algorithm also provides the capability of retaining the knowledge acquired during the tuning process. Furthermore, this knowledge is shared on the network through a wireless communication link with other controllers. Based on the relationships between controller gains and the closed-loop characteristics, an auto-tuning algorithm is developed. Simulation experiments using standard second order systems demonstrate the effectiveness of the algorithm with respect to auto-tuning, tracking setpoints and rejecting external disturbances. Furthermore, a zero overshoot response is produced with improvements in the transient and the steady state responses. The wireless network structure is implemented using LabVIEW by composing a network of several fuzzy controllers. The results demonstrate that the controllers are able to retain and share the knowledge

Fuzzy control and its application to a pH process

In the chemical industry, the control of pH is a well-known problem that presents difficulties due to the large variations in its process dynamics and the static nonlinearity between pH and concentration. pH control requires the application of advanced control techniques such as linear or nonlinear adaptive control methods. Unfortunately, adaptive controllers rely on a mathematical model of the process being controlled, the parameters being determined or modified in real time. Because of its characteristics, the pH control process is extremely difficult to model accurately. Fuzzy logic, which is derived from Zadeh's theory of fuzzy sets and algorithms, provides an effective means of capturing the approximate, inexact nature of the physical world. It can be used to convert a linguistic control strategy based on expert knowledge, into an automatic control strategy to control a system in the absence of an exact mathematical model. The work described in this thesis sets out to investigate the suitability of fuzzy techniques for the control of pH within a continuous flow titration process. Initially, a simple fuzzy development system was designed and used to produce an experimental fuzzy control program. A detailed study was then performed on the relationship between fuzzy decision table scaling factors and the control constants of a digital PI controller. Equation derived from this study were then confirmed experimentally using an analogue simulation of a first order plant. As a result of this work a novel method of tuning a fuzzy controller by adjusting its scaling factors, was derived. This technique was then used for the remainder of the work described in this thesis. The findings of the simulation studies were confirmed by an extensive series of experiments using a pH process pilot plant. The performance of the tunable fuzzy controller was compared with that of a conventional PI controller in response to step change in the set-point, at a number of pH levels. The results showed not only that the fuzzy controller could be easily adjusted to provided a wide range of operating characteristics, but also that the fuzzy controller was much better at controlling the highly non-linear pH process, than a conventional digital PI controller. The fuzzy controller achieved a shorter settling time, produced less over-shoot, and was less affected by contamination than the digital PI controller. One of the most important characteristics of the tunable fuzzy controller is its ability to implement a wide variety of control mechanisms simply by modifying one or two control variables. Thus the controller can be made to behave in a manner similar to that of a conventional PI controller, or with different parameter values, can imitate other forms of controller. One such mode of operation uses sliding mode control, with the fuzzy decision table main diagonal being used as the variable structure system (VSS) switching line. A theoretical explanation of this behavior, and its boundary conditions, are given within the text. While the work described within this thesis has concentrated on the use of fuzzy techniques in the control of continuous flow pH plants, the flexibility of the fuzzy control strategy described here, make it of interest in other areas. It is likely to be particularly useful in situations where high degrees of non-linearity make more conventional control methods ineffective