A hybrid PSO-ANFIS approach for short-term wind power prediction in Portugal

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Wind power prediction plays a key role in tackling these challenges. The contribution of this paper is to propose a new hybrid approach, combining particle swarm optimization and adaptive-network-based fuzzy inference system, for short-term wind power prediction in Portugal. Significant improvements regarding forecasting accuracy are attainable using the proposed approach, in comparison with the results obtained with five other approaches.

Learning disability prediction tool using ANN and ANFIS

Learning Disability (LD) is a neurological condition that affects a child’s brain and impairs his ability to carry out one or many specific tasks. LD affects about 15 % of children enrolled in schools. The prediction of LD is a vital and intricate job. The aim of this paper is to design an effective and powerful tool, using the two intelligent methods viz., Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System, for measuring the percentage of LD that affected in school-age children. In this study, we are proposing some soft computing methods in data preprocessing for improving the accuracy of the tool as well as the classifier. The data preprocessing is performed through Principal Component Analysis for attribute reduction and closest fit algorithm is used for imputing missing values. The main idea in developing the LD prediction tool is not only to predict the LD present in children but also to measure its percentage along with its class like low or minor or major. The system is implemented in Mathworks Software MatLab 7.10. The results obtained from this study have illustrated that the designed prediction system or tool is capable of measuring the LD effectively

ANFIS BASED MODELS FOR ACCESSING QUALITY OF WIKIPEDIA ARTICLES

Wikipedia is a free, web-based, collaborative, multilingual encyclopedia project supported by the non-profit Wikimedia Foundation. Due to the free nature of Wikipedia and allowing open access to everyone to edit articles the quality of articles may be affected. As all people don’t have equal level of knowledge and also different people have different opinions about a topic so there may be difference between the contributions made by different authors. To overcome this situation it is very important to classify the articles so that the articles of good quality can be separated from the poor quality articles and should be removed from the database. The aim of this study is to classify the articles of Wikipedia into two classes class 0 (poor quality) and class 1(good quality) using the Adaptive Neuro Fuzzy Inference System (ANFIS) and data mining techniques. Two ANFIS are built using the Fuzzy Logic Toolbox [1] available in Matlab. The first ANFIS is based on the rules obtained from J48 classifier in WEKA while the other one was built by using the expert’s knowledge. The data used for this research work contains 226 article’s records taken from the German version of Wikipedia. The dataset consists of 19 inputs and one output. The data was preprocessed to remove any similar attributes. The input variables are related to the editors, contributors, length of articles and the lifecycle of articles. In the end analysis of different methods implemented in this research is made to analyze the performance of each classification method used.

Controle fuzzy espacialmente diferenciado para um sistema de irrigação

Traditional irrigation projects do not locally determine the water availability in the soil. Then, irregular irrigation cycles may occur: some with insufficient amount that leads to water deficit, other with excessive watering that causes lack of oxygen in plants. Due to the nonlinear nature of this problem and the multivariable context of irrigation processes, fuzzy logic is suggested to replace commercial ON-OFF irrigation system with predefined timing. Other limitation of commercial solutions is that irrigation processes either consider the different watering needs throughout plant growth cycles or the climate changes. In order to fulfill location based agricultural needs, it is indicated to monitor environmental data using wireless sensors connected to an intelligent control system. This is more evident in applications as precision agriculture. This work presents the theoretical and experimental development of a fuzzy system to implement a spatially differentiated control of an irrigation system, based on soil moisture measurement with wireless sensor nodes. The control system architecture is modular: a fuzzy supervisor determines the soil moisture set point of each sensor node area (according to the soil-plant set) and another fuzzy system, embedded in the sensor node, does the local control and actuates in the irrigation system. The fuzzy control system was simulated with SIMULINK® programming tool and was experimentally built embedded in mobile device SunSPOTTM operating in ZigBee. Controller models were designed and evaluated in different combinations of input variables and inference rules base

Anfis applied to the prediction of surface roughness in grinding of advanced ceramics

This paper introduces a methodology for predicting the surface roughness of advanced ceramics using Adaptive Neuro-Fuzzy Inference System (ANFIS). To this end, a grinding machine was used, equipped with an acoustic emission sensor and a power transducer connected to the electric motor rotating the diamond grinding wheel. The alumina workpieces used in this work were pressed and sintered into rectangular bars. Acoustic emission and cutting power signals were collected during the tests and digitally processed to calculate the mean, standard deviation, and two other statistical data. These statistics, as well the root mean square of the acoustic emission and cutting power signals were used as input data for ANFIS. The output values of surface roughness (measured during the tests) were implemented for training and validation of the model. The results indicated that an ANFIS network is an excellent tool when applied to predict the surface roughness of ceramic workpieces in the grinding process.

Lógica ANFIS aplicada na estimação da rugosidade e do desgaste da ferramenta de corte no processo de retificação plana de cerâmicas avançadas

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Estimação da densidade de solos utilizando sistemas de inferência fuzzy

Pós-graduação em Agronomia (Energia na Agricultura) - FCA

Metodologia baseada em sistema fuzzy intervalar do tipo-2 para detecção e identificação de faltas de incipientes em motores de indução

Desde a incorporação da automação no processo produtivo, a busca por sistemas mais eficientes, objetivando o aumento da produtividade e da qualidade dos produtos e serviços, direcionou os estudos para o planejamento de estratégias que permitissem o monitoramento de sistemas com o intuito principal de torna-los mais autônomos e robustos. Por esse motivo, as pesquisas envolvendo o diagnóstico de faltas em sistemas industriais tornaram-se mais intensivas, visto a necessidade da incorporação de técnicas para monitoramente detalhado de sistemas. Tais técnicas permitem a verificação de perturbações, falta ou mesmo falhas. Em vista disso, essa trabalho investiga técnicas de detecção e diagnostico de faltas e sua aplicação em motores de indução trifásicos, delimitando o seu estudo em duas situações: sistemas livre de faltas, e sobre atuação da falta incipiente do tipo curto-circuitoparcial nas espiras do enrolamento do estator. Para a detecção de faltas, utilizou-se analise paramétrica dos parâmetros de um modelo de tempo discreto, de primeira ordem, na estrutura autoregressivo com entradas exógenas (ARX). Os parâmetros do modelo ARX, que trazem informação sobre a dinâmica dominante do sistema, são obtidos recursivamente pela técnica dos mínimos quadrados recursivos (MQR). Para avaliação da falta, foi desenvolvido um sistema de inferência fuzzy (SIF) intervala do tipo-2, cuja mancha de incerteza ou footprint of uncertainty (FOU), características de sistema fuzzy tipo-2, é ideal como forma de representar ruídos inerentes a sistemas reais e erros numéricos provenientes do processo de estimação paramétrica. Os parâmetros do modelo ARX são entradas para o SIF. Algoritmos genéricos (AG’s) foram utilizados para otimização dos SIF intervalares tipo-2, objetivando reduzir o erro de diagnóstico da falta identificada na saída desses sistemas. Os resultados obtidos em teste de simulação computacional demonstram a efetividade da metodologia proposta.

Conceptual framework for the design, implementation and validation of psychomotor skills’ assessment systems in minimally invasive surgery

Las técnicas de cirugía de mínima invasión (CMI) se están consolidando hoy en día como alternativa a la cirugía tradicional, debido a sus numerosos beneficios para los pacientes. Este cambio de paradigma implica que los cirujanos deben aprender una serie de habilidades distintas de aquellas requeridas en cirugía abierta. El entrenamiento y evaluación de estas habilidades se ha convertido en una de las mayores preocupaciones en los programas de formación de cirujanos, debido en gran parte a la presión de una sociedad que exige cirujanos bien preparados y una reducción en el número de errores médicos. Por tanto, se está prestando especial atención a la definición de nuevos programas que permitan el entrenamiento y la evaluación de las habilidades psicomotoras en entornos seguros antes de que los nuevos cirujanos puedan operar sobre pacientes reales. Para tal fin, hospitales y centros de formación están gradualmente incorporando instalaciones de entrenamiento donde los residentes puedan practicar y aprender sin riesgos. Es cada vez más común que estos laboratorios dispongan de simuladores virtuales o simuladores físicos capaces de registrar los movimientos del instrumental de cada residente. Estos simuladores ofrecen una gran variedad de tareas de entrenamiento y evaluación, así como la posibilidad de obtener información objetiva de los ejercicios. Los diferentes estudios de validación llevados a cabo dan muestra de su utilidad; pese a todo, los niveles de evidencia presentados son en muchas ocasiones insuficientes. Lo que es más importante, no existe un consenso claro a la hora de definir qué métricas son más útiles para caracterizar la pericia quirúrgica. El objetivo de esta tesis doctoral es diseñar y validar un marco de trabajo conceptual para la definición y validación de entornos para la evaluación de habilidades en CMI, en base a un modelo en tres fases: pedagógica (tareas y métricas a emplear), tecnológica (tecnologías de adquisición de métricas) y analítica (interpretación de la competencia en base a las métricas). Para tal fin, se describe la implementación práctica de un entorno basado en (1) un sistema de seguimiento de instrumental fundamentado en el análisis del vídeo laparoscópico; y (2) la determinación de la pericia en base a métricas de movimiento del instrumental. Para la fase pedagógica se diseñó e implementó un conjunto de tareas para la evaluación de habilidades psicomotoras básicas, así como una serie de métricas de movimiento. La validación de construcción llevada a cabo sobre ellas mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. Adicionalmente, los resultados obtenidos en la validación de apariencia fueron en general positivos en todos los grupos considerados (noveles, residentes, expertos). Para la fase tecnológica, se introdujo el EVA Tracking System, una solución para el seguimiento del instrumental quirúrgico basado en el análisis del vídeo endoscópico. La precisión del sistema se evaluó a 16,33ppRMS para el seguimiento 2D de la herramienta en la imagen; y a 13mmRMS para el seguimiento espacial de la misma. La validación de construcción con una de las tareas de evaluación mostró buenos resultados para tiempo, camino recorrido, profundidad, velocidad media, aceleración media, economía de área y economía de volumen. La validación concurrente con el TrEndo® Tracking System por su parte presentó valores altos de correlación para 8 de las 9 métricas analizadas. Finalmente, para la fase analítica se comparó el comportamiento de tres clasificadores supervisados a la hora de determinar automáticamente la pericia quirúrgica en base a la información de movimiento del instrumental, basados en aproximaciones lineales (análisis lineal discriminante, LDA), no lineales (máquinas de soporte vectorial, SVM) y difusas (sistemas adaptativos de inferencia neurodifusa, ANFIS). Los resultados muestran que en media SVM presenta un comportamiento ligeramente superior: 78,2% frente a los 71% y 71,7% obtenidos por ANFIS y LDA respectivamente. Sin embargo las diferencias estadísticas medidas entre los tres no fueron demostradas significativas. En general, esta tesis doctoral corrobora las hipótesis de investigación postuladas relativas a la definición de sistemas de evaluación de habilidades para cirugía de mínima invasión, a la utilidad del análisis de vídeo como fuente de información y a la importancia de la información de movimiento de instrumental a la hora de caracterizar la pericia quirúrgica. Basándose en estos cimientos, se han de abrir nuevos campos de investigación que contribuyan a la definición de programas de formación estructurados y objetivos, que puedan garantizar la acreditación de cirujanos sobradamente preparados y promocionen la seguridad del paciente en el quirófano. Abstract Minimally invasive surgery (MIS) techniques have become a standard in many surgical sub-specialties, due to their many benefits for patients. However, this shift in paradigm implies that surgeons must acquire a complete different set of skills than those normally attributed to open surgery. Training and assessment of these skills has become a major concern in surgical learning programmes, especially considering the social demand for better-prepared professionals and for the decrease of medical errors. Therefore, much effort is being put in the definition of structured MIS learning programmes, where practice with real patients in the operating room (OR) can be delayed until the resident can attest for a minimum level of psychomotor competence. To this end, skills’ laboratory settings are being introduced in hospitals and training centres where residents may practice and be assessed on their psychomotor skills. Technological advances in the field of tracking technologies and virtual reality (VR) have enabled the creation of new learning systems such as VR simulators or enhanced box trainers. These systems offer a wide range of tasks, as well as the capability of registering objective data on the trainees’ performance. Validation studies give proof of their usefulness; however, levels of evidence reported are in many cases low. More importantly, there is still no clear consensus on topics such as the optimal metrics that must be used to assess competence, the validity of VR simulation, the portability of tracking technologies into real surgeries (for advanced assessment) or the degree to which the skills measured and obtained in laboratory environments transfer to the OR. The purpose of this PhD is to design and validate a conceptual framework for the definition and validation of MIS assessment environments based on a three-pillared model defining three main stages: pedagogical (tasks and metrics to employ), technological (metric acquisition technologies) and analytical (interpretation of competence based on metrics). To this end, a practical implementation of the framework is presented, focused on (1) a video-based tracking system and (2) the determination of surgical competence based on the laparoscopic instruments’ motionrelated data. The pedagogical stage’s results led to the design and implementation of a set of basic tasks for MIS psychomotor skills’ assessment, as well as the definition of motion analysis parameters (MAPs) to measure performance on said tasks. Validation yielded good construct results for parameters such as time, path length, depth, average speed, average acceleration, economy of area and economy of volume. Additionally, face validation results showed positive acceptance on behalf of the experts, residents and novices. For the technological stage the EVA Tracking System is introduced. EVA provides a solution for tracking laparoscopic instruments from the analysis of the monoscopic video image. Accuracy tests for the system are presented, which yielded an average RMSE of 16.33pp for 2D tracking of the instrument on the image and of 13mm for 3D spatial tracking. A validation experiment was conducted using one of the tasks and the most relevant MAPs. Construct validation showed significant differences for time, path length, depth, average speed, average acceleration, economy of area and economy of volume; especially between novices and residents/experts. More importantly, concurrent validation with the TrEndo® Tracking System presented high correlation values (>0.7) for 8 of the 9 MAPs proposed. Finally, the analytical stage allowed comparing the performance of three different supervised classification strategies in the determination of surgical competence based on motion-related information. The three classifiers were based on linear (linear discriminant analysis, LDA), non-linear (support vector machines, SVM) and fuzzy (adaptive neuro fuzzy inference systems, ANFIS) approaches. Results for SVM show slightly better performance than the other two classifiers: on average, accuracy for LDA, SVM and ANFIS was of 71.7%, 78.2% and 71% respectively. However, when confronted, no statistical significance was found between any of the three. Overall, this PhD corroborates the investigated research hypotheses regarding the definition of MIS assessment systems, the use of endoscopic video analysis as the main source of information and the relevance of motion analysis in the determination of surgical competence. New research fields in the training and assessment of MIS surgeons can be proposed based on these foundations, in order to contribute to the definition of structured and objective learning programmes that guarantee the accreditation of well-prepared professionals and the promotion of patient safety in the OR.

Metodologías de modelado, monitorización y asistencia robótica en neurorrehabilitación funcional de extremidad superior

La Organización Mundial de la Salud (OMS) prevé que para el año 2020, el Daño Cerebral Adquirido (DCA) estará entre las 10 causas más comunes de discapacidad. Estas lesiones, dadas sus consecuencias físicas, sensoriales, cognitivas, emocionales y socioeconómicas, cambian dramáticamente la vida de los pacientes y sus familias. Las nuevas técnicas de intervención precoz y el desarrollo de la medicina intensiva en la atención al DCA han mejorado notablemente la probabilidad de supervivencia. Sin embargo, hoy por hoy, las lesiones cerebrales no tienen ningún tratamiento quirúrgico que tenga por objetivo restablecer la funcionalidad perdida, sino que las terapias rehabilitadoras se dirigen hacia la compensación de los déficits producidos. Uno de los objetivos principales de la neurorrehabilitación es, por tanto, dotar al paciente de la capacidad necesaria para ejecutar las Actividades de Vida Diaria (AVDs) necesarias para desarrollar una vida independiente, siendo fundamentales aquellas en las que la Extremidad Superior (ES) está directamente implicada, dada su gran importancia a la hora de la manipulación de objetos. Con la incorporación de nuevas soluciones tecnológicas al proceso de neurorrehabilitación se pretende alcanzar un nuevo paradigma centrado en ofrecer una práctica personalizada, monitorizada y ubicua con una valoración continua de la eficacia y de la eficiencia de los procedimientos y con capacidad de generar conocimientos que impulsen la ruptura del paradigma de actual. Los nuevos objetivos consistirán en minimizar el impacto de las enfermedades que afectan a la capacidad funcional de las personas, disminuir el tiempo de incapacidad y permitir una gestión más eficiente de los recursos. Estos objetivos clínicos, de gran impacto socio-económico, sólo pueden alcanzarse desde una apuesta decidida en nuevas tecnologías, metodologías y algoritmos capaces de ocasionar la ruptura tecnológica necesaria que permita superar las barreras que hasta el momento han impedido la penetración tecnológica en el campo de la rehabilitación de manera universal. De esta forma, los trabajos y resultados alcanzados en la Tesis son los siguientes: 1. Modelado de AVDs: como paso previo a la incorporación de ayudas tecnológicas al proceso rehabilitador, se hace necesaria una primera fase de modelado y formalización del conocimiento asociado a la ejecución de las actividades que se realizan como parte de la terapia. En particular, las tareas más complejas y a su vez con mayor repercusión terapéutica son las AVDs, cuya formalización permitirá disponer de modelos de movimiento sanos que actuarán de referencia para futuros desarrollos tecnológicos dirigidos a personas con DCA. Siguiendo una metodología basada en diagramas de estados UML se han modelado las AVDs 'servir agua de una jarra' y 'coger un botella' 2. Monitorización ubícua del movimiento de la ES: se ha diseñado, desarrollado y validado un sistema de adquisición de movimiento basado en tecnología inercial que mejora las limitaciones de los dispositivos comerciales actuales (coste muy elevado e incapacidad para trabajar en entornos no controlados); los altos coeficientes de correlación y los bajos niveles de error obtenidos en los corregistros llevados a cabo con el sistema comercial BTS SMART-D demuestran la alta precisión del sistema. También se ha realizado un trabajo de investigación exploratorio de un sistema de captura de movimiento de coste muy reducido basado en visión estereoscópica, habiéndose detectado los puntos clave donde se hace necesario incidir desde un punto de vista tecnológico para su incorporación en un entorno real 3. Resolución del Problema Cinemático Inverso (PCI): se ha diseñado, desarrollado y validado una solución al PCI cuando el manipulador se corresponde con una ES humana estudiándose 2 posibles alternativas, una basada en la utilización de un Perceptrón Multicapa (PMC) y otra basada en sistemas Artificial Neuro-Fuzzy Inference Systems (ANFIS). La validación, llevada a cabo utilizando información relativa a los modelos disponibles de AVDs, indica que una solución basada en un PMC con 3 neuronas en la capa de entrada, una capa oculta también de 3 neuronas y una capa de salida con tantas neuronas como Grados de Libertad (GdLs) tenga el modelo de la ES, proporciona resultados, tanto de precisión como de tiempo de cálculo, que la hacen idónea para trabajar en sistemas con requisitos de tiempo real 4. Control inteligente assisted-as-needed: se ha diseñado, desarrollado y validado un algoritmo de control assisted-as-needed para una ortesis robótica con capacidades de actuación anticipatoria de la que existe un prototipo implementado en la actualidad. Los resultados obtenidos demuestran cómo el sistema es capaz de adaptarse al perfil disfuncional del paciente activando la ayuda en instantes anteriores a la ocurrencia de movimientos incorrectos. Esta estrategia implica un aumento en la participación del paciente y, por tanto, en su actividad muscular, fomentándose los procesos la plasticidad cerebral responsables del reaprendizaje o readaptación motora 5. Simuladores robóticos para planificación: se propone la utilización de un simulador robótico assisted-as-needed como herramienta de planificación de sesiones de rehabilitación personalizadas y con un objetivo clínico marcado en las que interviene una ortesis robotizada. Los resultados obtenidos evidencian como, tras la ejecución de ciertos algoritmos sencillos, es posible seleccionar automáticamente una configuración para el algoritmo de control assisted-as-needed que consigue que la ortesis se adapte a los criterios establecidos desde un punto de vista clínico en función del paciente estudiado. Estos resultados invitan a profundizar en el desarrollo de algoritmos más avanzados de selección de parámetros a partir de baterías de simulaciones Estos trabajos han servido para corroborar las hipótesis de investigación planteadas al inicio de la misma, permitiendo, asimismo, la apertura de nuevas líneas de investigación. Summary The World Health Organization (WHO) predicts that by the year 2020, Acquired Brain Injury (ABI) will be among the ten most common ailments. These injuries dramatically change the life of the patients and their families due to their physical, sensory, cognitive, emotional and socio-economic consequences. New techniques of early intervention and the development of intensive ABI care have noticeably improved the survival rate. However, in spite of these advances, brain injuries still have no surgical or pharmacological treatment to re-establish the lost functions. Neurorehabilitation therapies address this problem by restoring, minimizing or compensating the functional alterations in a person disabled because of a nervous system injury. One of the main objectives of Neurorehabilitation is to provide patients with the capacity to perform specific Activities of the Daily Life (ADL) required for an independent life, especially those in which the Upper Limb (UL) is directly involved due to its great importance in manipulating objects within the patients' environment. The incorporation of new technological aids to the neurorehabilitation process tries to reach a new paradigm focused on offering a personalized, monitored and ubiquitous practise with continuous assessment of both the efficacy and the efficiency of the procedures and with the capacity of generating new knowledge. New targets will be to minimize the impact of the sicknesses affecting the functional capabilitiies of the subjects, to decrease the time of the physical handicap and to allow a more efficient resources handling. These targets, of a great socio-economic impact, can only be achieved by means of new technologies and algorithms able to provoke the technological break needed to beat the barriers that are stopping the universal penetration of the technology in the field of rehabilitation. In this way, this PhD Thesis has achieved the following results: 1. ADL Modeling: as a previous step to the incorporation of technological aids to the neurorehabilitation process, it is necessary a first modelling and formalization phase of the knowledge associated to the execution of the activities that are performed as a part of the therapy. In particular, the most complex and therapeutically relevant tasks are the ADLs, whose formalization will produce healthy motion models to be used as a reference for future technological developments. Following a methodology based on UML state-chart diagrams, the ADLs 'serving water from a jar' and 'picking up a bottle' have been modelled 2. Ubiquitous monitoring of the UL movement: it has been designed, developed and validated a motion acquisition system based on inertial technology that improves the limitations of the current devices (high monetary cost and inability of working within uncontrolled environments); the high correlation coefficients and the low error levels obtained throughout several co-registration sessions with the commercial sys- tem BTS SMART-D show the high precision of the system. Besides an exploration of a very low cost stereoscopic vision-based motion capture system has been carried out and the key points where it is necessary to insist from a technological point of view have been detected 3. Inverse Kinematics (IK) problem solving: a solution to the IK problem has been proposed for a manipulator that corresponds to a human UL. This solution has been faced by means of two different alternatives, one based on a Mulilayer Perceptron (MLP) and another based on Artificial Neuro-Fuzzy Inference Systems (ANFIS). The validation of these solutions, carried out using the information regarding the previously generated motion models, indicate that a MLP-based solution, with an architecture consisting in 3 neurons in the input layer, one hidden layer of 3 neurons and an output layer with as many neurons as the number of Degrees of Freedom (DoFs) that the UL model has, is the one that provides the best results both in terms of precission and in terms of processing time, making in idoneous to be integrated within a system with real time restrictions 4. Assisted-as-needed intelligent control: an assisted-as-needed control algorithm with anticipatory actuation capabilities has been designed, developed and validated for a robotic orthosis of which there is an already implemented prototype. Obtained results demonstrate that the control system is able to adapt to the dysfunctional profile of the patient by triggering the assistance right before an incorrect movement is going to take place. This strategy implies an increase in the participation of the patients and in his or her muscle activity, encouraging the neural plasticity processes in charge of the motor learning 5. Planification with a robotic simulator: in this work a robotic simulator is proposed as a planification tool for personalized rehabilitation sessions under a certain clinical criterium. Obtained results indicate that, after the execution of simple parameter selection algorithms, it is possible to automatically choose a specific configuration that makes the assisted-as-needed control algorithm to adapt both to the clinical criteria and to the patient. These results invite researchers to work in the development of more complex parameter selection algorithms departing from simulation batteries Obtained results have been useful to corroborate the hypotheses set out at the beginning of this PhD Thesis. Besides, they have allowed the creation of new research lines in all the studied application fields.

The Fuzzy-Neuro Classifier for Decision Support

This paper aims at development of procedures and algorithms for application of artificial intelligence tools to acquire process and analyze various types of knowledge. The proposed environment integrates techniques of knowledge and decision process modeling such as neural networks and fuzzy logic-based reasoning methods. The problem of an identification of complex processes with the use of neuro-fuzzy systems is solved. The proposed classifier has been successfully applied for building one decision support systems for solving managerial problem.

PCA-ANFIS based prediction for water Injection effectiveness cycle in oil fields.

By proposing a numerical based method on PCA-ANFIS(Adaptive Neuro-Fuzzy Inference System), this paper is focusing on solving the problem of uncertain cycle of water injection in the oilfield. As the dimension of original data is reduced by PCA, ANFIS can be applied for training and testing the new data proposed by this paper. The correctness of PCA-ANFIS models are verified by the injection statistics data collected from 116 wells inside an oilfield, the average absolute error of testing is 1.80 months. With comparison by non-PCA based models which average error is 4.33 months largely ahead of PCA-ANFIS based models, it shows that the testing accuracy has been greatly enhanced by our approach. With the conclusion of the above testing, the PCA-ANFIS method is robust in predicting the effectiveness cycle of water injection which helps oilfield developers to design the water injection scheme.

REAL TIME FUZZY CONTROLLER FOR QUADROTOR STABILITY CONTROL

In this report, we develop an intelligent adaptive neuro-fuzzy controller by using adaptive neuro fuzzy inference system (ANFIS) techniques. We begin by starting with a standard proportional-derivative (PD) controller and use the PD controller data to train the ANFIS system to develop a fuzzy controller. We then propose and validate a method to implement this control strategy on commercial off-the-shelf (COTS) hardware. An analysis is made into the choice of filters for attitude estimation. These choices are limited by the complexity of the filter and the computing ability and memory constraints of the micro-controller. Simplified Kalman filters are found to be good at estimation of attitude given the above constraints. Using model based design techniques, the models are implemented on an embedded system. This enables the deployment of fuzzy controllers on enthusiast-grade controllers. We evaluate the feasibility of the proposed control strategy in a model-in-the-loop simulation. We then propose a rapid prototyping strategy, allowing us to deploy these control algorithms on a system consisting of a combination of an ARM-based microcontroller and two Arduino-based controllers. We then use a combination of the code generation capabilities within MATLAB/Simulink in combination with multiple open-source projects in order to deploy code to an ARM CortexM4 based controller board. We also evaluate this strategy on an ARM-A8 based board, and a much less powerful Arduino based flight controller. We conclude by proving the feasibility of fuzzy controllers on Commercial-off the shelf (COTS) hardware, we also point out the limitations in the current hardware and make suggestions for hardware that we think would be better suited for memory heavy controllers.

Modelação e controlo não-linear do sistema motor humano

Mestrado Integrado em Engenharia Electrotécnica e de Computadores

Flexible multibody dynamics and intelligent control of a hydraulically driven hybrid redundant robot machine

The assembly and maintenance of the International Thermonuclear Experimental Reactor (ITER) vacuum vessel (VV) is highly challenging since the tasks performed by the robot involve welding, material handling, and machine cutting from inside the VV. The VV is made of stainless steel, which has poor machinability and tends to work harden very rapidly, and all the machining operations need to be carried out from inside of the ITER VV. A general industrial robot cannot be used due to its poor stiffness in the heavy duty machining process, and this will cause many problems, such as poor surface quality, tool damage, low accuracy. Therefore, one of the most suitable options should be a light weight mobile robot which is able to move around inside of the VV and perform different machining tasks by replacing different cutting tools. Reducing the mass of the robot manipulators offers many advantages: reduced material costs, reduced power consumption, the possibility of using smaller actuators, and a higher payload-to-robot weight ratio. Offsetting these advantages, the lighter weight robot is more flexible, which makes it more difficult to control. To achieve good machining surface quality, the tracking of the end effector must be accurate, and an accurate model for a more flexible robot must be constructed. This thesis studies the dynamics and control of a 10 degree-of-freedom (DOF) redundant hybrid robot (4-DOF serial mechanism and 6-DOF 6-UPS hexapod parallel mechanisms) hydraulically driven with flexible rods under the influence of machining forces. Firstly, the flexibility of the bodies is described using the floating frame of reference method (FFRF). A finite element model (FEM) provided the Craig-Bampton (CB) modes needed for the FFRF. A dynamic model of the system of six closed loop mechanisms was assembled using the constrained Lagrange equations and the Lagrange multiplier method. Subsequently, the reaction forces between the parallel and serial parts were used to study the dynamics of the serial robot. A PID control based on position predictions was implemented independently to control the hydraulic cylinders of the robot. Secondly, in machining, to achieve greater end effector trajectory tracking accuracy for surface quality, a robust control of the actuators for the flexible link has to be deduced. This thesis investigates the intelligent control of a hydraulically driven parallel robot part based on the dynamic model and two schemes of intelligent control for a hydraulically driven parallel mechanism based on the dynamic model: (1) a fuzzy-PID self-tuning controller composed of the conventional PID control and with fuzzy logic, and (2) adaptive neuro-fuzzy inference system-PID (ANFIS-PID) self-tuning of the gains of the PID controller, which are implemented independently to control each hydraulic cylinder of the parallel mechanism based on rod length predictions. The serial component of the hybrid robot can be analyzed using the equilibrium of reaction forces at the universal joint connections of the hexa-element. To achieve precise positional control of the end effector for maximum precision machining, the hydraulic cylinder should be controlled to hold the hexa-element. Thirdly, a finite element approach of multibody systems using the Special Euclidean group SE(3) framework is presented for a parallel mechanism with flexible piston rods under the influence of machining forces. The flexibility of the bodies is described using the nonlinear interpolation method with an exponential map. The equations of motion take the form of a differential algebraic equation on a Lie group, which is solved using a Lie group time integration scheme. The method relies on the local description of motions, so that it provides a singularity-free formulation, and no parameterization of the nodal variables needs to be introduced. The flexible slider constraint is formulated using a Lie group and used for modeling a flexible rod sliding inside a cylinder. The dynamic model of the system of six closed loop mechanisms was assembled using Hamilton’s principle and the Lagrange multiplier method. A linearized hydraulic control system based on rod length predictions was implemented independently to control the hydraulic cylinders. Consequently, the results of the simulations demonstrating the behavior of the robot machine are presented for each case study. In conclusion, this thesis studies the dynamic analysis of a special hybrid (serialparallel) robot for the above-mentioned special task involving the ITER and investigates different control algorithms that can significantly improve machining performance. These analyses and results provide valuable insight into the design and control of the parallel robot with flexible rods.