A fuzzy rule-based decision support system for Duodopa treatment in Parkinson

A decision support system (DSS) was implemented based on a fuzzy logic inference system (FIS) to provide assistance in dose alteration of Duodopa infusion in patients with advanced Parkinson’s disease, using data from motor state assessments and dosage. Three-tier architecture with an object oriented approach was used. The DSS has a web enabled graphical user interface that presents alerts indicating non optimal dosage and states, new recommendations, namely typical advice with typical dose and statistical measurements. One data set was used for design and tuning of the FIS and another data set was used for evaluating performance compared with actual given dose. Overall goodness-of-fit for the new patients (design data) was 0.65 and for the ongoing patients (evaluation data) 0.98. User evaluation is now ongoing. The system could work as an assistant to clinical staff for Duodopa treatment in advanced Parkinson’s disease.

Design of a simplified neuro-fuzzy system based on CMAC Architectures

This work presents the design of a fuzzy controller with simplified architecture that use an artificial neural network working as the aggregation operator for several active fuzzy rules. The simplified architecture of the fuzzy controller is used to minimize the time processing used in the closed loop system operation, the basic procedures of fuzzification are simplified to maximum while all the inference procedures are computed in a private way. As consequence, this simplified architecture allows a fast and easy configuration of the simplified fuzzy controller. The structuring of the fuzzy rules that define the control actions is previously computed using an artificial neural network based on CMAC Cerebellar Model Articulation Controller. The operational limits are standardized and all the control actions are previously calculated and stored in memory. For applications, results and conclusions several configurations of this fuzzy controller are considered.

Fuzzy control of a water pump for an agricultural plant growth system

At the present time there is a high pressure toward the improvement of all the production processes. Those improvements can be sensed in several directions in particular those that involve energy efficiency. The definition of tight energy efficiency improvement policies is transversal to several operational areas ranging from industry to public services. As can be expected, agricultural processes are not immune to this tendency. This statement takes more severe contours when dealing with indoor productions where it is required to artificially control the climate inside the building or a partial growing zone. Regarding the latter, this paper presents an innovative system that improves energy efficiency of a trees growing platform. This new system requires the control of both a water pump and a gas heating system based on information provided by an array of sensors. In order to do this, a multi-input, multi-output regulator was implemented by means of a Fuzzy logic control strategy. Presented results show that it is possible to simultaneously keep track of the desired growing temperature set-point while maintaining actuators stress within an acceptable range.

An SVM-Fuzzy system design for diabetes risk classification

Design and evaluation of a prototype tracking system

In this paper, we present the design and construction of a prototype target tracking system. The experimental set up consists of three main modules for moving the object, detecting the motion of the object and its tracking. The mechanism for moving the object includes an object and two stepper motors and their driving and control circuitry. The detection of the object’s motion is realized by photo switch array. The tracking mechanism consists of a laser beam and two DC servomotors and their associated circuitry. The control algorithm is a standard fuzzy logic controller. The system is designed to operate in two modes in such a way that the role of target and tracker can be interchanged. Experimental results indicate that the fuzzy controller is capable of controlling the system in both modes.

An assessment system for evaluation of driving competencies

Automobiles have deeply impacted the way in which we travel but they have also contributed to many deaths and injury due to crashes. A number of reasons for these crashes have been pointed out by researchers. Inexperience has been identified as a contributing factor to road crashes. Driver’s driving abilities also play a vital role in judging the road environment and reacting in-time to avoid any possible collision. Therefore driver’s perceptual and motor skills remain the key factors impacting on road safety. Our failure to understand what is really important for learners, in terms of competent driving, is one of the many challenges for building better training programs. Driver training is one of the interventions aimed at decreasing the number of crashes that involve young drivers. Currently, there is a need to develop comprehensive driver evaluation system that benefits from the advances in Driver Assistance Systems. A multidisciplinary approach is necessary to explain how driving abilities evolves with on-road driving experience. To our knowledge, driver assistance systems have never been comprehensively used in a driver training context to assess the safety aspect of driving. The aim and novelty of this thesis is to develop and evaluate an Intelligent Driver Training System (IDTS) as an automated assessment tool that will help drivers and their trainers to comprehensively view complex driving manoeuvres and potentially provide effective feedback by post processing the data recorded during driving. This system is designed to help driver trainers to accurately evaluate driver performance and has the potential to provide valuable feedback to the drivers. Since driving is dependent on fuzzy inputs from the driver (i.e. approximate distance calculation from the other vehicles, approximate assumption of the other vehicle speed), it is necessary that the evaluation system is based on criteria and rules that handles uncertain and fuzzy characteristics of the driving tasks. Therefore, the proposed IDTS utilizes fuzzy set theory for the assessment of driver performance. The proposed research program focuses on integrating the multi-sensory information acquired from the vehicle, driver and environment to assess driving competencies. After information acquisition, the current research focuses on automated segmentation of the selected manoeuvres from the driving scenario. This leads to the creation of a model that determines a “competency” criterion through the driving performance protocol used by driver trainers (i.e. expert knowledge) to assess drivers. This is achieved by comprehensively evaluating and assessing the data stream acquired from multiple in-vehicle sensors using fuzzy rules and classifying the driving manoeuvres (i.e. overtake, lane change, T-crossing and turn) between low and high competency. The fuzzy rules use parameters such as following distance, gaze depth and scan area, distance with respect to lanes and excessive acceleration or braking during the manoeuvres to assess competency. These rules that identify driving competency were initially designed with the help of expert’s knowledge (i.e. driver trainers). In-order to fine tune these rules and the parameters that define these rules, a driving experiment was conducted to identify the empirical differences between novice and experienced drivers. The results from the driving experiment indicated that significant differences existed between novice and experienced driver, in terms of their gaze pattern and duration, speed, stop time at the T-crossing, lane keeping and the time spent in lanes while performing the selected manoeuvres. These differences were used to refine the fuzzy membership functions and rules that govern the assessments of the driving tasks. Next, this research focused on providing an integrated visual assessment interface to both driver trainers and their trainees. By providing a rich set of interactive graphical interfaces, displaying information about the driving tasks, Intelligent Driver Training System (IDTS) visualisation module has the potential to give empirical feedback to its users. Lastly, the validation of the IDTS system’s assessment was conducted by comparing IDTS objective assessments, for the driving experiment, with the subjective assessments of the driver trainers for particular manoeuvres. Results show that not only IDTS was able to match the subjective assessments made by driver trainers during the driving experiment but also identified some additional driving manoeuvres performed in low competency that were not identified by the driver trainers due to increased mental workload of trainers when assessing multiple variables that constitute driving. The validation of IDTS emphasized the need for an automated assessment tool that can segment the manoeuvres from the driving scenario, further investigate the variables within that manoeuvre to determine the manoeuvre’s competency and provide integrated visualisation regarding the manoeuvre to its users (i.e. trainers and trainees). Through analysis and validation it was shown that IDTS is a useful assistance tool for driver trainers to empirically assess and potentially provide feedback regarding the manoeuvres undertaken by the drivers.

Quadcopter see and avoid using a fuzzy controller

Unmanned Aerial Vehicles (UAVs) industry is a fast growing sector. Nowadays, the market offers numerous possibilities for off-the-shelf UAVs such as quadrotors or fixed-wings. Until UAVs demonstrate advance capabilities such as autonomous collision avoidance they will be segregated and restricted to flight in controlled environments. This work presents a visual fuzzy servoing system for obstacle avoidance using UAVs. To accomplish this task we used the visual information from the front camera. Images are processed off-board and the result send to the Fuzzy Logic controller which then send commands to modify the orientation of the aircraft. Results from flight test are presented with a commercial off-the-shelf platform.

Cross-entropy optimization for scaling factors of a fuzzy controller : a see-and-avoid approach for unmanned aerial systems

The Cross-Entropy (CE) is an efficient method for the estimation of rare-event probabilities and combinatorial optimization. This work presents a novel approach of the CE for optimization of a Soft-Computing controller. A Fuzzy controller was designed to command an unmanned aerial system (UAS) for avoiding collision task. The only sensor used to accomplish this task was a forward camera. The CE is used to reach a near-optimal controller by modifying the scaling factors of the controller inputs. The optimization was realized using the ROS-Gazebo simulation system. In order to evaluate the optimization a big amount of tests were carried out with a real quadcopter.

Fuzzy-expert approach for voltage-reactive power dispatch

The voltage stability control problem has become an important concern for utilities transmitting power over long distances. This paper presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage stability of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed fuzzy logic control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and some IEEE standard test systems. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a 24 - node equivalent EHV system of part of Indian southern grid and IEEE New England 39-bus system are presented for illustration purposes. The proposed Fuzzy-Expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups.

Application of UPFC for system security improvement under normal and network contingencies

Electric power systems are exposed to various contingencies. Network contingencies often contribute to over-loading of network branches, unsatisfactory voltages and also leading to problems of stability/voltage collapse. To maintain security of the systems, it is desirable to estimate the effect of contingencies and plan suitable measures to improve system security/stability. This paper presents an approach for selection of unified power flow controller (UPFC) suitable locations considering normal and network contingencies after evaluating the degree of severity of the contingencies. The ranking is evaluated using composite criteria based fuzzy logic for eliminating masking effect. The fuzzy approach, in addition to real power loadings and bus voltage violations, voltage stability indices at the load buses also used as the post-contingent quantities to evaluate the network contingency ranking. The selection of UPFC suitable locations uses the criteria on the basis of improved system security/stability. The proposed approach for selection of UPFC suitable locations has been tested under simulated conditions on a few power systems and the results for a 24-node real-life equivalent EHV power network and 39-node New England (modified) test system are presented for illustration purposes.

Fuzzy Expert Approach with Curtailed Controllers for Improved Reactive Power Dispatch

This paper addresses the problem of curtailing the number of control actions using fuzzy expert approach for voltage/reactive power dispatch. It presents an approach using fuzzy set theory for reactive power control with the purpose of improving the voltage profile of a power system. To minimize the voltage deviations from pre-desired values of all the load buses, using the sensitivities with respect to reactive power control variables form the basis of the proposed Fuzzy Logic Control (FLC). Control variables considered are switchable VAR compensators, On Load Tap Changing (OLTC) transformers and generator excitations. Voltage deviations and controlling variables are translated into fuzzy set notations to formulate the relation between voltage deviations and controlling ability of controlling devices. The developed fuzzy system is tested on a few simulated practical Indian power systems and modified IEEE-30 bus system. The performance of the fuzzy system is compared with conventional optimization technique and results obtained are encouraging. Results obtained for a modified IEEE-30 bus test system and a 205-node equivalent EHV system a part of Indian southern grid are presented for illustration purposes. The proposed fuzzy-expert technique is found suitable for on-line applications in energy control centre as the solution is obtained fast with significant speedups with few number of controllers.

Implementação Eletrônica de Sistemas Fuzzy.

Este trabalho investiga a implementação de sistemas fuzzy com circuitos eletrônicos. Tais sistemas têm demonstrado sua capacidade de resolver diversos tipos de problemas em várias aplicações de engenharia, em especial nas relacionadas com controle de processos. Para processos mais complexos, o raciocínio aproximado da lógica fuzzy fornece uma maneira de compreender o comportamento do sistema, permitindo a interpolação aproximada entre situações observadas de entrada e saída. A implementação de um sistema fuzzy pode ser baseada em hardware, em software ou em ambos. Tipicamente, as implementações em software utilizam ambientes de programação integrados com simulação, de modo a facilitar o trabalho do projetista. As implementações em hardware, tradicionais ou evolutivas, podem ser analógicas ou digitais e viabilizam sistemas de maior desempenho. Este trabalho tem por objetivo pesquisar a implementação eletrônica de sistemas fuzzy, a fim de viabilizar a criação de sistemas reais capazes de realizar o mapeamento de entrada e saída adequado. O foco é a utilização de uma plataforma com uma arquitetura analógico-digital baseada em uma tabela de mapeamento armazenada em uma memória de alta capacidade. Memórias do tipo SD (Secure Digital) foram estudadas e utilizadas na construção do protótipo eletrônico da plataforma. Também foram desenvolvidos estudos sobre a quantização, especificamente sobre a possibilidade de redução do número de bits. Com a implementação realizada é possível desenvolver um sistema fuzzy num ambiente simulado (Matlab), configurar a plataforma e executar o sistema fuzzy diretamente na plataforma eletrônica. Os testes com o protótipo construído comprovaram seu bom funcionamento.

Estudo Comparativo de Controladores Fuzzy Aplicados a um Sistema Solar Fotovoltaico.

Neste trabalho apresenta-se o modelo de um controlador baseado em Lógica Fuzzy para um sistema de energia baseado em fonte renovável solar fotovoltaica (photovoltaic - PV) multi-string em operação isolada, para o aproveitamento da máxima potência desta fonte. O sistema é composto por painéis solares, conversor CC-CC tipo elevador de tensão (boost), armazenamento por banco de baterias, inversor trifásico e carga trifásica variável. O sistema fotovoltaico foi modelado no MATLAB/Simulink de forma a representar a curva característica V-I do módulo PV, e que é baseado nos dados disponíveis em data-sheets de painéis fotovoltaicos comerciais. Outros estudos de natureza elétrica tais como o cálculo dos valores eficazes das correntes no conversor CC-CC, para avaliação das perdas, indispensáveis para o dimensionamento de componentes eletrônicos, foram realizados. O método tradicional Perturb and Observe de rastreamento do ponto de máxima potência (Maximum Power Point Tracking MPPT) de painéis foi testado e comparado com métodos que usam a Lógica Fuzzy. Devido ao seu desempenho, foi adotado o método Fuzzy que realiza o MPPT por inferência do ciclo de trabalho de um modulador por largura de pulso (Pulse Width Modulation - PWM) através da variação da potência pela variação da corrente do painel solar. O modelo Fuzzy adotado neste trabalho foi testado com sucesso. Os resultados mostraram que ele pode ser robusto e atende à aplicação proposta. Segundo alguns testes realizados, este controlador pode realizar o MPPT de um sistema PV na configuração multi-string onde alguns arranjos fotovoltaicos são usados. Inclusive, este controle pode ser facilmente adaptado para realizar o MPPT de outras fontes de energia baseados no mesmo princípio de controle, como é o caso do aerogerador.

Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy-Genético.

Geração e Simplificação da Base de Conhecimento de um Sistema Híbrido Fuzzy- Genético propõe uma metodologia para o desenvolvimento da base de conhecimento de sistemas fuzzy, fundamentada em técnicas de computação evolucionária. Os sistemas fuzzy evoluídos são avaliados segundo dois critérios distintos: desempenho e interpretabilidade. Uma metodologia para a análise de problemas multiobjetivo utilizando a Lógica Fuzzy foi também desenvolvida para esse fim e incorporada ao processo de avaliação dos AGs. Os sistemas fuzzy evoluídos foram avaliados através de simulações computacionais e os resultados obtidos foram comparados com os obtidos por outros métodos em diferentes tipos de aplicações. O uso da metodologia proposta demonstrou que os sistemas fuzzy evoluídos possuem um bom desempenho aliado a uma boa interpretabilidade da sua base de conhecimento, tornando viável a sua utilização no projeto de sistemas reais.