Adaptive Fuzzy PID Based on Granular Function for Proton Exchange Membrane Fuel Cell Oxygen Excess Ratio Control

An effective oxygen excess ratio control strategy for a proton exchange membrane fuel cell (PEMFC) can avoid oxygen starvation and optimize system performance. In this paper, a fuzzy PID control strategy based on granular function (GFPID) was proposed. Meanwhile, a proton exchange membrane fuel cell dynamic model was established on the MATLAB/Simulink platform, including the stack model system and the auxiliary system. In order to avoid oxygen starvation due to the transient variation of load current and optimize the parasitic power of the auxiliary system and the stack voltage, the purpose of optimizing the overall operating condition of the system was finally achieved. Adaptive fuzzy PID (AFPID) control has the technical bottleneck limitation of fuzzy rules explosion. GFPID eliminates fuzzification and defuzzification to solve this phenomenon. The number of fuzzy rules does not affect the precision of GFPID control, which is only related to the fuzzy granular points in the fitted granular response function. The granular function replaces the conventional fuzzy controller to realize the online adjustment of PID parameters. Compared with the conventional PID and AFPID control, the feasibility and superiority of the algorithm based on particle function are verified.</jats:p

Sistemas granulares evolutivos

Orientador: Fernando Antonio Campos GomideTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Recentemente tem-se observado um crescente interesse em abordagens de modelagem computacional para lidar com fluxos de dados do mundo real. Métodos e algoritmos têm sido propostos para obtenção de conhecimento a partir de conjuntos de dados muito grandes e, a princípio, sem valor aparente. Este trabalho apresenta uma plataforma computacional para modelagem granular evolutiva de fluxos de dados incertos. Sistemas granulares evolutivos abrangem uma variedade de abordagens para modelagem on-line inspiradas na forma com que os humanos lidam com a complexidade. Esses sistemas exploram o fluxo de informação em ambiente dinâmico e extrai disso modelos que podem ser linguisticamente entendidos. Particularmente, a granulação da informação é uma técnica natural para dispensar atenção a detalhes desnecessários e enfatizar transparência, interpretabilidade e escalabilidade de sistemas de informação. Dados incertos (granulares) surgem a partir de percepções ou descrições imprecisas do valor de uma variável. De maneira geral, vários fatores podem afetar a escolha da representação dos dados tal que o objeto representativo reflita o significado do conceito que ele está sendo usado para representar. Neste trabalho são considerados dados numéricos, intervalares e fuzzy; e modelos intervalares, fuzzy e neuro-fuzzy. A aprendizagem de sistemas granulares é baseada em algoritmos incrementais que constroem a estrutura do modelo sem conhecimento anterior sobre o processo e adapta os parâmetros do modelo sempre que necessário. Este paradigma de aprendizagem é particularmente importante uma vez que ele evita a reconstrução e o retreinamento do modelo quando o ambiente muda. Exemplos de aplicação em classificação, aproximação de função, predição de séries temporais e controle usando dados sintéticos e reais ilustram a utilidade das abordagens de modelagem granular propostas. O comportamento de fluxos de dados não-estacionários com mudanças graduais e abruptas de regime é também analisado dentro do paradigma de computação granular evolutiva. Realçamos o papel da computação intervalar, fuzzy e neuro-fuzzy em processar dados incertos e prover soluções aproximadas de alta qualidade e sumário de regras de conjuntos de dados de entrada e saída. As abordagens e o paradigma introduzidos constituem uma extensão natural de sistemas inteligentes evolutivos para processamento de dados numéricos a sistemas granulares evolutivos para processamento de dados granularesAbstract: In recent years there has been increasing interest in computational modeling approaches to deal with real-world data streams. Methods and algorithms have been proposed to uncover meaningful knowledge from very large (often unbounded) data sets in principle with no apparent value. This thesis introduces a framework for evolving granular modeling of uncertain data streams. Evolving granular systems comprise an array of online modeling approaches inspired by the way in which humans deal with complexity. These systems explore the information flow in dynamic environments and derive from it models that can be linguistically understood. Particularly, information granulation is a natural technique to dispense unnecessary details and emphasize transparency, interpretability and scalability of information systems. Uncertain (granular) data arise from imprecise perception or description of the value of a variable. Broadly stated, various factors can affect one's choice of data representation such that the representing object conveys the meaning of the concept it is being used to represent. Of particular concern to this work are numerical, interval, and fuzzy types of granular data; and interval, fuzzy, and neurofuzzy modeling frameworks. Learning in evolving granular systems is based on incremental algorithms that build model structure from scratch on a per-sample basis and adapt model parameters whenever necessary. This learning paradigm is meaningful once it avoids redesigning and retraining models all along if the system changes. Application examples in classification, function approximation, time-series prediction and control using real and synthetic data illustrate the usefulness of the granular approaches and framework proposed. The behavior of nonstationary data streams with gradual and abrupt regime shifts is also analyzed in the realm of evolving granular computing. We shed light upon the role of interval, fuzzy, and neurofuzzy computing in processing uncertain data and providing high-quality approximate solutions and rule summary of input-output data sets. The approaches and framework introduced constitute a natural extension of evolving intelligent systems over numeric data streams to evolving granular systems over granular data streamsDoutoradoAutomaçãoDoutor em Engenharia Elétric

RSL ROVER: Robotic Systems Laboratory Rugged Offroad Vehicle for Experimental Research

The goal of this project was to build an autonomous vehicle testbed for the Robotics Systems Laboratory. This testbed will be used by undergraduate, graduate, and faculty researchers to test different control methods, sensor combinations, vehicle control laws, and eventually autonomous navigation. This paper documents our accomplishments to achieve this goal; we built a hierarchical control system, robust actuator mounts, and an effective safety system. The result is a capable 6-wheeled offroad vehicle that can be electronically controlled by remote or directly by wire. A feed-forward control law was incorporated for speed control, yielding predictable performance given a desired speed. Actuators were tuned for fast, reliable response and wiring was kept organized. The team believes the vehicle will be a useful asset to the Robotic Systems Lab for future research. To improve upon our testbed, global positioning and a compass should be integrated along with other sensors that came with the vehicle such as a Lidar unit. With these additional components, the vehicle should be able to run autonomously

Feed-forward control for a drinking water treatment plant chlorination process

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Chlorination in drinking water treatment plants (DWTP) is the final process applied to water before it is sent to storage tanks in the supply network for subsequent human consumption. An excessive dosage of chlorine or, conversely, too small a dosage, may breach existing legal regulations on mandatory limits. In DWTP where there is no significant variability in the quality of the water to be treated, a type of control that is proportional to the flow rate in the effluent can have fully satisfactory results. Therefore, Proportional-Integral (PI) control is a rather frequently used solution. However, when there are inherently long delays in the process, variability in the quality of the water to be treated and considerable variations alternative type is needed. This article presents the strategy and results of a control method that proposes a Fuzzy based feed-forward system to complement an existing PI control. The control system results are shown as applied to the DWTP of Barcelona city, producing satisfactory experimental results.Postprint (author's final draft

Architectural Uncertainty Analysis for Access Control Scenarios in Industry 4.0

Industrie 4.0-Systeme zeichnen sich durch ihre hohe Komplexität, Konnektivität und ihren hohen Datenaustausch aus. Aufgrund dieser Eigenschaften ist es entscheidend, eine Vertraulichkeit der Daten sicher zu stellen. Ein häufig verwendetes Verfahren zum Sicherstellen von Vertraulichkeit ist das Verwenden von Zugriffskontrolle. Basierend auf modellierter Softwarearchitektur, kann eine Zugriffskontrolle bereits während der Entwurfszeit konzeptionell auf das System angewendet werden. Dies ermöglicht es, potentielle Vertraulichkeitsprobleme bereits früh zu identifizieren und bietet die Möglichkeit, die Auswirkungen von Was-wäre-wenn-Szenarien auf die Vertraulichkeit zu analysieren, bevor entsprechende Änderungen umgesetzt werden. Ungewissheiten der Systemumgebung, die sich aus Unklarheiten in den frühen Phasen der Entwicklung oder der abstrakten Sicht des Softwarearchitekturmodells ergeben, können sich jedoch direkt auf bestehende Zugriffskontrollrichtlinien auswirken und zu einer reduzierten Vertraulichkeit führen. Um dies abzuschwächen, ist es wichtig, Ungewissheiten zu identifizieren und zu behandeln. In dieser Arbeit stellen wir unseren Ansatz zum Umgang mit Ungewissheiten der Zugriffskontrolle während der Entwurfszeit vor. Wir erstellen eine Charakterisierung von Ungewissheiten in der Zugriffskontrolle auf der Architekturebene, um ein besseres Verständnis über die existierenden Arten von Ungewissheiten zu erhalten. Darauf basierend definieren wir ein Konzept des Vertrauens in die Gültigkeit von Eigenschaften der Zugriffskontrolle. Dieses Konzept bietet die Möglichkeit mit Ungewissheiten umzugehen, die bereits in Publikationen zu Zugriffskontrollmodellen beschrieben wurden. Das Konzept des Vertrauens ist eine Zusammensetzung von Umgebungsfaktoren, die die Gültigkeit von und folglich das Vertrauen in Zugriffskontrolleigenschaften beeinflussen. Um Umgebungsfaktoren zu kombinieren und so Vertrauenswerte von Zugriffskontrolleigenschaften zu erhalten, nutzen wir Fuzzy-Inferenzsysteme. Diese erhaltenen Vertrauenswerte werden von einem Analyseprozess mit in Betracht gezogen, um Probleme zu identifizieren, die aus einem Mangel an Vertrauen entstehen. Wir erweitern einen bestehenden Ansatz zur Analyse von Informationsfluss und Zugriffskontrolle zur Entwurfszeit, basierend auf Datenflussdiagrammen. Das Wissen, welches wir mit unserem Konzept des Vertrauens hinzufügen, soll Softwarearchitekten die Möglichkeit geben, die Qualität ihrer Modelle zu erhöhen und Anforderungen an die Zugriffskontrolle ihrer Systeme bereits in frühen Phasen der Softwareentwicklung, unter Berücksichtigung von Ungewissheiten zu verifizieren. Die Anwendbarkeit unseres Ansatzes evaluieren wir anhand der Verfügbarkeit der notwendigen Daten in verschiedenen Phasen der Softwareentwicklung, sowie des potenziellen Mehrwerts für bestehende Systeme. Wir messen die Genauigkeit der Analyse beim Identifizieren von Problemen und die Skalierbarkeit hinsichtlich der Ausführungszeit, wenn verschiedene Modellaspekte individuell vergrößert werden

Evolving fuzzy and neuro-fuzzy approaches in clustering, regression, identification, and classification: A Survey

Major assumptions in computational intelligence and machine learning consist of the availability of a historical dataset for model development, and that the resulting model will, to some extent, handle similar instances during its online operation. However, in many real world applications, these assumptions may not hold as the amount of previously available data may be insufficient to represent the underlying system, and the environment and the system may change over time. As the amount of data increases, it is no longer feasible to process data efficiently using iterative algorithms, which typically require multiple passes over the same portions of data. Evolving modeling from data streams has emerged as a framework to address these issues properly by self-adaptation, single-pass learning steps and evolution as well as contraction of model components on demand and on the fly. This survey focuses on evolving fuzzy rule-based models and neuro-fuzzy networks for clustering, classification and regression and system identification in online, real-time environments where learning and model development should be performed incrementally. (C) 2019 Published by Elsevier Inc.Igor Škrjanc, Jose Antonio Iglesias and Araceli Sanchis would like to thank to the Chair of Excellence of Universidad Carlos III de Madrid, and the Bank of Santander Program for their support. Igor Škrjanc is grateful to Slovenian Research Agency with the research program P2-0219, Modeling, simulation and control. Daniel Leite acknowledges the Minas Gerais Foundation for Research and Development (FAPEMIG), process APQ-03384-18. Igor Škrjanc and Edwin Lughofer acknowledges the support by the ”LCM — K2 Center for Symbiotic Mechatronics” within the framework of the Austrian COMET-K2 program. Fernando Gomide is grateful to the Brazilian National Council for Scientific and Technological Development (CNPq) for grant 305906/2014-3

Experimental validation of fuzzy type-2 against type-1 scheme applied in DC/DC converter integrated to a PEM fuel cell system

This research presents and compares the outcomes of experimental implementations of different fuzzy logic control structures for a proton exchange membrane fuel cell (PEMFC). These devices are well known for their capability to transform chemical energy into electrical with low emissions. Commonly, a PEMFC has a linkage with a boost converter which allows a suitable end-user voltage through a nonlinear control law. Hence, the contribution in this sense is the experimental comparison of two fuzzy logic strategies known as type-1 and type-2 that were implemented in a PEMFC system. The approaches were embedded in a control board dSPACE 1102 which also has the capability to acquire data. The contrast of results showed capabilities improvement against disturbances in terms of error reduction, control signal, and robustness.The authors wish to express their gratitude to the Basque Government, through the project EKOHEGAZ (ELKARTEK KK-2021/00092), to the Diputación Foral de Álava (DFA), through the project CONAVANTER, and to the UPV/EHU, through the project GIU20/063, for supporting this work

Review of Intelligent Control Systems with Robotics

Interactive between human and robot assumes a significant job in improving the productivity of the instrument in mechanical technology. Numerous intricate undertakings are cultivated continuously via self-sufficient versatile robots. Current automated control frameworks have upset the creation business, making them very adaptable and simple to utilize. This paper examines current and up and coming sorts of control frameworks and their execution in mechanical technology, and the job of AI in apply autonomy. It additionally expects to reveal insight into the different issues around the control frameworks and the various approaches to fix them. It additionally proposes the basics of apply autonomy control frameworks and various kinds of mechanical technology control frameworks. Each kind of control framework has its upsides and downsides which are talked about in this paper. Another kind of robot control framework that upgrades and difficulties the pursuit stage is man-made brainpower. A portion of the speculations utilized in man-made reasoning, for example, Artificial Intelligence (AI) such as fuzzy logic, neural network and genetic algorithm, are itemized in this paper. At long last, a portion of the joint efforts between mechanical autonomy, people, and innovation were referenced. Human coordinated effort, for example, Kinect signal acknowledgment utilized in games and versatile upper-arm-based robots utilized in the clinical field for individuals with inabilities. Later on, it is normal that the significance of different sensors will build, accordingly expanding the knowledge and activity of the robot in a modern domai