Optimal fuzzy PID control tuned with genetic algorithms

Fuzzy logic controllers (FLC) are intelligent systems, based on heuristic knowledge, that have been largely applied in numerous areas of everyday life. They can be used to describe a linear or nonlinear system and are suitable when a real system is not known or too difficult to find their model. FLC provide a formal methodology for representing, manipulating and implementing a human heuristic knowledge on how to control a system. These controllers can be seen as artificial decision makers that operate in a closed-loop system, in real time. The main aim of this work was to develop a single optimal fuzzy controller, easily adaptable to a wide range of systems – simple to complex, linear to nonlinear – and able to control all these systems. Due to their efficiency in searching and finding optimal solution for high complexity problems, GAs were used to perform the FLC tuning by finding the best parameters to obtain the best responses. The work was performed using the MATLAB/SIMULINK software. This is a very useful tool that provides an easy way to test and analyse the FLC, the PID and the GAs in the same environment. Therefore, it was proposed a Fuzzy PID controller (FL-PID) type namely, the Fuzzy PD+I. For that, the controller was compared with the classical PID controller tuned with, the heuristic Ziegler-Nichols tuning method, the optimal Zhuang-Atherton tuning method and the GA method itself. The IAE, ISE, ITAE and ITSE criteria, used as the GA fitness functions, were applied to compare the controllers performance used in this work. Overall, and for most systems, the FL-PID results tuned with GAs were very satisfactory. Moreover, in some cases the results were substantially better than for the other PID controllers. The best system responses were obtained with the IAE and ITAE criteria used to tune the FL-PID and PID controllers.

A fuzzy multi-criteria decision approach for enhanced auto-tracking of seismic events

The main activity carried out by the geophysicist when interpreting seismic data, in terms of both importance and time spent is tracking (or picking) seismic events. in practice, this activity turns out to be rather challenging, particularly when the targeted event is interrupted by discontinuities such as geological faults or exhibits lateral changes in seismic character. In recent years, several automated schemes, known as auto-trackers, have been developed to assist the interpreter in this tedious and time-consuming task. The automatic tracking tool available in modem interpretation software packages often employs artificial neural networks (ANN's) to identify seismic picks belonging to target events through a pattern recognition process. The ability of ANNs to track horizons across discontinuities largely depends on how reliably data patterns characterise these horizons. While seismic attributes are commonly used to characterise amplitude peaks forming a seismic horizon, some researchers in the field claim that inherent seismic information is lost in the attribute extraction process and advocate instead the use of raw data (amplitude samples). This paper investigates the performance of ANNs using either characterisation methods, and demonstrates how the complementarity of both seismic attributes and raw data can be exploited in conjunction with other geological information in a fuzzy inference system (FIS) to achieve an enhanced auto-tracking performance.

Efficient fuzzy control of a rotary inverted pendulum based on LQR mapping

This paper develops fuzzy methods for control of the rotary inverted pendulum, an underactuated mechanical system. Two control laws are presented, one for swing up and another for the stabilization. The pendulum is swung up from the vertical down stable position to the upward unstable position in a controlled trajectory. The rules for the swing up are heuristically written such that each swing results in greater energy build up. The stabilization is achieved by mapping a stabilizing LQR control law to two fuzzy inference engines, which reduces the computational load compared with using a single fuzzy inference engine. The robustness of the balancing control is tested by attaching a bottle of water at the tip of the pendulum.

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.

Previsão em tempo atual de cheias com uso de sistema especialista difuso

Sistemas de previsão de cheias podem ser adequadamente utilizados quando o alcance é suficiente, em comparação com o tempo necessário para ações preventivas ou corretivas. Além disso, são fundamentalmente importantes a confiabilidade e a precisão das previsões. Previsões de níveis de inundação são sempre aproximações, e intervalos de confiança não são sempre aplicáveis, especialmente com graus de incerteza altos, o que produz intervalos de confiança muito grandes. Estes intervalos são problemáticos, em presença de níveis fluviais muito altos ou muito baixos. Neste estudo, previsões de níveis de cheia são efetuadas, tanto na forma numérica tradicional quanto na forma de categorias, para as quais utiliza-se um sistema especialista baseado em regras e inferências difusas. Metodologias e procedimentos computacionais para aprendizado, simulação e consulta são idealizados, e então desenvolvidos sob forma de um aplicativo (SELF – Sistema Especialista com uso de Lógica “Fuzzy”), com objetivo de pesquisa e operação. As comparações, com base nos aspectos de utilização para a previsão, de sistemas especialistas difusos e modelos empíricos lineares, revelam forte analogia, apesar das diferenças teóricas fundamentais existentes. As metodologias são aplicadas para previsão na bacia do rio Camaquã (15543 km2), para alcances entre 10 e 48 horas. Dificuldades práticas à aplicação são identificadas, resultando em soluções as quais constituem-se em avanços do conhecimento e da técnica. Previsões, tanto na forma numérica quanto categorizada são executadas com sucesso, com uso dos novos recursos. As avaliações e comparações das previsões são feitas utilizandose um novo grupo de estatísticas, derivadas das freqüências simultâneas de ocorrência de valores observados e preditos na mesma categoria, durante a simulação. Os efeitos da variação da densidade da rede são analisados, verificando-se que sistemas de previsão pluvio-hidrométrica em tempo atual são possíveis, mesmo com pequeno número de postos de aquisição de dados de chuva, para previsões sob forma de categorias difusas.

Estrutura ANFIS modificada para identificação e controle de plantas com ampla faixa de operação e não linearidade acentuada

In this work a modification on ANFIS (Adaptive Network Based Fuzzy Inference System) structure is proposed to find a systematic method for nonlinear plants, with large operational range, identification and control, using linear local systems: models and controllers. This method is based on multiple model approach. This way, linear local models are obtained and then those models are combined by the proposed neurofuzzy structure. A metric that allows a satisfactory combination of those models is obtained after the structure training. It results on plant s global identification. A controller is projected for each local model. The global control is obtained by mixing local controllers signals. This is done by the modified ANFIS. The modification on ANFIS architecture allows the two neurofuzzy structures knowledge sharing. So the same metric obtained to combine models can be used to combine controllers. Two cases study are used to validate the new ANFIS structure. The knowledge sharing is evaluated in the second case study. It shows that just one modified ANFIS structure is necessary to combine linear models to identify, a nonlinear plant, and combine linear controllers to control this plant. The proposed method allows the usage of any identification and control techniques for local models and local controllers obtaining. It also reduces the complexity of ANFIS usage for identification and control. This work has prioritized simpler techniques for the identification and control systems to simplify the use of the method

Desenvolvimento de um ambiente para projeto de controladores fuzzy para dispositivos móveis

Fuzzy intelligent systems are present in a variety of equipment ranging from household appliances to Fuzzy intelligent systems are present in a variety of equipment ranging from household appliances to small devices such as digital cameras and cell phones being used primarily for dealing with the uncertainties in the modeling of real systems. However, commercial implementations of Fuzzy systems are not general purpose and do not have portability to different hardware platforms. Thinking about these issues this work presents the implementation of an open source development environment that consists of a desktop system capable of generate Graphically a general purpose Fuzzy controller and export these parameters for an embedded system with a Fuzzy controller written in Java Platform Micro Edition To (J2ME), whose modular design makes it portable to any mobile device that supports J2ME. Thus, the proposed development platform is capable of generating all the parameters of a Fuzzy controller and export it in XML file, and the code responsible for the control logic that is embedded in the mobile device is able to read this file and start the controller. All the parameters of a Fuzzy controller are configurable using the desktop system, since the membership functions and rule base, even the universe of discourse of the linguistic terms of output variables. This system generates Fuzzy controllers for the interpolation model of Takagi-Sugeno. As the validation process and testing of the proposed solution the Fuzzy controller was embedded on the mobile device Sun SPOT ® and used to control a plant-level Quanser®, and to compare the Fuzzy controller generated by the system with other types of controllers was implemented and embedded in sun spot a PID controller to control the same level plant of Quanser®

Sistema para detecção e diagnóstico de falhas em motores elétricos de indução utilizando lógica fuzzy

Induction motors are one of the most important equipment of modern industry. However, in many situations, are subject to inadequate conditions as high temperatures and pressures, load variations and constant vibrations, for example. Such conditions, leaving them more susceptible to failures, either external or internal in nature, unwanted in the industrial process. In this context, predictive maintenance plays an important role, where the detection and diagnosis of faults in a timely manner enables the increase of time of the engine and the possibiity of reducing costs, caused mainly by stopping the production and corrective maintenance the motor itself. In this juncture, this work proposes the design of a system that is able to detect and diagnose faults in induction motors, from the collection of electrical line voltage and current, and also the measurement of engine speed. This information will use as input to a fuzzy inference system based on rules that find and classify a failure from the variation of thess quantities

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Lógica BDI fuzzy

Intendding to understand how the human mind operates, some philosophers and psycologists began to study about rationality. Theories were built from those studies and nowadays that interest have been extended to many other areas such as computing engineering and computing science, but with a minimal distinction at its goal: to understand the mind operational proccess and apply it on agents modelling to become possible the implementation (of softwares or hardwares) with the agent-oriented paradigm where agents are able to deliberate their own plans of actions. In computing science, the sub-area of multiagents systems has progressed using several works concerning artificial intelligence, computational logic, distributed systems, games theory and even philosophy and psycology. This present work hopes to show how it can be get a logical formalisation extention of a rational agents architecture model called BDI (based in a philosophic Bratman s Theory) in which agents are capable to deliberate actions from its beliefs, desires and intentions. The formalisation of this model is called BDI logic and it is a modal logic (in general it is a branching time logic) with three access relations: B, D and I. And here, it will show two possible extentions that tranform BDI logic in a modal-fuzzy logic where the formulae and the access relations can be evaluated by values from the interval [0,1]

Sistema especialista fuzzy para dimensionamento de bombeio mecânico

Despite the emergence of other forms of artificial lift, sucker rod pumping systems remains hegemonic because of its flexibility of operation and lower investment cost compared to other lifting techniques developed. A successful rod pumping sizing necessarily passes through the supply of estimated flow and the controlled wear of pumping equipment used in the mounted configuration. However, the mediation of these elements is particularly challenging, especially for most designers dealing with this work, which still lack the experience needed to get good projects pumping in time. Even with the existence of various computer applications on the market in order to facilitate this task, they must face a grueling process of trial and error until you get the most appropriate combination of equipment for installation in the well. This thesis proposes the creation of an expert system in the design of sucker rod pumping systems. Its mission is to guide a petroleum engineer in the task of selecting a range of equipment appropriate to the context provided by the characteristics of the oil that will be raised to the surface. Features such as the level of gas separation, presence of corrosive elements, possibility of production of sand and waxing are taken into account in selecting the pumping unit, sucker-rod strings and subsurface pump and their operation mode. It is able to approximate the inferente process in the way of human reasoning, which leads to results closer to those obtained by a specialist. For this, their production rules were based on the theory of fuzzy sets, able to model vague concepts typically present in human reasoning. The calculations of operating parameters of the pumping system are made by the API RP 11L method. Based on information input, the system is able to return to the user a set of pumping configurations that meet a given design flow, but without subjecting the selected equipment to an effort beyond that which can bear

Inferência fuzzy para o problema de corte de estoque com sobras aproveitáveis de material

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Risk prediction for weed infestation using classification rules

This paper proposes a fuzzy classification system for the risk of infestation by weeds in agricultural zones considering the variability of weeds. The inputs of the system are features of the infestation extracted from estimated maps by kriging for the weed seed production and weed coverage, and from the competitiveness, inferred from narrow and broad-leaved weeds. Furthermore, a Bayesian network classifier is used to extract rules from data which are compared to the fuzzy rule set obtained on the base of specialist knowledge. Results for the risk inference in a maize crop field are presented and evaluated by the estimated yield loss. © 2009 IEEE.

Traffic flow breakdown prediction using feature reduction through Rough-Neuro fuzzy Networks

The prediction of the traffic behavior could help to make decision about the routing process, as well as enables gains on effectiveness and productivity on the physical distribution. This need motivated the search for technological improvements in the Routing performance in metropolitan areas. The purpose of this paper is to present computational evidences that Artificial Neural Network ANN could be use to predict the traffic behavior in a metropolitan area such So Paulo (around 16 million inhabitants). The proposed methodology involves the application of Rough-Fuzzy Sets to define inference morphology for insertion of the behavior of Dynamic Routing into a structured rule basis, without human expert aid. The dynamics of the traffic parameters are described through membership functions. Rough Sets Theory identifies the attributes that are important, and suggest Fuzzy relations to be inserted on a Rough Neuro Fuzzy Network (RNFN) type Multilayer Perceptron (MLP) and type Radial Basis Function (RBF), in order to get an optimal surface response. To measure the performance of the proposed RNFN, the responses of the unreduced rule basis are compared with the reduced rule one. The results show that by making use of the Feature Reduction through RNFN, it is possible to reduce the need for human expert in the construction of the Fuzzy inference mechanism in such flow process like traffic breakdown. © 2011 IEEE.