Fuzzy Controller

The paper deals with the controlled dynamic thermal and illumination response of human-built environment in real-time conditions. The aim is to harmonize the thermal and optical behavior of a building with regulated energy flows through the envelope. A control system with fuzzy elements for changeable transparent elements of the building envelope was developed. For this purpose a test chamber with an opening on the south aside was built. Energy flows through the envelope are controlled with changeable geometry of the window, realized with the external roller blind, which is automatically positioned. The fuzzy controller for the corresponding positioning of the roller blind in a given combination of outside conditions as external disturbances is the subject of this paper.

The paper deals with the controlled dynamic thermal and illumination response of human-built environment in real-time conditions. The aim is to harmonize the thermal and optical behavior of a building with regulated energy flows through the envelope. A control system with fuzzy elements for changeable transparent elements of the building envelope was developed. For this purpose a test chamber with an opening on the south aside was built. Energy flows through the envelope are controlled with changeable geometry of the window, realized with the external roller blind, which is automatically positioned. The fuzzy controller for the corresponding positioning of the roller blind in a given combination of outside conditions as external disturbances is the subject of this paper.

In today's fast-moving industrial sectors which include paper, textile, and plastic manufacture the core of production quality is in the precise coordination of multi-drive systems. While PI controllers are the mainstay of the industry, they do have issues in that they struggle with the nonlinearity and dynamics of large-scale windings, which in turn causes instability and product integrity issues. To that end, this paper presents an optimized fuzzygenetic controller (FLC-GA), which we put forward as a better linear speed synchronization solution. We used genetic algorithms in the tuning of fuzzy logic parameters, which also takes out the very time-consuming task of manual calibration, and at the same time sees a great increase in the system's ability to deal with process variability. We put our FLC-GA through its paces in a head-to-head comparison with the classic PI and PI-PSO controllers. What we found was that our proposed controller did very well; we saw zero overshoot, a quick 0.5 s settling time, and the total elimination of tension ripples. Also, we saw from a 13.2% change in system inertia that the FLC-GA did a 65% better job in terms of speed accuracy and stability than what we see from standard PI control. We present the FLC-GA not only as a theoretical improvement but as a very robust, high-performance solution in the very tough field of continuous industrial synchronization.

This paper establishes a mathematically rigorous tracking framework designed to mitigate structural information uncertainty within a Contested Electromagnetic Environment (CEME) by employing the newly formulated Indeterminacy Fuzzy Set (IDFS) theory. Traditional triadic extensions, such as unconstrained neutrosophic sets, lack bounded algebraic closure, which creates formal inconsistencies and prevents a coherent probabilistic interpretation. Conversely, classical intuitionistic fuzzy models treat hesitation as a purely derived, non-primitive index, rendering them representationally inadequate for distinguishing between genuine epistemic ignorance and malicious signal conflict. By elevating indeterminacy to a primitive coordinate restricted by a strict simplex constraint (T + I + F ≤ 1), the proposed framework partitions physical wave-front rotations over uncooled distributed sensor surface networks while isolating electronic countermeasures. An adaptive filtering window is dynamically adjusted via the spatial entropy of primitive indeterminacy to handle severe data degradation. To suppress tracking loop divergence during tactical high-G target maneuvers, a non-linear Angle-of-Arrival (AoA) Unscented Kalman Filter (UKF) overlay is integrated Quantitative evaluations across a scalable 5-node pentagon configuration prove that the algebraically closed IDFS lattice preserves robust track locks up to a 4.5× jamming intensity multiplier, reducing mean-square localization errors by 22.4% compared to conventional linearized paradigms.

The paper presents an optimal fuzzy logic control algorithm for vibration mitigation of buildings using magneto-rheological (MR) dampers. MR dampers are semi-active devices and are monitored using external voltage supply. The voltage monitoring of MR damper is accomplished using evolutionary fuzzy system, where the fuzzy system is optimized using evolutionary algorithms (EAs). A micro-genetic algorithm (m-GA) and a particle swarm optimization (PSO) are used to optimize the FLC parameters. Two cases of optimal FLCs are shown. One where FLC is optimized keeping the rule base predefined and in the other case, FLC rule base is also optimized along with other FLC parameters. The FLC rule base and membership function parameters are optimized using 10 variables. Fuzzy controllers with a predefined rule base and with an optimal rule base are applied to a single degree of freedom (SDOF) and a multidegree of freedom (MDOF) system. Finally, the study evaluates the performance of the fuzzy controller optimized off-line, on a three storey building model under seismic excitations. The main advantage of using FLC to drive the MR damper voltage is that it provides a gradual and smooth change in voltage. Consequently, the present approach provides a better vibration control for structures under earthquake excitations.

Autonomous vehicles stand at the forefront of technological advancement, promising to alter fundamentally how we navigate, with implications for everything from inter city logistics to daily commuting. The expected benefits of this technology include advancements in safety protocols, streamlining of traffic flow, and superior comfort for those onboard. However, the path to widespread integration is paved with signifi cant barriers that need to be overcome. This article provides a thorough review of the historical development of autonomous driving technologies, beginning with the early vehicles powered by internal combustion engines and extending to the present excite ment over Apple’s Project Titan. Rumors suggest this particular project involves the creation of an electric vehicle fully capable of operating without traditional manual controls, including steering wheels and seats. The article concludes by spotlighting the potential for autonomous vehicles to enable disruptive changes in the transport sector, while also considering the myriad of challenges that must be surmounted for their adoption on a grand scale.

This paper develops a novel fuzzy reinforcement learning (RL) based controller for multiagent partially observable Markov decision processes (POMDPs) modeled as a sequence of Bayesian games. Multiagent POMDPs have emerged as a powerful framework for modeling and optimizing multiagent sequential decision making problems under uncertainty, but finding optimal policies is computationally very challenging. Our aim here is twin fold, (i) introduction of a learning paradigm in infinite horizon multiagent POMDPs and (ii) scaling up multiagent POMDP solution approaches by introduction of fuzzy inference systems (FIS) based generalization. We introduce what may be called fuzzy multiagent POMDPs to overcome space and time complexity issues involved in finding optimal policies for multiagent POMDPs. The proposed FIS based RL controller approximates optimal policies for multiagent POMDPs modeled as a sequence of Bayesian games. We empirically evaluate the proposed fuzzy multiagent POMDP controller on the standard benchmark multiagent tiger problem and compare its performance against other state-of-the-art multiagent POMDP solution approaches. Results showcase the effectiveness of the proposed approach and validate the feasibility of employing Bayesian game based RL (in conjunction with FIS approximation) for addressing the intractability of multiagent POMDPs.

Akıllı DA-DA Dönüştürücülerinin özellikle yenilenebilir enerji kaynakları (RES) uygulamalarında artan kullanımı göz önünde bulundurulduğunda, uygun bir kontrol sistemi tasarımı, verim ve güvenilirlik açısından son derece önemlidir. Normal çalışma koşullarında, her DA-DA güç dönüştürücü yapısı, genellikle dönüştürücü çıkışında sabit bir gerilim kontrol bloğuna ve dönüştürücünün yarı iletken güç anahtarı tarafında bir görev döngüsü kontrolü bloğuna sahiptir. Klasik yöntemlerde, geri besleme kontrol döngülerinde orantılı-integral türev (PID) kontrolörü kullanılmaktadır. Bu kontrolör ile ilgili temel sorun, DA-DA dönüştürücüler gibi doğrusal olmayan sistemlerde, davranışlarının yalnızca sistemin çalışma noktasında en uygun olmasıdır. Bulanık mantık denetleyicisi, bu tür sistemlerin büyük sinyal değişimi senaryolarındaki davranışlarını iyileştirmek için kullanılabilir. Ek olarak, normal şartlarda ve dönüştürücüde bir hata olması durumunda, sistemin davranışında önemli bir iyileşme görülmektedir. Bu çalışmada, yükseltici dönüştürücüyü kontrol etmek için bulanık mantık ilkeleri kullanılmıştır. Dönüştürücünün çıkış gerilimi, gürültü, yük değişimleri ve giriş gerilimi değişimleri gibi sistem hatalarına karşı kararlı olmalıdır. Tasarlanan bulanık denetleyicideki yenilik, bu denetleyicinin daha önce tasarlanan kontrolörlerin giriş-çıkış bilgisine ve deneysel olarak zamana bağlı değişkenlere dayanmasıdır. Bulanık mantık denetleyicisini kullanarak, sistem parametreleri bu oynamalara karşı kararlı olması sağlanmaktadır. Performans koşulları MATLAB yazılımı kullanılarak benzetilmiştir. Anahtar Kelimeler: DA-DA dönüştürücüsü, Yenilenebilir enerji uygulamaları, Yüksek kazançlı dönüştürücüler, Yükseltici yapılar.

The design and simulation of a digital fuzzy logic controller applicable to steam room in order to control its steam in artificial stone factory is considered in this paper. For implementing this idea, a new programmable fuzzifier circuit has been designed for a five-bit digital input signal and membership degree as an analog current with five-bit resolution in the range of 58uA. It has also been presented a new high-accurate simple current-mode circuit for Max block.. A controller of two inputs, nine rules, and one output simulated with MATLAB systematically, and the total controller circuit simulated with HSPICE and the Layouts were extracted with Magic.

Intelligent Agents can help users in finding and retrieving goods from electronic marketplaces. Additionally, agents can represent providers in such places facilitating the automatic negotiation about the purchase of products. In this paper, we describe a finite horizon bargaining model between buyers and sellers and we focus on the seller's side. Seller agents are a good example of an autonomous decentralized system. We present a method for the 'bargaining' deadline calculation based on Fuzzy-Logic (FL). Such deadline indicates the time for which it is profitable for a seller to participate in the bargaining procedure. We provide methods for automatic fuzzy rules generation. These rules result the deadline values at each interaction and are based on data provided by experts. We compare results taken from a Fuzzy controller based on such automatic methods with results taken by previous research efforts.

Obstacle detection and avoidance systems as a research area have been gaining attention lately, but the issues of blind areas and ground/small obstacle problems have received little interest, especially indoor autonomous navigation. In this work, we present an environment-sweep technique for obstacle detection and a fuzzy algorithm for ground obstacle avoidance, surface elevation estimation, and obstacle classification for indoor robots that eliminates the blind area problem. We employed an ultrasonic sensor on a servo for environmental sweep to eliminate blind areas. Results show that the issue of interference was mitigated by the servo sweep technique we propose. Fuzzy algorithms combined with a single ultrasonic sensor reduced the complexity of the robot, thereby improving the robot's performance in real-time. Fuzzy algorithms combined with a differential wheel driving mechanism speed up the inference and actuation process of the proposed system. This is due to fewer rules and a less complex yet effective algorithm design that converts steering angles to different wheel speeds, making the response time much less than the stipulated threshold of 2s as has been determined in previous literature.

Fuzzy set, since its advent has played an important role in control systems and many other area of applications. One of such area is the control of autonomous vehicle. There seem to be some difficulty however, for a new timer trying to get a clear picture of the autonomous navigation problem. To this end, this survey presents a panoramic view of the Intelligent Transportation Systems with some few example of the Advance Driver Assistance Systems and a good discussion on the autonomous systems with its eminent problems. More attention was focused on the fuzzy controllers designed for collision avoidance; as its performance has largely simplified and smoothens the collision avoidance process of an autonomous vehicular system.

This research studies the shipboard microgrid (MG) scheme's frequency fluctuations problem contrary to the impulsiveness of renewable resources, load instabilities, and the uncertainty of the parameters in the ship MG plant. A shipboard MG system consists of some of the renewable energy resource s (RESs) such as photovoltaic (PV), wind turbine generator (WTG), battery energy storage system (BESS), ship diesel generator (DG), fuel cell (FC), aqua electrolyzer (AE), and loads. A new fuzzy proportional integral derivative (FPID) controller is established to attain the desired frequency stability for the shipboard MG system. Additionally, various scenarios are executed in this research to validate the robustness of the anticipated controller to various load disturbances, parameter changes of plant, and fluctuations of solar irradiance and wind speed. The numerical simulation results obtained in three scenarios compared with those of the conventional PID controller and the existing time-varying derivative fractional order PID (TVD-FOPID) controller in literatures to validate the high usefulness and applicability of the planned control strategy. In brief, the established load frequency controller (LFC) based on FPID technique can improve frequency deviation in shipboard MG plant effectively.

This paper presents a robust control strategy for DC-DC buck converters operating under sudden input voltage disturbances. A novel quasi-Linear Parameter Varying (LPV) model is constructed by incorporating parasitic elements and expressing system dynamics as functions of the duty cycle to capture real-world dynamics more accurately. Based on this model, a gain-scheduled LPV feedforward compensator is synthesized using affine parameter-dependent Linear Matrix Inequalities (LMIs) to achieve H∞ disturbance attenuation performance. The proposed feedforward compensator adapts continuously to duty cycle variations using only input voltage measurement, thereby simplifying implementation without compromising control performance. This feedforward structure is integrated with a generic feedback controller, enhancing the system's ability to reject abrupt input disturbances effectively. Both simulation and experimental results demonstrate that the proposed architecture provides superior disturbance rejection capabilities and ensures robust output voltage regulation, making it highly applicable to renewable energy systems, electric vehicles, and industrial power converters.

In power systems, frequency constitutes a parameter indicating the equilibrium between power demanded by load and energy produced by generation systems. This chapter studies the effects of varying different system parameters on the overall performance of the traditional frequency regulation system when including contributions of renewable energy sources. A model for the inclusion of variable speed wind turbines in the frequency control loops is analyzed, and parametric sensitivity functions are established using linearized models and transfer function representations for the system components. Through both theoretical analysis and performance simulations, the impact of an inaccurate representation of system inertia in frequency performance is established. Stability analysis for inertia sensitivity of frequency regulation involving wind generation is also provided. Results indicate more robustness to parameter variations for systems including wind turbine participation. However, the frequency deviation rate increases when the uncertainty in system parameters grows. This behavior might lead to instability scenarios under frequency disturbances for the power system.

This paper presents the genetic optimization of the parameters of a fuzzy PI controller for UPFC( Unified Power flow controller) using an improved genetic algorithm to enhance the transient stability performance of power systems. The function based Takagi-Sugeno-Kang (TSK) fuzzy controller uses minimum number of rules(hvo rules) and generates the proportional action which by one-to-two inference mapping gives a variable gain PI controller. This single input function based scheme dispenses the gain dependency of the proportional or integral gains and generates independent control actions. Computer simulation results on a 2-area 4-machine 12-bus power system with an UPFC installed between buses sand r confirm the efficacy of the new approach in damping the local-mode and inter-area mode of oscillations.

An evolutionary approach to fuzzy logic controller design is presented in this paper. We propose the use of a class of genetic algorithms to produce suboptimal fuzzy rule-bases (internally represented as constrained syntactic trees). This model has been applied to the cart centering problem. The obtained results show that a good parameterization of the algorithm and an appropriate evaluation function lead to near-optimal solutions.

Разработано базовое уравнение математической модели элемента внутреннего контура двигателя Стирлинга с учетом изменения массы рабочего тела в элементе, а также термодинамических и теплообменных процессов. Уравнение приведено в дифференциальном виде и в виде соотношений между приращениями параметров на расчетном интервале.
Предложенные соотношения, увязывающие изменения параметров на интервале, получены путем интегрирования линеаризованного базового дифференциального уравнения.
По опыту применения алгоритма расчета рабочего цикла двигателя Стирлинга, основанного на подобной математической модели с линеаризацией параметров на расчетном интервале, но без учета теплообмена в горячей и холодной полостях, предложенный метод позволил без потери точности счета в пределах вычислительной чувствительности выполнять расчеты рабочего цикла с шагом счета порядка 1 град.
Ввиду алгебраической сложности, применение разработанного расчетного соотношения предполагает итерационный алгоритм.
The basic equation of the mathematical model of the element for modeling the working process of the Stirling engine has been developed. The equation includes a change in the mass of the working fluid in the element, as well as thermodynamic and heat transfer processes. The equation is given in differential form, as well as in the form of ratios between increments of parameters in the calculation interval.
The relations linking the changes in the parameters on the interval are obtained by integrating the linearized basic differential equation.
Based on the experience of applying the Stirling engine duty cycle calculation algorithm based on a similar mathematical model with parameters linearization in the calculated interval, but without taking into account heat transfer in hot and cold cavities,
The proposed method made it possible to simulate the working cycle of the Stirling engine with an angle step of about 1 deg.
Due to the algebraic complexity, the use of the developed calculation ratio is possible with an iterative algorithm.

The present chapter deals with the issues related to the evolution of optimal fuzzy logic controllers (FLC) by proper tuning of its knowledge base (KB), using different tools, such as least-square techniques, genetic algorithms, backpropagation (steepest descent) algorithm, ant-colony optimization, reinforcement learning, Tabu search, Taguchi method and simulated annealing. The selection of a particular tool for the evolution of the FLC, generally depends on the application. Some of the applications have also been included in this chapter.

The present chapter deals with the issues related to the evolution of optimal fuzzy logic controllers (FLC) by proper tuning of its knowledge base (KB), using different tools, such as least-square techniques, genetic algorithms, backpropagation (steepest descent) algorithm, ant-colony optimization, reinforcement learning, Tabu search, Taguchi method and simulated annealing. The selection of a particular tool for the evolution of the FLC, generally depends on the application. Some of the applications have also been included in this chapter.

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Power system analysis is all about analysing power system in terms behaviour such as stability, faults, performance under different load type and different circumstances. A phi-representation of transmission line used to model the short, medium and long distances helps us to analyse transmission lines with respect to different variables such as series resistance and inductance, shunt capacitor and conductance. To protect the power system transmission lines we adopt the use of distance relaying which calculate the impedance 𝒁 = 𝑽 𝑰 along the line. The adoption of relaying for power system protection enhanced the grid system by integrating AI in power system which makes the idea of dynamic state estimate (DSE) possible for monitoring and control of power system.

In this paper the concept of hierarchical hybrid fuzzy controllers (HHFCs) is introduced. By means of HHFCs, the development of neuro-fuzzy controllers for complex plants can be simplified, just by dividing the controller into blocks, each of which solves a particular subset of control tasks.

In this paper, a bidirectional boost converter operated in Discontinuous Conduction Mode (DCM) is presented as a suitable power conditioning circuit for tuning of kinetic energy harvesters without the need of a battery. A nonlinear control scheme, composed by two linear controllers, is used to control the average value of the input current, enabling the synthesization of complex loads. The converter, along with the control system, is validated through SPICE simulations using the LTspice tool. The converter model and the controller transfer functions are derived. From the simulation results, it was found that the input current distortion increases with the introduced phase shift and that, such distortion, is almost entirely present at the zero-crossing point of the input voltage. Keywords—Average current control, boost converter, electrical tuning, energy harvesting.

In view of the wind power generation hill-climbing method, the S function modeling is more complex, error prone and difficult to troubleshoot. In the paper, it is established by using Matlab/Simulink basic module. Simulation results show that the hill-climbing model based on Matlab/Simulink can find the maximum power point tracking correctly and realize the MPPT control strategy. The modeling method based on Matlab/Simulink is simple and effective, and independent of the wind power system simulation model. For beginner, it is easy to improve the algorithm, and it's very practical.

The purpose of the work presented in this paper is to present a novel optimisation approach for path planning based on dynamic programming and to integrate it into a proposed navigation strategy. The proposed navigation strategy is a finite-state machine that integrates three navigation aspects. This strategy falls in the category of hybrid architectures. The two aspects other than path planning discussed in this paper are path tracking and obstacle avoidance. Other aspects and behaviours can easily be added. For path tracking, a fuzzy control technique is suggested. The fuzzy controller is used to drive the robot through a set of waypoints leading to the destination. The curvature velocity method is proposed for obstacle avoidance. The testing was conducted on a P3-AT all-terrain mobile robot equipped with encoders, a gyroscope and sonar sensors for localisation and environment perception. The test results validate the theoretical analysis.

We present two secure real-time video conferencing solutions with JPEG coding using a cipher engine based on the Blowfish algorithm. One of these solutions is developed from Java media framework (JMF). In this case, our solution consists of encrypting frames coded with JPEG that are later encapsulated in packets of the RTP protocol (H. Schulzrinne et al., 1996) (real time transport protocol). Our cipher system is based on the Blowfish algorithm (B. Schneier, 1994) of the Cryptix open source cryptographic software library. Another solution is developed from VIC (video conferencing tool) (S. McCanne and V. Jacobson, 1995). In this case, our solution consists of encrypting the UDP packets that contain the JPEG coded frames incorporated in RTP packets. In this paper we analyse the solutions proposed and test the implementations developed in different data networks (LAN/WLAN, frame relay)

Bronchopulmonary dysplasia (BPD) is a form of chronic lung disease that develops in neonates as a consequence of preterm birth, arrested fetal lung development, and inflammation. The incidence of BPD remains on the rise as a result of increasing survival of extremely preterm infants. Severe BPD contributes to significant health care costs and is associated with prolonged hospitalizations, respiratory infections, and neurodevelopmental deficits. In this study, we aimed to detect novel biomarkers of BPD severity. We collected tracheal aspirates (TAs) from preterm babies with mild/moderate (n = 8) and severe (n = 17) BPD, and we profiled the expression of 1048 miRNAs using a PCR array. Associations with biological pathways were determined with the Ingenuity Pathway Analysis (IPA) software. We found 31 miRNAs differentially expressed between the two disease groups (2-fold change, false discovery rate (FDR) < 0.05). Of these, 4 miRNAs displayed significantly higher expression levels, and 27 miRNAs had significantly lower expression levels in the severe BPD group when compared to the mild/moderate BPD group. IPA identified cell signaling and inflammation pathways associated with miRNA signatures. We conclude that TAs of extremely premature infants contain miRNA signatures associated with severe BPD. These may serve as potential biomarkers of disease severity in infants with BPD.

This paper foregrounds fault tolerant robust control of uncertain dynamic linear systems in the state space representation. In fact, the industrial systems are more and more complex and the diagnosis process becomes indispensable to guarantee their surety of functioning and availability, that's why a fault tolerant control law is imperative to achieve the diagnosis. In this paper, we address the problem of state feedback 2 / H H ∞ mixed with regional pole placement for linear continuous uncertain system. Sufficient conditions for feasibility are derived for a general class of convex regions of the complex plan. The conditions are presented as a collection of linear matrix inequalities ( ) ' LMI s . The efficiency and performance of this approach are then tested taking into consideration the robust control of a three-phase induction motor drive with the fluctuation of its parameters during the functioning.

In this paper we describe a perceptual interface which allows to navigate in a virtual D environment by interpreting the movements of the face of human users. The core of the proposed approach consists of a face tracker, which is based on the computation and analysis of integral projections. Applying some predefined heuristics, the location and pose of the face are first estimated and, then, transformed in control signals for the navigation in the virtual world. The system has been implemented using OpenCV and DirectX 9. Some experimental results are shown, proving the feasibility of the proposed technique.

A tuning algorithm of digital controllers to the objects’ models with inertia and time delay using the maximal stability degree method of the continuous control system is proposed in this paper. The object’s model is a model with inertia (second or third order) and time delay. The controller represents a digital algorithm – PI, PID quasicontinuous and it is tuning in two stages. On first stage the PI, PID controllers are tuning in conformity with the maximal stability degree method of continuous control system. On second stage the discretized PI, PID controllers are tuning as the quasicontinuous controllers choosing initial the discretization period in conformity with the constant of inertia and time delay of object’s model.

Soft computing methodologies, when used in combination with sliding-mode control (SMC) systems, aim to alleviate implementation difficulties of SMCs or to intelligently tune the controller parameters. In this paper, it is proposed to combine adaptive fuzzy systems with SMCs to solve the chattering problem of sliding-mode control for robotic applications. In the design of the controller, special attention is paid to chattering elimination without a degradation of the tracking performance. Furthermore, the a priori knowledge required about the system dynamics for design is kept to a minimum. The paper starts with a consideration of basic principles of sliding-mode and fuzzy controllers. Implementation difficulties and most popular solutions are then overviewed. Next, the design of a SMC reported in the literature is outlined and guidelines for the selection of controller parameters for the best tracking performance without chattering are presented. A novel approach based on the introduction of a "chattering variable" is developed. This variable, as a measure of chattering, is used as an input to an adaptive fuzzy system responsible for ringing minimization. On-line tuning of parameters by fuzzy rules is carried out for the SMC and experimental results are presented. Conclusions are presented lastly.

Resumo-0 Gerenciamento Baseado em Polfticas e uma tecnica para coordenar a configura~iio de diversos equipamentos de uma rede, a partir de contratos administrativos (SLAs). Esses contratos indicam polfticas abstratas diffceis de serem interpretadas e implementadas pelos equipamentos de rede, que requerem informa~Oes absolutas. Como a I6gica fuzzy possibilita a representac;ao de valores abstratos, urn controlador fuzzy foi utilizado para implementar urn mecanisme de provisionamento dinfimico, reconfigurando os n6s de acordo como tnifego. Esse trabalho prop6e uma metodologia de constmc;fio de urn controlador para configurar o provisionamento de rede a partir de politicas de gerenciamento visando melhorar a QoS em urn dorninio DiffServ. As funcionalidades sao demonstradas atraves de simulacrao de uma aplicacrfio de Telefonia IP cmzando urn dorninio DiffServ.

This paper presents a new cell state space based Takagi-Sugeno (TS) type Fuzzy Logic Controller (FLC) automatic optimization algorithm. It is a new version of Incremental Best Estimate Directed Search (IBEDS) algorithm. IBEDS starts with an initial training set that may be empty, then an FLC with randomly initialized rule output parameters is trained by Least Mean Square (LMS) learning algorithm in an iterative procedure. In each iteration, the trained FLC is evaluated with cell state space based global and local performance measures, the training set is then updated based on the evaluation under Best Kept Policy, which only keeps the best control commands for each cell center found so far. Originally, in IBEDS, all the FLCs with progressively better performance was discarded. This paper proposes to reuse and reorganize the rule bases of all the FLCs being evaluated to further expedite the search. In the new approach, the training set and the parameter set of a rule base are optimized simultaneously. Simulation results with a 4D inverted pendulum show that the new version of IBEDS is much faster than IBEDS when the initial training set is empty and the search needs to bootstrap itself.

Abstract This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS has two advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for BFS. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed implementation by multi-agent approach. Dynamic agents interact to provide an optimal solution in order to obtain the best BFS reaching the balance interpretability-precision. The performance of the method is tested on a simulated example.